In the framework of the rational design of macromolecules capable of binding to a specific target for biosensing applications, we here further develop an evolutionary protocol designed to optimize the binding affinity of protein binders. In particular we focus on the optimization of the binding portion of small antibody fragments known as nanobodies (or VHH) and choose the hen egg white lysozyme (HEWL) as our target. By implementing a replica exchange scheme for this optimization, we show that an initial hit is not needed and similar solutions can be found by either optimizing an already known anti-HEWL VHH or a randomly selected binder (here a VHH selective towards another macromolecule). While we believe that exhaustive searches of the mutation space are most appropriate when only few key residues have to be optimized, in case a lead binder is not available the proposed evolutionary algorithm should be instead the method of choice.

Protein Structure Prediction (PSP) has achieved significant progress lately. Prediction of inter-residue distances by machine learning and their exploitation during the conformational search is largely among the critical factors behind the progress. Real values than bin probabilities could more naturally represent inter-residue distances, while the latter, via spline curves more naturally helps obtain differentiable objective functions than the former. Consequently, PSP methods that exploit predicted binned distances perform better than those that exploit predicted real-valued distances. To leverage the advantage of bin probabilities in getting differentiable objective functions, in this work, we propose techniques to convert real-valued distances into distance bin probabilities. Using standard benchmark proteins, we then show that our real-to-bin converted distances help PSP methods obtain three-dimensional structures with 4%-16% better root mean squared deviation (RMSD), template modeling score (TM-Score), and global distance test (GDT) values than existing similar PSP methods. Our proposed PSP method is named real to bin (R2B) inter-residue distance predictor, and its code is available from https://gitlab.com/mahnewton/r2b.

β-oxidation of fatty acids plays a significant role in the energy metabolism of the cell. This paper presents a β-oxidation model of fatty acids based on queueing theory. It uses Michaelis-Menten enzyme kinetics, and literature data on metabolites' concentration and enzymatic constants. A genetic algorithm was used to optimize the parameters for the pathway reactions. The model enables real-time tracking of changes in the concentrations of metabolites with different carbon chain lengths. Another application of the presented model is to predict the changes caused by system disturbance, such as altered enzyme activity or abnormal fatty acid concentration. The model has been validated against experimental data. There are diseases that change the metabolism of fatty acids and the presented model can be used to understand the cause of these changes, analyze metabolites abnormalities, and determine the initial target of treatment.

In this paper we propose that high copy number of the mitochondrial genome in neurons is a functional adaptation. We simulated the proliferation of deletion mutants of the human mitochondrial genome in a virtual mitochondrion and recorded the cell loss rates due to deletions overwhelming the wild-type. Our results showed that cell loss increased with mtDNA copy number. Given that neuron loss equates to cognitive dysfunction, it would seem counterintuitive that there would be a selective pressure for high copy number over low. However, for a low copy number, the onset of cognitive decline, while mild, started early in life. Whereas, for high copy number, it did not start until middle age but progressed rapidly. There could have been an advantage to high copy number in the brain if it delayed the onset of cognitive decline until after reproductive age. The prevalence of dementia in our aged population is a consequence of this functional adaptation.

Nearly all mushrooms of the Psilocybe genus contain the natural product psilocybin, which is a psychoactive alkaloid derived from l-tryptophan. Considering their use in ancient times, as well as their psychedelic properties, these mushrooms have re-emerged with psychotherapeutic potential for treating depression, which has triggered increased pharmaceutical interest. However, the psilocybin biosynthesis pathway was only recently defined and, as such, little exists in the way of structural data. Accordingly, the aim of this study was to structurally characterize this pathway by generating homology models for the four Psilocybe cubensis enzymes involved in psilocybin biosynthesis (PsiD, a decarboxylase; PsiH, a monooxygenase; PsiK, a phosphotransferase; PsiM, a methyltransferase). Following initial model generation and alignment with the identified structural templates, repeated refinement of the models was carried out using secondary structure prediction, geometry evaluation, energy minimization, and molecular dynamics simulations in water. The final models were then evaluated using molecular docking interactions with their substrates, i.e., psilocybin precursors (l-tryptophan, tryptamine, 4-hydroxytryptamine, and norbaeocystin/baeocystin), all of which generated feasible binding modes for the expected biotransformation. Further plausibility of the psilocybin → aeruginascin, 4-hydroxytryptamine → norpsilocin, and tryptamine → N,N-dimethyltryptamine conversions, all mediated by the generated model for PsiM, suggests valid routes of formation for these key secondary metabolites. The structural characterization of these enzymes and their binding modes which emerged from this study can lead to a better understanding of psilocybin synthesis, thereby paving the way for the development of novel substrates and selective inhibitors, as well as improved biotechnological manipulation and production of psilocybin in vitro.

Identifying lowly prevalent diseases, or rare diseases, in their early stages is key to disease treatment in the medical field. Deep learning techniques now provide promising tools for this purpose. Nevertheless, the low prevalence of rare diseases entangles the proper application of deep networks for disease identification due to the severe class-imbalance issue. In the past decades, some balancing methods have been studied to handle the data-imbalance issue. The bad news is that it is verified that none of these methods guarantees superior performance to others. This performance variation causes the need to formulate a systematic pipeline with a comprehensive software tool for enhancing deep-learning applications in rare disease identification. We reviewed the existing balancing schemes and summarized a systematic deep ensemble pipeline with a constructed tool called RDDL for handling the data imbalance issue. Through two real case studies, we showed that rare disease identification could be boosted with this systematic RDDL pipeline tool by lessening the data imbalance problem during model training. The RDDL pipeline tool is available at https://github.com/cobisLab/RDDL/.

Research onβ3-AR, the new member of the adrenoceptor family, is in its infancy and few β3-AR agonists have been approved for marketing to date. Meanwhile, β3-AR exhibited obvious species differences in pharmacological properties, such as between human and animals, however, the 3D structure of human β3-AR has not been published, which makes it difficult to understand the interaction between human β3-AR and its agonists. Herein, binding patterns of β3-AR agonists are explored starting from the Alphafold predicted structural model, and the obtained model was optimized by using molecular dynamics simulations. Moreover, the human β3-AR and its agonists were subjected to molecular docking, dynamics simulations, binding free energy calculations and pharmacophore modeling to elucidate the characteristics of human β3-AR activity pockets and agonist conformational relationships, including a hydrophobic group, a positively charged group as well as two hydrogen-bonded donors, which provide comprehensive insights into the interactions between human β3-AR and its agonists.

Proteins in the tumor necrosis factor (TNF) superfamily (TNFSF) regulate diverse cellular processes by interacting with their receptors in the TNF receptor (TNFR) superfamily (TNFRSF). Ligands and receptors in these two superfamilies form a complicated network of interactions, in which the same ligand can bind to different receptors and the same receptor can be shared by different ligands. In order to study these interactions on a systematic level, a TNFSF-TNFRSF interactome was constructed in this study by searching the database which consists of both experimentally measured and computationally predicted protein-protein interactions (PPIs). The interactome contains a total number of 194 interactions between 18 TNFSF ligands and 29 TNFRSF receptors in human. We modeled the structure for each ligand-receptor interaction in the network. Their binding affinities were further computationally estimated based on modeled structures. Our computational outputs, which are all publicly accessible, serve as a valuable addition to the currently limited experimental resources to study TNF-mediated cell signaling.

Ectopic pregnancy (EP) is one of the leading causes of maternal mortality, where the fertilized embryo grows outside of the uterus. Recent experiments on mice have uncovered the importance of genetic factors in the transport of embryos inside the uterus. In the past, efforts have been made to identify possible gene or protein markers in EP in humans through multiple expression studies. Although there exist comprehensive gene resources for other maternal health disorders, there is no specific resource that compiles the genes associated with EP from such expression studies. Here, we address that knowledge gap by creating a computational resource, Ectopic Pregnancy Expression Knowledgebase (EPEK), that involves manual compilation and curation of expression profiles of EP in humans from published articles. In EPEK, we compiled information on 314 differentially expressed genes, 17 metabolites, and 3 SNPs associated with EP. Computational analyses on the gene set from EPEK showed the implication of cellular signaling processes in EP. We also identified possible exosome markers that could be clinically relevant in the diagnosis of EP. In a nutshell, EPEK is the first and only dedicated resource on the expression profile of EP in humans. EPEK is accessible at https://cb.imsc.res.in/epek.

Single-cell RNA sequencing (ScRNA-seq) technology reveals gene expression information at the cellular level. The critical tasks in ScRNA-seq data analysis are clustering and dimensionality reduction. Recent deep clustering algorithms are used to optimize the two tasks jointly, and their variations, graph-based deep clustering algorithms, are used to capture and preserve topological information in the process. However, the existing graph-based deep clustering algorithms ignore the distribution information of nodes when constructing cell graphs which leads to incomplete information in the embedding representation; and graph convolutional networks (GCN), which are most commonly used, often suffer from over-smoothing that leads to high sample similarity in the embedding representation and then poor clustering performance. Here, the dual-GCN-based deep clustering with Triplet contrast (scDGDC) is proposed for dimensionality reduction and clustering of scRNA-seq data. Two critical components are dual-GCN-based encoder for capturing more comprehensive topological information and triplet contrast for reducing GCN over-smoothing. The two components improve the dimensionality reduction and clustering performance of scDGDC in terms of information acquisition and model optimization, respectively. The experiments on eight real ScRNA-seq datasets showed that scDGDC achieves excellent performance for both clustering and dimensionality reduction tasks and is high robustness to parameters.

Multiple studies have established the Pks13-TE domain as a promising target for anti-tuberculosis drug development. However, recent findings have revealed that the lead compound currently in the pipeline for Pks13-TE has significant cardiotoxicity issues. Given the pressing need for new chemical structures for Pks13-TE inhibitors, this study aims to provide a detailed understanding of the Pks13-TE domain binding site through the application of computational chemical biology techniques. Our results highlight the size and shape of the Pks13-TE domain binding pocket, key residues including Asp1644, Asn1640, Phe1670, and Tyr1674 within the pocket, and inhibitor pharmacophore characteristics such as aromatic ring sites, positively charged sites, and hydrogen bond donors. To our knowledge, these simulation results are novel and contribute to the discovery of next-generation Pks13-TE inhibitors without similar prior studies.

B-Cell epitopes (BCEs) can identify and bind with receptor proteins (antigens) to initiate an immune response against pathogens. Understanding antigen-antibody binding interactions has many applications in biotechnology and biomedicine, including designing antibodies, therapeutics, and vaccines. Lab-based experimental identification of these proteins is time-consuming and challenging. Computational techniques have been proposed to discover BCEs, but most lack of significant accomplishments. This work uses classical and deep learning models (DLMs) with sequence-based features to predict immunity stimulator BCEs from proteomics sequences. The proposed convolutional neural network-based model outperforms other models with an accuracy (ACC) of 0.878, an F-measure of 0.871, and an area under the receiver operating characteristic curve (AUC) of 0.945. The proposed strategy achieves 58.7% better results on average than other state-of-the-art approaches based on the Mathews Correlation Coefficient (MCC) results. The established model is accessible through a web application located at http://deeplbcepred.pythonanywhere.com.

The identification of drug-target interaction (DTI) is significant in drug discovery and development, which is usually of high cost in time and money due to large amount of molecule and protein space. The application of deep learning in predicting DTI pairs can overcome these limitations through feature engineering. However, most works do the features extraction using the whole drug and target, which do not take the theoretical basis of pharmacological reaction that the interaction is closely related to some substructure of molecule and protein into consideration, thus poor in performance. On the other hand, some substructure-oriented studies only consider a single type of fragment, e.g., functional group. To address these issues, we propose an end-to-end predicting framework for drug-target interaction named BCM-DTI that takes diverse fragment types into account, including branch chain, common substructure and motif/fragments, and applies a feature learning module based on CNN to learn the synergistic effect between these fragments. We implement BCM-DTI on four public datasets, and the results show that BCM-DTI outperforms state-of-the-art approaches and requires lower training cost.

The correct evaluation of ligand binding free energies by computational methods is still a very challenging active area of research. The most employed methods for these calculations can be roughly classified into four groups: (i) the fastest and less accurate methods, such as molecular docking, designed to sample a large number of molecules and rapidly rank them according to the potential binding energy; (ii) the second class of methods use a thermodynamic ensemble, typically generated by molecular dynamics, to analyze the endpoints of the thermodynamic cycle for binding and extract differences, in the so-called 'end-point' methods; (iii) the third class of methods is based on the Zwanzig relationship and computes the free energy difference after a chemical change of the system (alchemical methods); and (iv) methods based on biased simulations, such as metadynamics, for example. These methods require increased computational power and as expected, result in increased accuracy for the determination of the strength of binding. Here, we describe an intermediate approach, based on the Monte Carlo Recursion (MCR) method first developed by Harold Scheraga. In this method, the system is sampled at increasing effective temperatures, and the free energy of the system is assessed from a series of terms W(b,T), computed from Monte Carlo (MC) averages at each iteration. We show the application of the MCR for ligand binding with datasets of guest-hosts systems (N = 75) and we observed that a good correlation is obtained between experimental data and the binding energies computed with MCR. We also compared the experimental data with an end-point calculation from equilibrium Monte Carlo calculations that allowed us to conclude that the lower-energy (lower-temperature) terms in the calculation are the most relevant to the estimation of the binding energies, resulting in similar correlations between MCR and MC data and the experimental values. On the other hand, the MCR method provides a reasonable view of the binding energy funnel, with possible connections with the ligand binding kinetics, as well. The codes developed for this analysis are publicly available on GitHub as a part of the LiBELa/MCLiBELa project (https://github.com/alessandronascimento/LiBELa).

A new mixed-ligand Ag(I) complex, [Ag(daf)(phen)]NO3 (daf = 4,5-diazafluoren-9-one and dian = N-(4,5-diazafluoren-9-ylidene)aniline), was synthesized. The elemental analysis, FTIR, 1HNMR, UV-Vis spectroscopy, cyclic voltammetry, and DFT (Density Functional Theory) geometry optimization method were applied in order to predict the Ag(I) complex structure which concluded to a distorted tetrahedral N4 coordination around the Ag(I) center. A detailed in silico analysis of the bioaffinity of the complex to DNA and human DNA-Topoisomerase I was conducted using molecular docking simulations and ONIOM (Our own N-layered Integrated molecular Orbital and molecular Mechanics) techniques. In this overall scenario, the results suggest the dominance of π-π stacking interactions of the heteroaromatic ligands in the intercalating pocket of DNA and the active site of the enzyme and the rational correlation between being a good intercalator and a potent Topoisomerase I inhibitor. In vitro DNA-binding experiments by spectrophotometric, spectrofluorometric, Voltammetric, and viscometric techniques at physiological pH also confirmed the computational results. The complex inhibited MCF-7 cell growth in a dose-dependent manner while being nontoxic on HUVEC normal cells.

Predicting the transcription factor binding site (TFBS) in the whole genome range is essential in exploring the rule of gene transcription control. Although many deep learning methods to predict TFBS have been proposed, predicting TFBS using single-cell ATAC-seq data and embedding attention mechanisms needs to be improved. To this end, we present IscPAM, an interpretable method based on deep learning with an attention mechanism to predict single-cell transcription factors. Our model adopts the convolution neural network to extract the data feature and optimize the pre-trained model. In particular, the model obtains faster training and prediction due to the embedded attention mechanism. For datasets, we take ATAC-seq, ChIP-seq, and DNA sequences data for the pre-trained model, and single-cell ATAC-seq data is used to predict the TF binding graph in the given cell. We verify the interpretability of the model through ablation experiments and sensitivity analysis. IscPAM can efficiently predict the combination of whole genome transcription factors in single cells and study cellular heterogeneity through chromatin accessibility of related diseases.

The rate of cesarean section has increased significantly worldwide, creating a group of women with one lower segment cesarean section concerned about the mode of delivery in their future pregnancies. This group of mothers will face a complex discussion because the likelihood for a successful vaginal birth after cesarean section provided to them is a general one. The probability of having a successful vaginal birth is the cornerstone factor of the mothers' decision. Therefore, providing a case-specific likelihood that respects the characteristics of each pregnancy will refine counseling, lower the decision conflict, and improve the success rate of vaginal birth trials eventually improving maternal and fetal outcomes. This paper aims to develop a clinical decision support system to evaluate the individualized likelihood mode of delivery for pregnant women with a previous lower segment cesarean section based on their unique characteristics. The study included six hundred fifty-nine pregnant women, where three hundred twenty-seven records had missing values. Various pre-processing steps, including missing data imputation and feature selection, were applied to the original dataset before model development to improve the data quality. Missing values were handled first, then a feature selection process using a genetic algorithm was applied to select the relevant features and to exclude features that may have been affected negatively by missing data imputation. After that, four machine learning classifiers, namely Decision Tree, Random Forest, K-Nearest Neighbors (KNN), and Logistic Regression, were used to build the prediction model. The results showed that imputing missing values followed by feature selection was more efficient than deleting them since the Area Under the Curve (AUC) has increased from 0.655 to 0.812 using the KNN classifier.

Colon cancer is a common cancer type in both sexes and its mortality rate increases at the metastatic stage. Most studies exclude nondifferentially expressed genes from biomarker analysis of metastatic colon cancers. The motivation of this study is to find the latent associations of the nondifferentially expressed genes with metastatic colon cancers and to evaluate the gender specificity of such associations. This study formulates the expression level prediction of a gene as a regression model trained for primary colon cancers. The difference between a gene's predicted and original expression levels in a testing sample is defined as its mqTrans value (model-based quantitative measure of transcription regulation), which quantitatively measures the change of the gene's transcription regulation in this testing sample. We use the mqTrans analysis to detect the messenger RNA (mRNA) genes with nondifferential expression on their original expression levels but differentially expressed mqTrans values between primary and metastatic colon cancers. These genes are referred to as dark biomarkers of metastatic colon cancer. All dark biomarker genes were verified by two transcriptome profiling technologies, RNA-seq and microarray. The mqTrans analysis of a mixed cohort of both sexes could not recover gender-specific dark biomarkers. Most dark biomarkers overlap with long non-coding RNAs (lncRNAs), and these lncRNAs might have contributed their transcripts to calculating the dark biomarkers' expression levels. Therefore, mqTrans analysis serves as a complementary approach to identify dark biomarkers generally ignored by conventional studies, and it is essential to separate the female and male samples into two analysis experiments. The dataset and mqTrans analysis code are available at https://figshare.com/articles/dataset/22250536.

The ultimate goal of this study is to analyze the gene regulation between FAM111B and p53 in lung adenocarcinoma using Boolean networks. Recent studies have shown that downregulation of FAM111B enhances the G2/M cell cycle checkpoint in the respective cell lines. Upregulation of p53 directly downregulates FAM111B, which is directed to affect cell cycle controllers Cdc25C and Cdk1/CyclinB, thereby controlling G2/M cell cycle arrest. As for apoptosis, down-regulation of FAM111B by p53 directly regulates the BAG3/Bcl-2 axis, which triggers apoptotic cell death. However, the molecular mechanisms involving p53 and FAM111B in G2/M checkpoint regulation are still unknown. Thus, we present a Boolean model of the G2/M checkpoint considering the effect of p53 and FAM111B. Our model indicates that the cell fate between the two cellular phenotypes, arrest, and apoptosis, at the G2/M checkpoint is non-deterministic and is controlled by p53. The model was compared with the experimental data involving gain- or loss-of-function genes and achieved a fair agreement. The model predicts a positive circuit involving p53/FAM111B/BAG3. Our circuit perturbation analysis suggests that this circuit may be essential for controlling cell-fate decisions at the G2/M checkpoint. Our model supports that FAM111B is an engaging target for drug development in lung adenocarcinoma.

RNA (ribonucleic acid) structure prediction finds many applications in health science and drug discovery due to its importance in several life regulatory processes. But despite significant advances in the close field of protein prediction, RNA 3D structure still poses a tremendous challenge to predict, especially for large sequences. In this regard, the approach unfolded by Rosetta FARFAR2 (Fragment Assembly of RNA with Full-Atom Refinement, version 2) has shown promising results, but the algorithm is non-deterministic by nature. In this paper, we develop P-FARFAR2: a parallel enhancement of FARFAR2 that increases its ability to assemble low-energy structures via multithreaded exploration of random configurations in a greedy manner. This strategy, appearing in the literature under the term "parallel mechanism", is made viable through two measures: first, the synchronization window is coarsened to several Monte Carlo cycles; second, all but one of the threads are differentiated as auxiliary and set to perform a weakened version of the problem. Following empirical analysis on a diverse range of RNA structures, we report achieving statistical significance in lowering the energy levels of ensuing samples. And consequently, despite the moderate-to-weak correlation between energy levels and prediction accuracy, this achievement happens to propagate to accuracy measurements.

Breast cancer is characterized as being a heterogeneous pathology with a broad phenotype variability. Breast cancer subtypes have been developed in order to capture some of this heterogeneity. Each of these breast cancer subtypes, in turns retains varied characteristic features impacting diagnostic, prognostic and therapeutics. Basal breast tumors, in particular have been challenging in these regards. Basal breast cancer is often more aggressive, of rapid evolution and no tailor-made targeted therapies are available yet to treat it. Arguably, epigenetic variability is behind some of these intricacies. It is possible to further classify basal breast tumor in groups based on their non-coding transcriptome and methylome profiles. It is expected that these groups will have differences in survival as well as in sensitivity to certain classes of drugs. With this in mind, we implemented a computational learning approach to infer different subpopulations of basal breast cancer (from TCGA multi-omic data) based on their epigenetic signatures. Such epigenomic signatures were associated with different survival profiles; we then identified their associated gene co-expression network structure, extracted a signature based on modules within these networks, and use these signatures to find and prioritize drugs (in the LINCS dataset) that may be used to target these types of cancer. In this way we are introducing the analytical workflow for an epigenomic signature-based drug repurposing structure.

Nef is a small accessory protein pivotal in the HIV-1 viral replication cycle. It is a multifunctional protein and its interactions with kinases in host cells have been well characterized through many in vitro and structural studies. Nef forms a homodimer to activate the kinases and subsequently the phosphorylation pathways. The disruption of its homodimerization represents a valuable approach in the search for novel classes of antiretroviral. However, this research avenue is still underdeveloped as just a few Nef inhibitors have been reported so far, with very limited structural information about their mechanism of action. To address this issue, we have employed an in silico structure-based drug design strategy that combines de novo ligand design with molecular docking and extensive molecular dynamics simulations. Since the Nef pocket involved in homodimerization has high lipophilicity, the initial de novo-designed structures displayed poor drug-likeness and solubility. Taking information from the hydration sites within the homodimerization pocket, structural modifications in the initial lead compound have been introduced to improve the solubility and drug-likeness, without affecting the binding profile. We propose lead compounds that can be the starting point for further optimizations to deliver long-awaited, rationally designed Nef inhibitors.

Super-enhancers are large domains on the genome where multiple short typical enhancers within a specific genomic distance are stitched together. Typically, they are cell type-specific and responsible for defining cell identity and regulating gene transcription. Numerous studies have demonstrated that super-enhancers are enriched for trait-associated variants, and mutations in super-enhancers are possibly related to known diseases. Recently, several machine learning-based methods have been used to distinguish super-enhancers from typical enhancers by using high-throughput data from various experimental methods. The acquisition of such experimental data is usually costly and time-consuming. In this paper, we innovatively proposed SENet, a groundbreaking method based on a deep neural network model, for discriminating between the two categories solely utilizing sequence information. SENet employs dna2vec feature embedding, convolution for local feature extraction, attention pooling for refined feature retention, and Transformer for contextual information extraction. Experiments demonstrate that SENet outperforms all current state-of-the-art computational methods and shows satisfactory performance in cross-species validation. Our method pioneers the distinction between super-enhancers and typical ones using only sequence information. The source code and datasets are stored in https://github.com/lhy0322/SENet.

Microbes in the human body are closely linked to many complex human diseases and are emerging as new drug targets. These microbes play a crucial role in drug development and disease treatment. Traditional methods of biological experiments are not only time-consuming but also costly. Using computational methods to predict microbe-drug associations can effectively complement biological experiments. In this experiment, we constructed heterogeneity networks for drugs, microbes, and diseases using multiple biomedical data sources. Then, we developed a model with matrix factorization and a three-layer heterogeneous network (MFTLHNMDA) to predict potential drug-microbe associations. The probability of microbe-drug association was obtained by a global network-based update algorithm. Finally, the performance of MFTLHNMDA was evaluated in the framework of leave-one-out cross-validation (LOOCV) and 5-fold cross-validation (5-fold CV). The results showed that our model performed better than six state-of-the-art methods that had AUC of 0.9396 and 0.9385 + /- 0.0000, respectively. This case study further confirms the effectiveness of MFTLHNMDA in identifying potential drug-microbe associations and new drug-microbe associations.

Computational designing of four different series (D-G) of thiazolidinone was done starting from different amines which was further condensed with various aldehydes. These underwent in silico molecular investigations for density functional theory (DFT), molecular docking, and absorption, distribution metabolism, excretion, and toxicity (ADMET) studies. The different electrochemical parameters of the compounds are predicted using quantum mechanical modeling approach with Gaussian. The docking software was used to dock the compounds against choosing PDB file for chickenpox, human immunodeficiency, hepatitis, and monkeypox virus as 1OSN, 1VZV, 6VLK, 1RTD, 3I7H, 3TYV, 4JU3, and 4QWO, respectively. The molecular interactions were visualized with discovery studio and maximum binding affinity was observed with D8 compounds against 4QWO (-13.383 kcal/mol) while for compound D5 against 1VZV which was -12.713 kcal/mol. Swiss ADME web tool was used to assess the drug-likeness of the designed compounds under consideration, and it is concluded that these molecules had a drug-like structure with almost zero violations.

Single-cell RNA sequencing technology provides a tremendous opportunity for studying disease mechanisms at the single-cell level. Cell type identification is a key step in the research of disease mechanisms. Many clustering algorithms have been proposed to identify cell types. Most clustering algorithms perform similarity calculation before cell clustering. Because clustering and similarity calculation are independent, a low-rank matrix obtained only by similarity calculation may be unable to fully reveal the patterns in single-cell data. In this study, to capture accurate single-cell clustering information, we propose a novel method based on a low-rank representation model, called KGLRR, that combines the low-rank representation approach with K-means clustering. The cluster centroid is updated as the cell dimension decreases to better from new clusters and improve the quality of clustering information. In addition, the low-rank representation model ignores local geometric information, so the graph regularization constraint is introduced. KGLRR is tested on both simulated and real single-cell datasets to validate the effectiveness of the new method. The experimental results show that KGLRR is more robust and accurate in cell type identification than other advanced algorithms.

As a critical indicator of how easily the human immune system recognizes tumour cells, tumour mutational burden (TMB) is widely used to identify the potential effectiveness of immune checkpoint inhibitor therapy. However, the difficulties associated with the whole exome sequencing (WES) process, such as high tissue sampling requirements, high costs, and long turnaround times, have hindered the widespread clinical use of WES. Furthermore, the mutation landscape varies across cancer types, and the distribution of TMBs varies across cancer subtypes. Therefore, there is an urgent clinical need to develop a small cancer-specific panel to estimate TMB accurately, predict immunotherapy response cost-effectively and assist physicians in precise decision-making. This paper uses a graph neural network framework (Graph-ETMB) to address the cancer specificity problem in TMB. The correlation and tractability between mutated genes are described through message-passing and aggregation algorithms between graph networks. Then the graph neural network is trained in the lung adenocarcinoma data through a semi-supervised approach, resulting in a mutation panel containing 20 genes with a length of only 0.16 Mb. The number of genes to be detected is smaller than most commercial panels currently in clinical use. In addition, the efficacy of the designed panel in predicting immunotherapy response was further determined in an independent validation dataset, exploring the association between TMB and immunotherapy efficacy.

FAM3 is a superfamily of four cytokines that maintain a single globular structure β -β -α of three classes: FAM3A, B, C and D. FAM3C was the first member of this family related to cancer and is functionally characterized as an essential factor for the epithelial-mesenchymal transition (EMT), leading to late delays in tumor progression. Due to its crucial role in EMT and metastasis, FAM3C has been termed an interleukin-like EMT (ILEI) inducer. There are several studies on the part of FAM3C in the progression of cancer and other diseases. However, little is known about its cellular receptors and possible inhibitors. In this study, based on in silico approaches, we performed structural analyses of factors related to FAM3C/ILEI dimerization. We also identified four possible inhibitor candidates, expected to be exciting prototypes and could be submitted to future biological tests targeting cancer treatment.

Despite the rising number of cases and increasing global disease burden, there is no definitive therapy against dengue to date, which necessitates the urgent discovery of inhibitors against the virus. The NS2B-NS3 serine protease of the dengue virus (DENV) catalyses polyprotein cleavage and is a potential target for drug discovery. The protease possesses a potentially druggable allosteric site, and the binding of inhibitors to this site locks the protease in an inactive conformation. The allosteric site is a potential druggable target for drug discovery against flaviviruses. This study aimed to identify serotype-specific hits against the allosteric site in the NS2B-NS3 protease of DENV serotype 2 (DENV2) from the Enamine, Selleck, and ChemDiv antiviral libraries. The prepared libraries were screened using a redocking and rescoring-based strategy with Glide SP and Glide XP, and the hitlist was initially screened by comparing their docking scores with that of reported allosteric inhibitors, myricetin and curcumin. The hitlist was subsequently screened by comparing the molecular mechanics with generalised Born and surface area solvation (MM-GBSA) energy with that of the standards. Ten hits were finally selected by virtual screening, and the stability of the hit-receptor complexes was determined with 100 ns molecular dynamics (MD) simulations in an explicit solvent. Trajectory visualisation and analyses of the RMSD and RMSF values revealed that three hits, including two catechins, remained stably bound to the allosteric binding site throughout the production run. Hit-receptor interaction analyses revealed that the hits formed highly stable interactions with Glu 88, Trp 89, Leu 149, Ile 165, and Asn 167, and MM-GBSA energy analysis revealed that the three hits had high binding affinity to the allosteric site. The findings obtained herein can aid in identifying novel serotype-specific inhibitors of DENV protease in future.

The therapeutic strategy employing destabilization of the preformed Aβ fibril by various natural compounds, as studied by experimental and computational methods, has been reported significant in curing Alzheimer's disease (AD). However, lycopene (a carotenoid), from terpenes family, needs investigation for its destabilization potential of Aβ fibril. The highest antioxidant potential and ability to cross blood brain barrier makes lycopene a preferred choice as drug lead for treating AD. The current study focuses on investigating the destabilization potential and underpinning mechanism of lycopene on different polymorphic forms of Aβ fibril via Molecular Dynamics (MD) simulation. The key findings highlight binding of lycopene to the outer surface of the chain F of the fibril (2NAO). Herein G9, K16 and V18 residues were found to be involved in van der Waals with the methyl groups of the lycopene. Additionally, Y10 and F20 residues were observed to interact via π-π interactions with CC bonds of the lycopene. The surface mediated binding of lycopene to the fibril is attributed to the large size and structural rigidity of lycopene along with the bulky size of 2NAO and narrow space of fibrillar cavity. The destabilization of the fibril is evident by breakage of inherent H-bonds and hydrophobic interactions in the presence of one lycopene molecule. The lesser β-sheet content explains disorganization of the fibril and bars the higher order aggregation curbing neurotoxicity of the fibril. The higher concentration of the lycopene is not found to be linearly correlated with the extent of destabilization of the fibril. Lycopene is also observed to destabilize the other polymorphic form of Aβ fibril (2BEG), by accessing the fibrillar cavity and lowering the β-sheet content. The destabilization observed by lycopene on two major polymorphs of Aβ fibril explains its potency towards developing an effective therapeutic approach in treating AD.

N-glycosylation of eukaryotic proteins plays roles in protein folding, trafficking, and signal transduction. The biological influence of the process is well understood, whereas the pharmacological impact of protein N-glycosylation is not well under discerned. The role of N-glycosylation on drug binding to protein has been rarely studied. We have modeled the influence of a bi-antennary N-glycan introduced at position N78 on the immune checkpoint TIM-3 (T cell immunoglobulin domain and mucin domain-containing molecule 3) on the interaction with a selective drug antagonist. The bulky N-glycan introduced at the consensus sequence Asn-Val-Thr has no influence on drug binding when the glycan adopts an extended conformation. But in a folded conformation, the glycan can interact directly with the triazoloquinazolinone derivative so as to further stabilize the drug-TIM-3 complex. The non-fucosylated glycan at position N78 markedly consolidates the drug interaction, via an additional H-bond interaction with the α3-mannose residue. It provides a gain of empirical potential energy of interaction (ΔE) of about 30 %. The presence of a more rigid fucosylated N-glycan is a little less favorable, with a gain of ΔE of about 20 %. The folded N-glycan appears to protect the ligand bound to the protein cavity, with the tricyclic core of the heterocyclic molecule sandwiched between two indole rings of tryptophan residues. Similar results were obtained when using a biantennary disialyl N-glycan with a bisecting GlcNAc residue and a tetra-antennary N-glycan. The molecular models illustrate the drug-stabilizing capacity of a bulky N-glycan positioned at a validated glycosylation site (N78 corresponding to N100 for the full-length protein). The modeling approach is useful to delineate further the role of the N-glycan of the immune checkpoint TIM-3 in interaction with small molecule ligands, and to guide the design of more potent compounds. The approach is transposable to other proteins to better comprehend the influence of N-glycans on drug-receptor interactions.

Protein-RNA interactions play a key role in various biological cellular processes, and many experimental and computational studies have been initiated to analyze their interactions. However, experimental determination is quite complex and expensive. Therefore, researchers have worked to develop efficient computational tools to detect protein-RNA binding residues. The accuracy of existing methods is limited by the features of the target and the performance of the computational models; there remains room for improvement. To solve the problem of the accurate detection of protein-RNA binding residues, we propose a convolutional network model named PBRPre based on improved MobileNet. First, by extracting the position information of the target complex and the 3-mer amino acid feature data, the position-specific scoring matrix (PSSM) is improved by using spatial neighbor smoothing processing and discrete wavelet transform to fully exploit the spatial structure information of the target and enrich the feature dataset. Second, the deep learning model MobileNet is used to integrate and optimize the potential features in the target complexes; then, by introducing the Vision Transformer (ViT) network classification layer, the deep-level information of the target is mined to enhance the processing ability of the model for global information and to improve the detection accuracy of the classifiers. The results show that the AUC value of the model can reach 0.866 in the independent testing dataset, which shows that PBRPre can effectively realize the detection of protein-RNA binding residues. All datasets and resource codes of PBRPre are available at https://github.com/linglewu/PBRPre for academic use.

Classifying microarray datasets, which usually contains many noise genes that degrade the performance of classifiers and decrease classification accuracy rate, is a competitive research topic. Feature selection (FS) is one of the most practical ways for finding the most optimal subset of genes that increases classification's accuracy for diagnostic and prognostic prediction of tumor cancer from the microarray datasets. This means that we always need to develop more efficient FS methods, that select only optimal or close-to-optimal subset of features to improve classification performance. In this paper, we propose a hybrid FS method for microarray data processing, that combines an ensemble filter with an Improved Intelligent Water Drop (IIWD) algorithm as a wrapper by adding one of three local search (LS) algorithms: Tabu search (TS), Novel LS algorithm (NLSA), or Hill Climbing (HC) in each iteration from IWD, and using a correlation coefficient filter as a heuristic undesirability (HUD) for next node selection in the original IWD algorithm. The effects of adding three different LS algorithms to the proposed IIWD algorithm have been evaluated through comparing the performance of the proposed ensemble filter-IIWD-based wrapper without adding any LS algorithms named (PHFS-IWD) FS method versus its performance when adding a specific LS algorithm from (TS, NLSA or HC) in FS methods named, (PHFS-IWDTS, PHFS-IWDNLSA, and PHFS-IWDHC), respectively. Naïve Bayes(NB) classifier with five microarray datasets have been deployed for evaluating and comparing the proposed hybrid FS methods. Results show that using LS algorithms in each iteration from the IWD algorithm improves F-score value with an average equal to 5% compared with PHFS-IWD. Also, PHFS-IWDNLSA improves the F-score value with an average of 4.15% over PHFS-IWDTS, and 5.67% over PHFS-IWDHC while PHFS-IWDTS outperformed PHFS-IWDHC with an average of increment equal to 1.6%. On the other hand, the proposed hybrid-based FS methods improve accuracy with an average equal to 8.92% in three out of five datasets and decrease the number of genes with a percentage of 58.5% in all five datasets compared with six of the most recent state-of-the-art FS methods.

In our effort to develop potent anti-hyperglycemic compounds with inhibitory activity against α-amylase and α-glucosidase, a series of novel quinoxaline-isoxazole moieties were synthesized. The novel quinoxaline-isoxazole derivatives were assessed in vitro for their anti-hyperglycemic activities on α-amylase and α-glucosidase inhibitions. The results revealed promising IC50 values compared to acarbose as a positive control for α-amylase and α-glucosidase. Among them, N-Ethyl-7-chloro-3-((3-phenylisoxazol-5-yl)methoxy)quinoxalin-2-amine 5b showed dual inhibitory with IC50 of 24.0 µM for α-amylase and 41.7 µM for α-glucosidase. In addition, N-Ethyl-7-methoxy-3-((3-(2-chlorophenyl)isoxazol-5-yl)methoxy)quinoxalin-2-amine 5j also had dual bioactivities against α-amylase and α-glucosidase with IC50 of 17.0 and 40.1 µM, respectively. Nevertheless, two more compounds N-Ethyl-7-cyano-3-((3-phenylisoxazol-5-yl)methoxy)quinoxaline-2-amine 5e showed strong mono-inhibition for α-glucosidase with IC50 of 16.6 µM followed by N-Ethyl-7-methoxy-3-((3-phenylisoxazol-5-yl)methoxy)quinoxalin-2-amine 5 f with IC50 of 18.6 µM. The molecular docking study for α-glucosidase inhibitor provided the binding energy ranging from 8.3 to 9.1 kcal/mol and α-amylase inhibitor showed the binding energy score at 8.4 and 8.5 kcal/mol. The dual inhibitions nature of 5b and 5j were further analyzed and confirmed via molecular dynamics including the stability of the compound, interaction energy, binding free energy, and the interaction residue analysis using the MM-GBSA approach. The results showed that compound 5j was the most potent compound. Lastly, the drug-likeness properties were also evaluated with all synthesized compounds 5a-5j and the results reveal that all potent compounds meet Lipinski's rules of five.

The PI3K/Akt/mTOR is an essential intracellular signaling pathway in which the serine/threonine mTOR kinase portrays a major role in cell growth, proliferation and survival. The mTOR kinase is frequently dysregulated in a broad spectrum of cancers, thus making it a potential target. Rapamycin and its analogs (rapalogs) allosterically inhibit mTOR, thereby dodging the deleterious effects prompted by ATP-competitive mTOR inhibitors. However, the available mTOR allosteric site inhibitors exhibit low oral bioavailability and suboptimal solubility. Bearing in mind this narrow therapeutic window of the current allosteric mTOR inhibitors, an in silico study was designed in search of new macrocyclic inhibitors. The macrocycles from the ChemBridge database (12,677 molecules) were filtered for their drug-likeness properties and the procured compounds were subjected for molecular docking within the binding cleft between FKBP25 and FRB domains of mTOR. The docking analysis resulted with 15 macrocycles displaying higher scores than the selective mTOR allosteric site inhibitor, DL001. The docked complexes were refined by subsequent molecular dynamics simulations for a period of 100 ns. Successive binding free energy computation revealed a total of 7 macrocyclic compounds (HITS) demonstrating better binding affinity than DL001, towards mTOR. The consequent assessment of pharmacokinetic properties resulted in HITS with similar or better properties than the selective inhibitor, DL001. The HITS from this investigation could act as effective mTOR allosteric site inhibitors and serve as macrocyclic scaffolds for developing compounds targeting the dysregulated mTOR.

Amyloid fibrils formed by the mis-aggregation of amyloid proteins can lead to neuronal degenerations in the Alzheimer's disease. Predicting amyloid proteins not only contributes to understanding physicochemical properties and formation mechanism of amyloid proteins, but also has significant implications in the amyloid disease treatment and the development of a new purpose for amyloid materials. In this study, an ensemble learning model with sequence-derived features, ECAmyloid, is proposed to identify amyloids. The sequence-derived features including Pseudo Position Specificity Score Matrix (Pse-PSSM), Split Amino Acid Composition (SAAC), Solvent Accessibility (SA), and Secondary Structure Information (SSI) are employed to incorporate sequence composition, evolutionary and structural information. The individual learners of the ensemble learning model are selected by an increment classifier selection strategy. The final prediction results are determined by voting of prediction results of multiple individual learners. In view of the imbalanced benchmark dataset, the Synthetic Minority Over-sampling Technique (SMOTE) is adopted to generate positive samples. To eliminate irrelevant features and redundant features, correlation-based feature subset (CFS) selection combined with a heuristic search strategy is performed to obtain the optimal feature subset. Experimental results indicate that the ensemble classifier achieves an accuracy of 98.29%, a sensitivity of 0.992, a specificity of 0.974 on the training dataset using the 10-fold cross validation, far higher than the results obtained by its individual learners. Compared with the original feature set, the accuracy, sensitivity, specificity, MCC, F1-score, G-Mean of the ensemble method trained by the optimal feature subset are improved by 1.05%, 0.012, 0.01, 0.021, 0.011 and 0.011, respectively. Moreover, the comparison results with existing methods on two same independent test datasets demonstrate that the proposed method is an effective and promising predictor for large-scale determination of amyloid proteins. The data and code used to develop ECAmyloid has been shared to Github, and can be freely downloaded at https://github.com/KOALA-L/ECAmyloid.git.

The robustness of a breast cancer gene signature, the super-proliferation set (SPS), is initially tested and investigated on breast cancer cell lines from the Cancer Cell Line Encyclopaedia (CCLE). Previously, SPS was derived via a meta-analysis of 47 independent breast cancer gene signatures, benchmarked on survival information from clinical data in the NKI dataset. Here, relying on the stability of cell line data and associative prior knowledge, we first demonstrate through Principal Component Analysis (PCA) that SPS prioritizes survival information over secondary subtype information, surpassing both PAM50 and Boruta, an artificial intelligence-based feature-selection algorithm, in this regard. We can also extract higher resolution 'progression' information using SPS, dividing survival outcomes into several clinically relevant stages ('good', 'intermediate', and 'bad) based on different quadrants of the PCA scatterplot. Furthermore, by transferring these 'progression' annotations onto independent clinical datasets, we demonstrate the generalisability of our method on actual patient data. Finally, via the characteristic genetic profiles of each quadrant/stage, we identified efficacious drugs using their gene reversal scores that can shift signatures across quadrants/stages, in a process known as gene signature reversal. This confirms the power of meta-analytical approaches for gene signature inference in breast cancer, as well as the clinical benefit in translating these inferences onto real-world patient data for more targeted therapies.

Cell division is a crucial process for the growth and development of all living organisms. Unfortunately, uncontrolled cell division and growth is a hallmark of cancer, leading to the formation of tumors. The Human Eg5 protein, also known as the mitotic kinesin Eg5, plays a vital role in the regulation of cell division and its dysfunction has been linked to cancer development. This study aimed to identify new inhibitors of the Human Eg5 protein. Over 2000 Traditional Chinese Medicine (TCM) compounds were screened through a combination of virtual and structure-based screening methods. The top five compounds (Compounds 1-5) showed improved binding affinity to Human Eg5 compared to the standard drug Monastrol, as demonstrated by docking and MMGBSA scores, as well as interactions with key amino acids GLY 116 and GLY 118. The potential absorption and bioactivity of these compounds were also predicted through ADMET properties and a QSAR model, respectively, and showed improved results compared to the standard. Further quantum mechanics docking confirmed the better binding affinity of the lead compound, Compound 1. Our findings highlight Compound 1-5 as promising hits for inhibiting Human Eg5 and the need for experimental validation of their potential in treating cancer.

Acute Myeloid Leukemia (AML) can be detected based on morphology, cytochemistry, immunological markers, and cytogenetics. MicroRNAs (miRNAs) influence key biological pathways in multiple haematological malignancies including AML. In this work, we have analysed the miRNome and the transcriptome of normal and AML samples and have identified the significant set of miRNA-target mRNA pairs present within AML- Peripheral Blood and AML- Bone Marrow samples from both tissue and cell lines. The miRNA target genes are further filtered based on their functional significance in AML system. These filtered genes constitute the set of selected miRNA target features, which have been finally used for developing machine learning based prediction tool, 'TbAMLPred' for preliminary detection of AML. This model implements both unsupervised clustering and supervised classification algorithms that would increase the reliability of prediction. Our results show that the selected miRNA target-based features can separate the control and disease samples linearly. Overall, we put forward 'TbAMLPred' for a non-invasive mode of preliminary AML diagnosis in future. Github link for accessing TbAMLPred: https://github.com/zglabDIB/TbAMLPred.

Many experiments have proved that long non-coding RNAs (lncRNAs) in humans have been implicated in disease development. The prediction of lncRNA-disease association is essential in promoting disease treatment and drug development. It is time-consuming and laborious to explore the relationship between lncRNA and diseases in the laboratory. The computation-based approach has clear advantages and has become a promising research direction. This paper proposes a new lncRNA disease association prediction algorithm BRWMC. Firstly, BRWMC constructed several lncRNA (disease) similarity networks based on different measurement angles and fused them into an integrated similarity network by similarity network fusion (SNF). In addition, the random walk method is used to preprocess the known lncRNA-disease association matrix and calculate the estimated scores of potential lncRNA-disease associations. Finally, the matrix completion method accurately predicts the potential lncRNA-disease associations. Under the framework of leave-one-out cross-validation and 5-fold cross-validation, the AUC values obtained by BRWMC are 0.9610 and 0.9739, respectively. In addition, case studies of three common diseases show that BRWMC is a reliable method for prediction.

Glucokinase (GK), an isoform of hexokinase expressed predominantly in liver, pancreas and hypothalamus is crucial to blood glucose management. It is a critical component of the glucose-sensing mechanism of the pancreatic islet cells and glycogen regulation in hepatocytes. GK modulators such as allosteric GKAs (glucokinase activators) and GK-GKRP (glucokinase regulatory protein) disruptors have found potential applications as safer antihyperglycemics. Recent studies have also demonstrated the potential of GK modulators as antiparasitic agents. Researchers targeting GK often undertake the time-consuming task of independently collecting and compiling modulator information due to the lack of any dedicated single-platform resource. Towards this, in the present study we demonstrate the design and development of GlucoKinaseDB (GKDB), a comprehensive, curated, online resource of GK modulators. GKDB contains experimentally derived structural and bioactivity information of 1723 modulators along with their detailed molecular descriptors. The web-interface is user-friendly with features such as in-browser visualization, advanced search queries, cross-links to other databases and original reference etc. The bioactivity and descriptor data can be downloaded in bulk (for entire database) or for individual modulators. The 3D structures are also downloadable in multiple formats. GKDB employs a PHP-based web design with Bootstrap styling and a MySQL database backend. GKDB can be utilized for clinical and molecular research via development of pharmacophore hypotheses, QSAR/QSPR models, predictive machine learning models etc. GKDB is freely accessible online at https://glucokinasedb.in.

Mimicry of host proteins is a strategy employed by pathogens to hijack host functions. Domain and motif mimicry was explored in the experimental and predicted SARS-CoV-2-human interactome. The host first interactor proteins were also added to capture the continuum of the interactions. The domains and motifs of the proteins were annotated using NCBI CD Search and ScanProsite, respectively. Host and pathogen proteins with a common host interactor and similar domain/motif constitute a mimicry pair indicating global structural similarity (domain mimicry pair; DMP) or local sequence similarity (motif mimicry pair; MMP). 593 DMPs and 7,02,472 MMPs were determined. AAA, DEXDc and Macro domains were frequent among DMPs whereas glycosylation, myristoylation and RGD motifs were abundant among MMP. The proteins involved in mimicry were visualised as a SARS-CoV-2 mimicry interaction network. The host proteins were enriched in multiple CVD pathways indicating the role of mimicry in COVID-19 associated CVDs. Bridging nodes were identified as potential drug targets. Approved antihypertensive and anti-inflammatory drugs are proposed for repurposing against COVID-19 associated CVDs. The SARS-CoV-2 mimicry data has been updated in ImitateDB (http://imitatedb.sblab-nsit.net/SARSCoV2Mimicry). Determination of key mechanisms, proteins, pathways, drug targets and repurposing candidates is critical for developing therapeutics for SARS CoV-2 associated CVDs.

DapE is a microbial metalloenzyme that hosts two Zn ions in its active site, although it shows catalytic activity with varying efficiency when the Zn ions in one or both of its metal-binding sites (MBS) are replaced by other transition-metal ions. The metal-ion promiscuity of DapE is believed to be a microbial strategy to overcome the homeostatic regulation of Zn ions by the mammalian host. Here, a hybrid QM/MM study is performed on a series of mixed-metal DapEs, where the Zn ion in the first MBS (MBS-1) is substituted by Mn, Co, Ni, and Cu ions, while the MBS-2 is occupied by Zn(II). The substrate binding affinity and the mechanism of catalytic action are estimated by optimizing the intermediates and the transition states with hybrid QM/MM method. Comparison of the binding affinity of the MBS-1 and MBS-2 substituted DapEs reveals that the MBS-1 substitution does not affect the substrate binding affinity in the mixed-metal DapEs, while a strong metal specificity was observed in MBS-2 substituted DapEs. On the contrary, the activation energy barriers show a high metal specificity at MBS-1 compared to MBS-2. Taken together, the QM/MM studies indicate that MBS-2 leads the substrate binding process, while MBS-1 steers the catalytic activity of the DapE enzyme.

Nucleoside analogs/derivatives (NAs/NDs) with potent antiviral activities are now deemed very convenient choices for the treatment of coronavirus disease 2019 (COVID-19) arisen by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. At the same time, the appearance of a new strain of SARS-CoV-2, the Omicron variant, necessitates multiplied efforts in fighting COVID-19. Counteracting the crucial SARS-CoV-2 enzymes RNA-dependent RNA polymerase (RdRp) and 3'-to-5' exoribonuclease (ExoN) jointly altogether using the same inhibitor is a quite successful new plan to demultiplicate SARS-CoV-2 particles and eliminate COVID-19 whatever the SARS-CoV-2 subtype is (due to the significant conservation nature of RdRps and ExoNs in the different SARS-CoV-2 strains). Successive in silico screening of known NAs finally disclosed six different promising NAs, which are riboprine/forodesine/tecadenoson/nelarabine/vidarabine/maribavir, respectively, that predictably can act through the planned dual-action mode. Further in vitro evaluations affirmed the anti-SARS-CoV-2/anti-COVID-19 potentials of these NAs, with riboprine and forodesine being at the top. The two NAs are able to effectively antagonize the replication of the new virulent SARS-CoV-2 strains with considerably minute in vitro anti-RdRp and anti-SARS-CoV-2 EC50 values of 189 and 408 nM for riboprine and 207 and 657 nM for forodesine, respectively, surpassing both remdesivir and the new anti-COVID-19 drug molnupiravir. Furthermore, the favorable structural characteristics of the two molecules qualify them for varied types of isosteric and analogistic chemical derivatization. In one word, the present important outcomes of this comprehensive dual study revealed the anticipating repurposing potentials of some known nucleosides, led by the two NAs riboprine and forodesine, to successfully discontinue the coronaviral-2 polymerase/exoribonuclease interactions with RNA nucleotides in the SARS-CoV-2 Omicron variant (BA.5 sublineage) and accordingly alleviate COVID-19 infections, motivating us to initiate the two drugs' diverse anti-COVID-19 pharmacological evaluations to add both of them betimes in the COVID-19 therapeutic protocols.

De novo drug design refers to the process of designing new drug molecules from scratch using computational methods. In contrast to other computational methods that primarily focus on modifying existing molecules, designing from scratch enables the exploration of new chemical space and the potential discovery of novel molecules with enhanced properties. In this research, we proposed a model that utilizes Generative Pre-trained Transformer (GPT) architecture and relative attention for de novo drug design. GPT is a language model that utilizes transformer architecture to predict the next word or token in a given sequence. Representation of molecules using SMILES notation has enabled the use of next-token prediction techniques in de novo drug design. GPT uses attention mechanisms to capture the dependencies and relationships between different tokens in a sequence and allows the model to focus on the most important information when processing the input. Relative attention is a variant of the attention mechanism, which allows the model to capture the relative distances and relationships between tokens in the input sequence. In the standard attention mechanism, positional information is typically encoded using fixed-position embeddings. In relative attention, positional information is supplied dynamically during attention calculation by incorporating relative positional encodings, enabling the model to quickly learn the syntax of new unseen tokens. Relative attention enables the GPT model to better understand the relative positions of tokens in the sequence, which can be particularly useful when dealing with limited dataset sizes or generating target-specific drugs. The proposed model was trained on benchmark datasets, and performance was compared with other generative models. We show that relative attention and transfer learning could enable the GPT model to generate molecules with improved validity, uniqueness, and novelty in the context of de novo drug design. To illustrate the effectiveness of relative attention, the model was trained using transfer learning on three target-specific datasets, and the performance was compared with standard attention.

This study aims to investigate the potential therapeutic application of Ixeridium dentatum (ID) in treating atopic dermatitis (AD) through network pharmacology, molecular docking, and molecular dynamic simulation. We employed GC-MS techniques and identified 40 bioactive compounds present in the ID and determined their targets by accessing public databases. The convergence of compounds and dermatitis related targets led to the identification of 32 common genes. Among them, IL1B, PTGS2, IL6, IL2, and RELA, were found to be significant targets which were analyzed using Cytoscape network topology. The KEGG pathway evaluation revealed that these targets were significantly enriched in the C-type lectin receptor signaling pathway. The therapeutic efficacy of Stigmasta-5,22-dien-3-ol, Urea, n-Heptyl-, and 3-Epimoretenol was demonstrated in molecular docking assay, as evidenced by their presence in the core compounds of the compound-target network. Furthermore, these compounds exhibited significant kinetic stability and chemical reactivity in DFT quantum analysis when compared to their co-crystallized ligands and reference drug, indicating their potential as key targets for future research. Among the top three docking complexes, namely IL6-3-Epimoretenol, and IL2- Stigmasta-5,22-dien-3-ol, both demonstrated exceptional dynamic characteristics in molecular dynamics simulations at 100 ns. The feasibility of these compounds could be attributed to the prior traditional interrelationship between ID and AD. Overall, this research elucidates the interplay between AD-associated signaling pathways and target receptors with the bioactive ID. The proposal posits the utilization of antecedent compounds as a substitute for the customary pharmaceutical intervention that obstructs the discharge of cytokines, which incite dermal inflammation in the C-type lectin receptor signaling pathway of atopic dermatitis.

High-grade serous ovarian cancer (HGSOC) is a type of ovarian cancer developed from serous tubal intraepithelial carcinoma. The intrinsic differences among molecular subtypes are closely associated with prognosis and pathological characteristics. At present, multi-omics data integration methods include early integration and late integration. Most existing HGSOC molecular subtypes classification methods are based on the early integration of multi-omics data. The mutual interference among multi-omics data is ignored, which affects the effectiveness of feature learning. High-dimensional multi-omics data contains genes unassociated with HGSOC molecular subtypes, resulting in redundant information, which is not conducive to model training. In this paper, we propose a multi-modal deep autoencoder learning method, MMDAE-HGSOC. MiRNA expression, DNA methylation, and copy number variation (CNV) are integrated with mRNA expression data to construct a multi-omics feature space. The multi-modal deep autoencoder network is used to learn the high-level feature representation of multi-omics data. The superposition LASSO (S-LASSO) regression algorithm is proposed to fully obtain the associated genes of HGSOC molecular subtypes. The experimental results show that MMDAE-HGSOC is superior to the existing classification methods. Finally, we analyze the enrichment gene ontology (GO) terms and biological pathways of these significant genes, which are discovered during the gene selection process.

MOTIVATION: Computational promoter prediction (CPP) tools designed to classify prokaryotic promoter regions usually assume that a transcription start site (TSS) is located at a predefined position within each promoter region. Such CPP tools are sensitive to any positional shifting of the TSS in a windowed region, and they are unsuitable for determining the boundaries of prokaryotic promoters. RESULTS: TSSUNet-MB is a deep learning model developed to identify the TSSs of σ70 promoters. Mononucleotide and bendability were used to encode input sequences. TSSUNet-MB outperforms other CPP tools when assessed using the sequences obtained from the neighborhood of real promoters. TSSUNet-MB achieved a sensitivity of 0.839 and specificity of 0.768 on sliding sequences, while other CPP tool cannot maintain both sensitivities and specificities in a compatible range. Furthermore, TSSUNet-MB can precisely predict the TSS position of σ70 promoter-containing regions with a 10-base accuracy of 77.6%. By leveraging the sliding window scanning approach, we further computed the confidence score of each predicted TSS, which allows for more accurately identifying TSS locations. Our results suggest that TSSUNet-MB is a robust tool for finding σ70 promoters and identifying TSSs.

The characterization of drug - metabolizing enzymes is a significant problem for customized therapy. It is important to choose the right drugs for cancer victims, and the ability to forecast how those drugs will react is usually based on the available information, genetic sequence, and structural properties. To the finest of our knowledge, this is the first study to evaluate optimization algorithms for selection of features and pharmacogenetics categorization using classification methods based on a successful evolutionary algorithm using datasets from the Cancer Cell Line Encyclopaedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC). The study proposes the uses of Firefly and Grey Wolf Optimization techniques for feature extraction, while comparing the traditional Machine Learning (ML), ensemble ML and Stacking Algorithm with the proposed Convolutional Temporal Deep Neural Network or CTDN. With the potential to increase efficiency from the suggested intelligible classifier model for a suggestive chemotherapeutic drugs response prediction, our study is important in particular for selecting an acceptable feature selection method. The comparison analysis demonstrates that the proposed model not only surpasses the prior state-of-the-art methods, but also uses Grey Wolf and Fire Fly Optimization to lessen multicollinearity and overfitting.

Malaria is a major global health issue due to the emergence of resistance to most of the available antimalarial drugs. There is an urgent need to discover new antimalarials to tackle the resistance issue. The present study aims to explore the antimalarial potential of chemical constituents reported from Cissampelos pareira L., a medicinal plant traditionally known for treating malaria. Phytochemically, benzylisoquinolines and bisbenzylisoquinolines are the major classes of alkaloids reported from this plant. In silico molecular docking revealed prominent interactions of bisbenzylisoquinolines such as hayatinine and curine with Pfdihydrofolate reductase (-6.983 Kcal/mol and -6.237 Kcal/mol), PfcGMP-dependent protein kinase (-6.652 Kcal/mol and -7.158 Kcal/mol), and Pfprolyl-tRNA synthetase (-7.569 Kcal/mol and -7.122 Kcal/mol). The binding affinity of hayatinine and curine with identified antimalarial targets was further evaluated using MD-simulation analysis. Among the identified antimalarial targets, the RMSD, RMSF, the radius of gyration, and PCA indicated the formation of stable complexes of hayatinine and curine with Pfprolyl-tRNA synthetase. The outcomes of in silico investigation putatively suggested that bisbenzylisoquinolines may act on the translation of the Plasmodium parasite to exhibit antimalarial potency.

Nipah virus (NiV) has been an alarming threat to human populations in southern Asia for more than a decade. It is one of the most deadly viruses in the Mononegavirales order. Despite its high mortality rate and virulence, no chemotherapeutic agent or vaccine is publicly available. Hence, this work was conducted to computationally screen marine natural products database for drug-like potential inhibitors for the viral RNA-dependent RNA polymerase (RdRp). The structural model was subjected to molecular dynamics (MD) simulation to obtain the native ensemble of the protein. The CMNPDB dataset of marine natural products was filtered to retain only compounds following Lipinski's five rules. The molecules were energy minimized and docked into different conformers of the RdRp using AutoDock Vina. The best 35 molecules were rescored by GNINA, a deep learning-based docking software. The resulting nine compounds were evaluated for their pharmacokinetic profiles and medicinal chemistry properties. The best five compounds were subjected to MD simulation for 100 ns, followed by binding free energy estimation via Molecular Mechanics/ Generalized Born Surface Area (MM/GBSA) calculations. The results showed remarkable behavior of five hits as inferred by stable binding pose and orientation to block the exit channel of RNA synthesis products in the RdRp cavity. These hits are promising starting materials for in vitro validation and structural modifications to enhance the pharmacokinetic and medicinal chemistry properties for developing antiviral lead compounds.

Covid-19 has caused massive numbers of infections and fatalities globally. In response, there has been a large-scale experimental and computational research effort to study and develop drugs. Towards this, Deep learning techniques are used for the generation of potential novel drug candidates that are proven to be effective against exploring large molecular search spaces. Recent advances in reinforcement learning in conjunction with generative techniques has proven to be a promising field in the area of drug discovery. In this regard, we propose a generative drug discovery approach using reinforcement techniques for sampling novel molecules that bind to the main protease of SARS-COV2. The generative method reported significant validity scores for the generated novel molecules and captured the underlying features of the training molecules. Further, the model is fine-tuned on existing re-purposed molecules which are active towards specific target proteins based on similarity metrics. Upon fine tuning the model generated 92.71% valid, 93.55% unique, and 100% novel molecules. Unlike previous methods which are dependent on docking procedures, we proposed a deep learning based novel drug target interaction (DTI) model to find the binding affinity between candidate molecules and target protease sequence. Finally, the binding affinity of the generated molecules is predicted against the 3CLPro main protease by using the proposed DTI model. Most of the generated molecules have shown binding affinity scores <100 nM (lower the better), which are significantly better compared to the existing commercial drugs including Remdesevir.

Poor pharmacokinetic and safety profiles create significant hurdles in the drug development process. This work focuses on a detailed understanding of drug discovery interplay among physicochemical, pharmacokinetic, toxicity endpoints, and antioxidant properties of oxindole derivatives. DFT compıutations were also performed at B3LYP/6-311G** level to evaluate the physicochemical properties, global reactivity features, and intramolecular interactions. The BOILED-Egg pharmacokinetic model envisaged gastrointestinal absorption, blood-brain barrier penetration, and no interaction with p-glycoprotein for compounds C1 and C2. The physicochemical evaluation revealed that C1 possesses superior drug-like properties fit for oral absorption. Both derivatives were predicted to have high plasma protein binding, efficient distribution, and inhibiting CYP 450 major isoforms but serve as substrates only for a few of them. Both molecules have mild to moderate clearance rates. Out of ten toxicity parameters, only hepatotoxicity was predicted. DFT results implied that the meta position of the -OH group made the possibility of charge transfer greater than -para positioned -OH, due to the ΔNmax (eV) values of molecules C1 and C2 being calculated at 2.596 and 2.477, respectively. Both C1 and C2 exhibited a concentration dependant DPPH and ABTS radical scavenging activity. The chemical structure-physicochemical-pharmacokinetic relationship identified the meta position as the favorite for the electron-withdrawing hydroxyl group. This provides useful insight to medicinal chemists to design 6-chlorooxindole derivatives with an acceptable drug-like and pharmacokinetic property.

Regarding the importance of N-degron pathway in protein degradation network, the adaptor protein ClpS recognizes the substrates bearing classical N-degrons, and delivers them to caseinolytic protease complex ClpAP for degradation. Interestingly, the majority of N-degrons located near the N-terminus of protein substrate are belonged to the hydrophobic type amino acids. Chloroplast, an important organelle for plant photosynthesis, contain a diversified Clp degradation system. Despite several studies have confirmed that chloroplastic ClpS is able to interact with classical N-degrons derived from prokaryotes, whereas, the molecular mechanism underlying how the chloroplastic ClpS protein could recognize the substrate tagged by N-degrons is still unclear until now. Chlamydomonas reinhardtii is a kind of unicellular model organism for photosynthesis researches, which possesses a large cup-shaped chloroplast, and the corresponding genome data indicates that it owns bacterial homologous adaptor protein, named CrClpS1. However, the relevant biochemical knowledges, and protein structure researches for CrClpS1 adaptor aren't reported up to date. The molecular interactions between CrClpS1 and possible N-degrons are undefined as well. Here, we build a reliable homology model of CrClpS1 and find a hydrophobic pocket for N-degron binding. We combine molecular docking, molecular dynamic simulations, and MM/PBSA, MM/GBSA binding free energy estimations to elucidate the molecular properties of CrClpS1-N-degron interactions. Besides, we investigate the conformational changes for CrClpS1-apo in water-solvent environment and analyze its possible biological significances through a long time molecular dynamic simulation. Specifically, the adaptor CrClpS1 displays the stronger interactions with Phe, Trp, Tyr, His and Ile with respect to other amino acids. Using the residue decomposition analysis, the interactions between CrClpS1 and N-degrons are heavily depended on several conservative residues, which are located around the hydrophobic pocket, implying that chloroplast isolated from Chlamydomonas reinhadtii adopts a relatively conservative N-degron recognition mode. Besides, the opening-closure of hydrophobic pocket of CrClpS1 might be beneficial for the N-degron selectivity.

COVID-19 shook the world during the pandemic, where the climax it reached was vaccine manufacturing at an unfathomable pace. Alternative promising solutions to prevent infection from SARS-CoV-2 and its variants will remain crucial in the years to come. Due to its key role in viral replication, the major protease (Mpro) enzyme of SARS-CoV-2 can be an attractive therapeutic target. In the present work, natural terpenoids from mangrove medicinal plant Xylocarpus moluccensis (Lam.) M. Roem. were screened using computational methods for inhibition of Mpro protein. Out of sixty-seven terpenoids, Angolensic acid methyl ester, Moluccensin V, Thaixylomolin F, Godavarin J, and Xylomexicanolide A were shortlisted based on their docking scores and interaction affinities (- 13.502 to - 15.52 kcal/mol). The efficacy was validated by the 100 ns molecular dynamics study. Lead terpenoids were within the acceptable range of RMSD and RMSF with a mean value of 2.5 Å and 1.5 Å, respectively indicating that they bound tightly within Mpro and there was minimal fluctuation and stability of Mpro upon binding of these terpenoids. The utmost favorable binding strengths as calculated by MM-GBSA, were of Angolensic acid methyl ester and Moluccensin V with binding free energies (ΔGbind) of - 39.084, and - 43.160 kcal/mol, respectively. The terpenoids showed no violations in terms of Drug Likeliness and ADMET predictions. Overall, the findings indicate that Angolensic acid methyl ester and Moluccensin V are effective terpenoids having strong binding interaction with Mpro protein, which must be tested in vitro as an effective anti-SARS-CoV-2 drug.

In this study, two series of novel 1,4-benzothiazine-3-one derivatives with alkyl substitution (series 1: 4a-4f) and aryl substitution (series 2: 4g-4l) were designed and synthesized based on the chemical scaffolds of perampanel, hydantoins, progabide and etifoxine as anti-convulsant agents. The chemical structures of the synthesized compounds were confirmed by FT-IR, 1H NMR and 13C NMR spectroscopy. Anti-convulsant effect of the compounds was examined through intraperitoneal pentylenetetrazol (i.p. PTZ) induced epilepsy mouse models. Compound 4h (4-(4-bromo-benzyl)- 4 H-benzo[b] [1,4] thiazin-3(4 H)-one) demonstrated a promising activity toward chemically-induced seizure experiment. Molecular dynamics simulation on GABA-Aergic receptors as a plausible mechanism were also done to achieve the binding and orientation of compounds in the active site of the target to evaluate the results of docking and experimental studies. The computational results were confirmed the biological activity. DFT study of 4c and 4h was performed on B3LYP/6-311 G** level of theory. Reactivity descriptors such as HOMO, LUMO, electron affinity, ionization potential, chemical potential, hardness and softness were studied in detail and show that 4h has higher activity than 4c. Also, the frequency calculations were performed on the same level of theory and the results are in line with experimental data. Moreover, in silico ADMET properties were done to establish a relationship between the physiochemical data of the designed compounds and their in-vivo activity. Appropriate plasma protein binding and high blood-brain barrier penetration are the main features of desired in-vivo performance.

Metisa plana (Lepidoptera: Psychidae) bagworm is a leaf-eater caterpillar ubiquitously found as a damaging pest in oil palm plantations, specifically in Malaysia. Various strategies have been implemented, including the usage of chemical insecticides. However, the main challenges include the development of insecticide resistance and its detrimental effects on the environment and non-target organisms. Therefore, a biorational insecticide is introduced by targeting the juvenile hormone (JH) biosynthetic pathway, which is mainly present in the insect and vital for the insect's growth, diapause, metamorphosis, and adult reproduction. This study aimed to investigate the potential inhibitor for the rate-limiting enzyme involved in the JH pathway known as farnesol dehydrogenase. A 255 amino acids sequence encoded for the putative M. plana farnesol dehydrogenase (MpFolDH) open reading frame had been identified and isolated. The three-dimensional structure of MpFolDH was predicted to have seven β- sheets with α-helices at both sides, showing typical characteristics for classical short-chain dehydrogenase and associated with oxidoreductase activity. Then, the ensemble-based virtual screening was conducted based on the ZINC20 database, in which 43 768 compounds that fulfilled pesticide-likeness criteria were screened by site-specific molecular docking. After a short molecular dynamics simulation (5 ns) was conducted towards 102 compounds, only the top 10 compounds based on their most favourable binding energy were selected for a more extended simulation (100 ns). Based on the protein-ligand stability, protein compactness, residues rigidity, binding interaction, binding energy throughout the 100 ns simulation, and physicochemical analysis, ZINC000408743205 was selected as a potential inhibitor for this enzyme. Amino acids decomposition analysis indicates Ile18, Ala95, Val198 and Val202 were the critical contributor residues for MpFolDH-inhibitors(s) complex.

Transcription control through cis-regulatory elements (CREs) is one of important regulators of gene expression. This study aimed to identify the location of CREs in oil palm (Elaeis guineensis Jacq.) using the combination of DNA free energy and single nucleotide polymorphism (SNP) density approaches. Promoter region sequences were extracted oil palm genome spanning from 1500 nucleotides (nt) upstream to 1000 nt downstream of every annotated transcription start sites (TSS). Free energy profiles of each promoter region were calculated using PromPredict software. Raw reads from the deep sequencing of 59 oil palm origins were used to calculate SNP density of each promoter region. The result showed that the average free energy (AFE) on the upstream region of TSS is about 1.5 kcal/mol higher compared to the downstream region. Using DNA free energy method, 16,281 regions of CREs were predicted. Most of predicted CREs was located between 1 and 500 nt upstream of TSS. Anti-correlation pattern between free energy and SNP density was observed on the predicted regions of CREs. This anti-correlated pattern was also observed on an experimentally determined promoter of the oil palm metallothionein gene, EgMSP1. Considering the increasing use of promoter information on plant biotechnology, an easy and accurate promoter prediction using the combination of free energy and SNP density method could be recommended.

This study delved into the exploration of novel antidiabetic medications acquired from natural resources, utilizing the Ayurvedic Rasayana herb Hemidesmus indicus through cutting-edge chemoprofiling and molecular modelling techniques. The methanolic extract of Hemidesmus indicus root exhibited the highest extractive yield (24.70 ± 0.08 %) and contained substantial levels of total phenolic and flavonoid content as 154.15 ± 1.24 mg Gallic Acid Equivalent/g extract and 70.61 ± 0.35 Quercetin Equivalent/g extract respectively. Invitro study revealed the potent inhibitory potential of methanolic extract of the herb against essential carbohydrate hydrolytic enzymes α-amylase (IC50 = 4.19 ± 0.04 mg/ml) and α-glucosidase (IC50 = 5.78 ± 0.10 mg/ml). Further, the enzyme kinetic study demonstrated the competitive mode of inhibition of both enzymes. HR-LCMS analysis identified the major phytoconstituents present in the extracts, including Solanocapsine, Cyclovirobuxine C, Lucidine B, Zygadenine, Aspidospermidine, silychristin, 3beta-3-Hydroxy-18-lupen-21-one, Manglupenone, and 19-Noretiocholanolone. Molecular docking, molecular dynamic simulation, and MM/GBSA analysis have proved stable, rigid, compact, and folded form of complexes during the entire 100 ns simulation, illustrating Zygadenine, Solanocapsine, and Cyclovirobuxine C as the superior inhibitors of α-A protein, while Zygadenine, Plumieride, and Phlegmarine exhibited greater inhibitory behaviour towards α-G protein than the FDA-approved drug acarbose. Collectively, our findings indicate that the Hemidesmus indicus could be a promising source of α-A and α-G inhibitors, potentially serving as a lead in order to develop medications for type-2 diabetes.

Heterocyclic compounds exert diverse functions, especially acetylcholinesterase (AChE) inhibition. Thus, identifying the association between their detailed structures and biological activities is important to the development of novel medications for Alzheimer's disease (AD) treatment. In this study, diverse sets of 120 potent and selective heterocyclic compounds (-log[the half‑maximal inhibitory concentration] (pIC50) values ranged from 8.01 to 12.50) were used to develop quantitative structure-activity relationship (QSAR) models using multiple linear regression (MLR), multiple nonlinear regression (MNLR), Bayesian model average (BMA), and artificial neural network (ANN) models. The models' robustness and stability have been assessed using both internal and external methodology. ANN outperforms MLR, MNLR, and BMA according to external validation. The molecular descriptors incorporated into the model were in satisfactory correlation with the AChE receptor-ligand complex X-ray structures, making the model interpretable and predictive. Three selected compounds exert drug-like characteristics (pIC50 values ranged from 11.01 to 11.17). The binding affinity between the optimal compounds and the AChE receptor (RCSB ID 3LII) ranged from - 7.4 to - 8.8 kcal/mol. Remarkably, the pharmacokinetics, physicochemical properties, and biological activities of compound 25 (C23H32N2O2, PubChem CID 118727071, pIC50 value = 11.17) were found to be consistent with its therapeutic effects in AD due to its cholinergic and non-toxic nature, non-P-glycoprotein, high gastrointestinal absorption, and capability to penetrate the blood-brain barrier.

To date, Cancer remains a global threat due to its impact on growing life expectancy. With the many efforts and methods of combating the disease, complete success remains a challenge owing to several limitations including cancer cells developing resistance through mutations, off-target effect of some cancer drugs resulting in toxicities, among many others. Aberrant DNA methylation is understood to be the primary reason for improper gene silence, which can result in neoplastic transformation, carcinogenesis, and tumour progression. DNA methyltransferase B (DNMT3B) enzyme is considered a potential target for the treatment of several cancers due to its important role in DNA methylation. However, only a few DNMT3B inhibitors have been reported to date. Herein, in silico molecular recognition techniques such as Molecular docking, Pharmacophore-based virtual screen and MD simulation were employed to identify potential inhibitors of DNMT3B that can halt aberrancy in DNA methylation. Findings initially identified 878 hit compounds based on a designed pharmacophore model from the reference compound Hypericin. Molecular docking was used to rank the hits by testing their efficiency when bound to the target enzyme and the top three (3) selected. All three (3) of the top hits showed excellent pharmacokinetic properties but two (2) (Zinc33330198 and Zinc77235130) were identified to be non-toxic. Molecular dynamic simulation of the final two hits showed good stability, flexibility, and structural rigidity of the compounds on DNMT3B. Finally, thermodynamic energy estimations show both compounds had favourable free energies comprising - 26.04 kcal/mol for Zinc77235130 and - 15.73 kcal/mol for Zinc33330198. Amongst the final two hits, Zinc77235130 showed consistency in favourable results across all the tested parameters and was thus selected as the lead compound for further experimental validation. The identification of this lead compound will form important basis for the inhibition of aberrant DNA methylation in cancer therapy.

GH-20 β-N-acetylglucosaminidases (GlcNAcases) are promising targets in the development of antimicrobial agents against Vibrio infections in humans and aquatic animals. In this study, we set up structure-based virtual screening to identify potential GH-20 GlcNAcase inhibitors from the Reaxys commercial database, using VhGlcNAcase from V. campbellii type strain ATCC® BAA 1116 as the protein target and Redoxal as the reference ligand. Using ChemPLP and RF-Score-VS machine learning scoring functions, eight lead compounds were identified and further evaluated for protein interaction preference and pharmacological properties. Protein-ligand analysis demonstrated that all selected compounds interacted exclusively at subsite - 1 with five hydrophobic residues W487, W505, W546, W582 and V544 at site S1, and with two polar residues, D437 and E438, at site 3. For subsite + 1, the most common residues were R274 and E584 at site 2 and I397 and Q398 at site 4. Based on the data obtained from binding free energy changes (ΔG°binding), pharmacological property analysis and molecular dynamic simulations, two ChemPLP compounds, 338175 and 1146525, and one RF-Score-VS compound, 337447, were considered as the likely lead compounds. The most promising compound, 1146525, could serve as a scaffold for the future design of novel antimicrobial agents against Vibrio infections.

Lipopeptides are medicinally essential building blocks with strong hemolytic, antifungal and antibiotic potential. In the present research article, we are presenting our findings regarding the synthesis of N-alkylated lipopeptides via Ugi four-component approach, their antimicrobial potential against pathogenic (Gram-positive and Gram-negative) bacteria, as well as computational studies to investigate the compounds binding affinity and dynamic behavior with MurD antibacterial target. Molecular docking demonstrated the compounds have good binding ability with MurD enzyme. The FT94, FT95 and FT97 compounds revealed binding affinity scores of -8.585 kcal mol- 1, -7.660 kcal mol- 1 and -7.351 kcal mol- 1, respectively. Furthermore, dynamics analysis pointed the systems high structure dynamics. The docking and simulation results were validated by binding free energies, demonstrating solid intermolecular interactions and in the assay in vitro, the Minimal Inhibitory Concentration (MIC) of FT97 to Staphylococcus aureus (S. aureus) was 62.5 μg/mL. In conclusion, a moderate inhibitory response of peptoid FT97 was observed against the Gram-positive bacteria, S. aureus and B. cereus.

Since dysregulation of the phosphatidylinositol 3-kinase gamma (PI3Kγ) signaling pathway is associated with the pathogenesis of cancer, inflammation, and autoimmunity, PI3Kγ has emerged as an attractive target for drug development. IPI-549 is the only selective PI3Kγ inhibitor that has advanced to clinical trials, thus, IPI-549 could serve as a promising template for designing novel PI3Kγ inhibitors. In this present study, a modeling strategy consisting of common feature pharmacophore modeling, receptor-ligand pharmacophore modeling, and molecular dynamics simulation was utilized to identify the key pharmacodynamic characteristic elements of the target compound and the key residue information of the PI3Kγ interaction with the inhibitors. Then, 10 molecules were designed based on the structure-activity relationships, and some of them exhibited satisfactory predicted binding affinities to PI3Kγ. Finally, a hierarchical multistage virtual screening method, involving the developed common feature and receptor-ligand pharmacophore model and molecular docking, was constructed for screening the potential PI3Kγ inhibitors. Overall, we hope these findings would provide some guidance for the development of novel PI3Kγ inhibitors.

The bacteria Mycobacterium tuberculosis is responsible for the infectious disease Tuberculosis. Targeting the tubercule bacteria is an important challenge in developing the antimycobacterials. The glyoxylate cycle is considered as a potential target for the development of anti-tuberculosis agents, due to its absence in the humans. Humans only possess tricarboxylic acid cycle, while this cycle gets connected to glyoxylate cycle in microbes. Glyoxylate cycle is essential to the Mycobacterium for its growth and survival. Due to this reason, it is considered as a potential therapeutic target for the development of anti-tuberculosis agents. Here, we explore the effect on the behavior of the tricarboxylic acid cycle, glyoxylate cycle and their integrated pathway with the bioenergetics of the Mycobacterium, under the inhibition of key glyoxylate cycle enzymes using Continuous Petri net. Continuous Petri net is a special Petri net used to perform the quantitative analysis of the networks. We first study the tricarboxylic acid cycle and glyoxylate cycle of the tubercule bacteria by simulating its Continuous Petri net model under different scenarios. Both the cycles are then integrated with the bioenergetics of the bacteria and the integrated pathway is again simulated under different conditions. The simulation graphs show the metabolic consequences of inhibiting the key glyoxylate cycle enzymes and adding the uncouplers on the individual as well as integrated pathway. The uncouplers that inhibit the synthesis of adenosine triphosphate, play an important role as anti-mycobacterials. The simulation study done here validates the proposed Continuous Petri net model as compared with the experimental outcomes and also explains the consequences of the enzyme inhibition on the biochemical reactions involved in the metabolic pathways of the mycobacterium.

A novel series of pyrimidine derivatives, bearing modified benzimidazoles at N-1 position, has been designed, synthesized and screened as NNRTIs against HIV and as broad-spectrum antiviral agents. The molecules were screened against different HIV targets using molecular docking experiment. The docking results indicated that the molecules interacted well with the residues Lys101, Tyr181, Tyr188, Trp229, Phe227 and Tyr318 present in NNIBP of HIV-RT protein, formed quite stable complexes and, thus, behaved as probable NNRTIs. Among these compounds, 2b and 4b showed anti-HIV activity with IC50 values as 6.65 µg/mL (SI = 15.50) and 15.82 µg/mL (SI = 14.26), respectively. Similarly, compound 1a showed inhibitory property against coxsackie virus B4 and compound 3b against different viruses. Molecular dynamics simulation results unequivocally demonstrated the higher stability of the complex HIV-RT:2b than the HIV-RT:nevirapine complex. The MM/PBSA-based binding free energy (-) 114.92 kJ/mol of HIV-RT:2b complex in comparison to that of HIV-RT:nevirapine complex (-) 88.33 kJ/mol, further demonstrated the higher binding strength of 2b and thus, established the potential of compound 2b as a lead molecule as an HIV-RT inhibitor.

Osmolytes play an important role in cellular physiology by modulating the properties of proteins, including their molecular specificity. EcoRI is a model restriction enzyme whose specificity to DNA is altered in the presence of osmolytes. Here, we investigate the effect of two different osmolytes, glycerol and DMSO, on the dynamics and hydration of the EcoRI enzyme using molecular dynamics simulations. Our results show that the osmolytes, alter the essential dynamics of EcoRI. Particularly, we observe that the dynamics of the arm region of EcoRI which is involved in DNA binding is significantly altered. In addition, conformational free energy analyses reveals that the osmolytes bring about a change in the landscape similar to that of EcoRI bound to cognate DNA. We further observe that the hydration of the enzyme for each of the osmolyte is different, indicating that the mechanism of action of each of these osmolytes could be different. Further analyses of interfacial water dynamics using rotational autocorrelation function reveals that while the protein surface contributes to a slower tumbling motion of water, osmolytes, additionally contribute to the slowing of the angular motion of the water molecules. Entropy analysis also corroborates with this finding. We also find that the slowed rotational motion of interfacial waters in the presence of osmolytes contributes to a slowed relaxation of the hydrogen bonds between the interfacial waters and the functionally important residues in the protein. Taken together, our results show that osmolytes alter the dynamics of the protein by altering the dynamics of water. This altered dynamics, mediated by the changes in the water dynamics and hydrogen bonds with functionally important residues, may contribute to the altered specificity of EcoRI in the presence of osmolytes.

Perhaps the most important question posed by brain research is: How the brain gives rise to the mind. To answer this question, we have primarily relied on the connectionist paradigm: The brain's entire knowledge and thinking skills are thought to be stored in the connections; and the mental operations are executed by network computations. I propose here an alternative paradigm: Our knowledge and skills are stored in metabotropic receptors (MRs) and the G protein-gated ion channels (GPGICs). Here, mental operations are assumed to be executed by the functions of MRs and GPGICs. As GPGICs have the capacity to close or open branches of dendritic trees and axon terminals, their states transiently re-route neural activity throughout the nervous system. First, MRs detect ligands that signal the need to activate GPGICs. Next, GPGICs transiently select a subnetwork within the brain. The process of selecting this new subnetwork is what constitutes a mental operation - be it in a form of directed attention, perception or making a decision. Synaptic connections and network computations play only a secondary role, supporting MRs and GPGICs. According to this new paradigm, the mind emerges within the brain as the function of MRs and GPGICs whose primary function is to continually select the pathways over which neural activity will be allowed to pass. It is argued that MRs and GPGICs solve the scaling problem of intelligence from which the connectionism paradigm suffers.

Accumulating evidence has shown that medicinal plants have been exploited for treatment purposes since time immemorial. Thus, this study investigated the mitigating potentials of the ligands; n-hexadecanoic acid, 9-octadecenoic acid and octadecanoic acid from Copaifera salikounda seed pond extract reported to have antidiabetic potentials in our previous study using computational techniques. Fatty acid-binding protein 4 (FABP4) and peroxisome proliferator-activated receptor alpha (PPARα) were identified as potential receptors. Both molecular docking and Estimated ΔGbind revealed that each ligand exhibited high binding affinity to the respective proteins; this is quite sufficient to be termed favourable. A critical examination of the type and the nature of binding interactions and energy contributions have identified Arg106, Arg126 and Tyr128 in FABP4 and Gln277, Ser280, Tyr314, His440 and Tyr464 in PPARα as consistently being responsible for the binding interactions and stabilizations of each ligand to the individual proteins. The establishment of hydrogen bonding type of interaction and activity between the carboxylic acid moieties of these ligands and these crucial/unique residues goes further to buttress our assertion. A general study of the conformational state of these protein via RMSF and PCA plots goes further validate the observed structural trends wherein the presence of ligands induced seemly structural rigidity. In depth structural stability investigations went further to reveal that the 3D structures of these protein didn't deviate from it known native conformational stable state when bound with these ligands. Our findings indicate that the ligands have considerable inhibitory action against FABP4 and PPARα corroborating the reported antidiabetic potential of the extract.

Quantitative structure activity relationship (QSAR) studies on pyrrolidine derivatives have been established using CoMFA, CoMSIA, and Hologram QSAR analysis to estimate the values (pIC50) of gelatinase inhibitors. When the CoMFA cross-validation value, Q², was 0.625, the training set coefficient of determination, R² was 0.981. In CoMSIA, Q² was 0.749 and R² was 0.988. In the HQSAR, Q² was 0.84 and R² was 0.946. Visualization of these models was performed by contour maps showing favorable and unfavorable regions for activity, while visualization of HQSAR model was performed by a colored atomic contribution graph. Based on the results obtained of external validation, the CoMSIA model was statistically more significant and robust and was selected as the best model to predict new, more active inhibitors. To study the modes of interactions of the predicted compounds in the active site of MMP-2 and MMP-9, a simulation of molecular docking was realized. A combined study of MD simulations and calculation of free binding energy, were also carried out to validate the results obtained on the best predicted and most active compound in dataset and the compound NNGH as control compound. The results confirm the molecular docking results and indicate that the predicted ligands were stable in the binding site of MMP-2 and MMP-9.

In this study, new thieno[2,3-d]pyrimidine derivatives that could have potential anticancer activity by inhibiting the VEGFR-2 receptor have been designed, synthesized, and investigated. The thieno[2,3-d]pyrimidine derivatives showed strong in vitro abilities to inhibit VEGFR-2 and to prevent cancer cell growth in two different types of cancer cells, MCF-7 and HepG2. Particularly, compound 22 showed the most potent anti-VEGFR-2 activity with an IC50 value of 0.58 µM. Additionally, compound 22 exhibited good anti-proliferative activity against both MCF-7 and HepG2 cancer cell lines, with IC50 values of 11.32 ± 0.32 and 16.66 ± 1.22 µM, respectively. Further investigations revealed that compound 22 induced cell cycle arrest at the G2/M phase and promoted both early and late apoptosis in the MCF-7 cancer cells. Compound 22 also increased the level of BAX (2.8-fold), and reduced the level of Bcl-2 (2.2-fold), hence increasing the rate of apoptosis. Compound 22 also revealed 2.9-fold and 2.8-fold higher levels of caspase-8 and caspase-9, respectively, in the treated MCF-7 cancer cells compared to the control cell lines. The MD simulations showed that the VEGFR-2-22 complex was structurally and energytically stable over 100 ns, while the MM-GBSA study indicated its stable thermodynamic behavior. The bi-dimensional projection analysis confirmed the proper binding of the VEGFR-2-22 complex, while the DFT studies provided optimized geometry, charge distribution, FMO, ESP, the total density of state, and QTAIM maps of compound 22. Finally, computational ADMET studies were performed to assess the drug development potential of the thieno[2,3-d]pyrimidine derivatives. Overall, this study suggests that compound 22 has the potential as an anticancer lead compound by inhibiting VEGFR-2, which may be a guide for future drug design and development.

Valinomycin is a potent ionophore known for its ability to transport potassium ions across biological membranes. The study focuses on the hydroxylated analogues of valinomycin (HyVLMs) and compares their energy profiles and capabilities for transporting potassium ions across phospholipid membranes. Using metadynamics, we investigated the energy profiles of wildtype valinomycin (VLM_1) and its three hydroxylated analogues (VLM_2, VLM_3, and VLM_4). We observed that all analogues exhibited energy maxima in the centre of the membrane and preferred positions below the phospholipid heads. Furthermore, the entry barriers for membrane penetration were similar among the analogues, suggesting that the hydroxyl group did not significantly affect their passage through the membrane. Transition state calculations provided insights into the ability of valinomycin analogues to capture potassium ions, with VLM_4 showing the lowest activation energy and VLM_2 displaying the highest. Our findings contribute to understanding the mechanisms of potassium transport by valinomycin analogues and highlight their potential as ionophores. The presence of the hydroxyl group is of particular importance because it paves the way for subsequent chemical modifications and the synthesis of new antiviral agents with reduced intrinsic toxicity.

The incidence and mortality rate of breast cancer increases yearly by an average of 1.44 % and 0.23 %, respectively. Till 2021, there were 7.8 million women who had been diagnosed with breast cancer within 5 years. Biopsies of tumors are often expensive and invasive and raise the risk of serious complications like infection, excessive bleeding, and puncture damage to nearby tissues and organs. Early detection biomarkers are often variably expressed in different patients and may even be below the detection level at an early stage. Hence PBMC that shows alteration in gene profile as a result of interaction with tumor antigens may serve as a better early detection biomarker. Also, such alterations in immune gene profile in PBMCs are more prone to detection despite variability in different breast cancer mutants.This study aimed to identify potential diagnostic biomarkers for breast cancer using eXplainable Artificial Intelligence (XAI) on XGBoost machine learning (ML) models trained on a binary classification dataset containing the expression data of PBMCs from 252 breast cancer patients and 194 healthy women.After effectively adding SHAP values further into the XGBoost model, ten important genes related to breast cancer development were discovered to be effective potential biomarkers. Our studies showed that SVIP, BEND3, MDGA2, LEF1-AS1, PRM1, TEX14, MZB1, TMIGD2, KIT, and FKBP7 are key genes that impact model prediction. These genes may serve as early, non-invasive diagnostic and prognostic biomarkers for breast cancer patients.

Immunotherapy development against colorectal cancer (CRC) is hindered by the lack of cell surface target highly expressed in cancer cells but with restricted presence in normal tissues to minimize off-tumor toxicities. In this in silico analysis, a longlist of genes (n = 13,488) expressed in CRCs according to the Human Protein Atlas (HPA) database were evaluated to shortlist for potential surface targets based on the following prerequisites: (i) Absent from the brain and lung tissues to minimize the likelihood of neurologic and pulmonary toxicities; (ii) Restricted expression profile in other normal human tissues; (iii) Genes that potentially encode cell surface proteins and; (iv) At least moderately expressed in CRC cases. Fifteen potential targets were shortlisted and subsequently ranked according to the combination of their transcript and protein expression levels in CRCs derived from multiple datasets (i.e. DepMap, TCGA, CPTAC-2, and HPA CRCs). The top-ranked target with the highest and homogenous expression in CRCs was cadherin 17 (CDH17). Downstream analysis of CRC transcriptomics and proteomics datasets showed that CDH17 was significantly correlated with carcinoembryonic antigen expression. Moreover, CDH17 expression was significantly lower in CRC cases with high microsatellite instability, as well as negatively associated with immune response gene sets and the expression of MHC class I and II molecules. CDH17 represents an optimal target for therapeutic development against CRCs, and this study provides a novel framework to identify key cell surface targets for therapeutic development against other malignancies.

The rapid development of multi-drug resistant (MDR) pathogens adds urgency to search for novel and safe drugs having promising action on new and re-emerging infectious pathogens. Serratia marcescens is an MDR pathogen that causes several-healthcare associated infections. Curbing bacterial virulence, rather than inhibiting its growth, is a promising strategy to diminish the pathogenesis of infectious bacteria, reduce the development of antimicrobial resistance, and boost the host immune power to eradicate infections. Bergamot essential oil (BEO) is a remarkable source of promising therapeutics against pathogens. Therefore, the present investigation aimed to analyze the major phytocompounds from BEO against S. marcescens virulent proteins using in silico studies. The analysis of BEO phytocompounds was achieved by Gas chromatography-mass spectrometry (GC-MS) method. The molecular docking was carried out using the SP and XP docking protocol of the Glide program. The drug-likeness and pharmacokinetics properties (ADMET properties) were analyzed with SwissADME and pkCSM server. The results revealed that the major compounds present in BEO are Linalool (8.17%), D-Limonene (21.26%), and Linalyl acetate (26.91%). Molecular docking analysis revealed that these compounds docked strongly within the binding cavities of Serratia protease and FabI model which in turn curb the pathogenesis of this bacteria. Linalool interacted with the Serratia protease and FabI with a binding energy of - 3.130 kcal/mol and - 3.939 kcal/mol, respectively. Based on the pharmacokinetics findings all lead BEO phytocompounds appear to be promising drug candidates. Overall, these results represent a significant step in the development of plant-based compounds as a promising inhibitor of the virulent proteins of the MDR S. marcescens.

The discovery of novel chemotherapeutic agents is always challenging for researchers in industry and academia. Among the recent promising anticancer therapeutic targets, an important modulatory factor in mitosis is the expression of the kinesin family motor protein (Eg5). In terms of chemotherapy treatment, mitosis has gained significant attention due to its role as one of the biological processes that can be intervened in it. This study was undertaken to design, synthesise and evaluation of 4-aminoquinoline hybrid compounds as potential Eg5 inhibitors. Based on data collected from Malachite green and steady state ATPase assays, it has been determined that compounds such as 6c, 6d, 6g, and 6h are sensitive to Eg5 inhibition. In special mention, compounds 4 and 6c showed promising inhibitory activity in Malachite green assay with IC50 values of 2.32 ± 0.23 µM and 1.97 ± 0.23 µM respectively. Compound 4 showed favourable inhibitory potential Steady state ATPase Assay with IC50 value of 5.39 ± 1.39 µM. We performed molecular docking, MM/GBSA calculations, and molecular dynamic simulations to evaluate the interactions between ligands and the binding site of the kinesin spindle protein to evaluate the functional consequences of these interactions. As a result of these findings, it can be concluded that these 4-amioquinoline Schiff's base hybrids may prove to be promising candidates for development as novel inhibitors of Eg5. Further in-vivo research in this area is required.

Driving fatigue detection based on EEG signals is a research hotspot in applying brain-computer interfaces. EEG signal is complex, unstable, and nonlinear. Most existing methods rarely analyze the data characteristics from multiple dimensions, so it takes work to analyze the data comprehensively. To analyze EEG signals more comprehensively, this paper evaluates a feature extraction strategy of EEG data based on differential entropy (DE). This method combines the characteristics of different frequency bands, extracts the frequency domain characteristics of EEG, and retains the spatial information between channels. This paper proposes a multi-feature fusion network (T-A-MFFNet) based on the time domain and attention network. The model is composed of a time domain network (TNet), channel attention network (CANet), spatial attention network (SANet), and multi-feature fusion network(MFFNet) based on a squeeze network. T-A-MFFNet aims to learn more valuable features from the input data to achieve good classification results. Specifically, the TNet network extracts high-level time series information from EEG data. CANet and SANet are used to fuse channel and spatial features. They use MFFNet to merge multi-dimensional features and realize classification. The validity of the model is verified on the SEED-VIG dataset. The experimental results show that the accuracy of the proposed method reaches 85.65 %, which is superior to the current popular model. The proposed method can learn more valuable information from EEG signals to improve the ability to identify fatigue status and promote the development of the research field of driving fatigue detection based on EEG signals.

Herien, three new Fe(III) (FeAZD), Ni(II) (NiAZD), and Cu(II) (CuAZD) complexes were synthesized and characterized using various physicochemical and spectroscopic approaches. The H2AZD ligand acted as a bi-basic bi-dentate NO ligand in a 1:1 molar ratio. The results revealed that the FeAZD and CuAZD complexes had octahedral geometry, while the NiAZD had a tetrahedral geometry. The optimized geometry, HOMO and LUMO analysis of the ligand and its metal complexes was determined via Density functional theory (DFT) using the B3LYP with 6-311 G(d,p), and LanL2DZ level of theory. The FeAZD, NiAZD and CuAZD had lower energy gap, 7.40, 7.93 and 7.06 eV, respectively, than the free ligand (9.58 eV), which proposed that CuAZD was more active one. The free ligand and its metal complexes were in vitro investigated for their antibacterial and antifungal activity. The results illustrated that the metal complexes had higher antibacterial and antifungal activity than the free ligand. More specifically, the CuAZD demonstrated good antibacterial activity against E. coli, P. aeruginosa, S. aureus, B. cereus, and A. flavus, T. rubrum, and C. albicans, with activity indexes of 72.22%, 65.01%, 77.78%, and 72.22%, 63.16%, 59.09%, and 61.90%, respectively. Also, the metal complexes showed lower MIC (6.25-3.125 ppm) compared to the free ligand (about 50 ppm). Finally, molecular docking was utilized to investigate the ability of the free ligand and its metal complexes to inhibit the growth of E. coli (PDB ID: 5iq9). The results showed that the CuAZD had the highest binding affinity to the receptor, with a more negative docking score of - 7.05 Kcal/mol, and lower inhibition constant (Ki) of 6.90 µM. That is indicating that it may be the most effective at inhibiting the growth of E. coli (PDB ID: 5iq9).

Coronavirus disease is caused by the pathogen severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) known as COVID-19. COVID-19 has caused the deaths of 6,541,936 people worldwide as of September 27th, 2022. SARS-CoV-2 severity is determined by a cytokine storm condition, in which the innate immune system creates an unregulated and excessive production of pro-inflammatory such IL-1, IL-6, NF Kappa B, and TNF alpha signaling molecules known as cytokines. The patient died due to respiratory organ failure and an acute complication because of the hyper-inflammation phenomenon. Green tea, soybean, and guava bioactive substances are well-known to act as anti-inflammation, and antioxidants become prospective COVID-19 illness candidates to overcome the cytokine storm. Our research aims to discover the bioactivity, bioavailability, and protein targets of green tea, soybean, and guava bioactive compounds as anti-inflammatory agents via the TNF inhibition pathway. The experiment uses in silico methods and harnesses the accessible datasets. Samples of 3D structure and SMILE identity of bioactive compounds were retrieved from the KNApSAck and Dr Duke databases. The QSAR analysis was done by WAY2DRUG web server, while the ADME prediction was performed using SWISSADME web server, following the Lipinsky rules of drugs. The target protein and protein-protein interaction were analyzed using STRING DB and Cytoscape software. Lastly, molecular docking was performed using Autodock 4.2 and visualization with BioVia Discovery Studio 2019. The identified study showed the potential of green tea, soybean, and guava's bioactive compounds have played an important role as anti-inflammation agents through TNF inhibitor pathway.

The optimized structures and theoretical studies of metal based anticancer drugs Nedaplatin (cis-Diammine glycolato platinum) (CDGP) and Oxaliplatin (Diaminocyclohexane oxalatoplatinum) (DCOP) structures by Density Functional Theory (DFT) method at the B3LYP level with LANL2DZ was applied to investigate the spectroscopic, structural optical properties, conducting properties for the chosen materials, Band Gap was predicted with the help of HOMO-LUMO values. From the calculated parameters of the title compounds CDGP and DCOP shows DCOP found to be highly reactive metal complexed anticancer drug over CDGP. On calculating the site of the lowest binding energy a receptor active site with ligand is done by Ligand-protein docking process. In this study, the difference in the coordinates of ligands and the binding (intermolecular) energy called as Root Mean Square Deviation (RMSD) estimated to estimate the drug-DNA interactions. Molecular docking is the broadly used screening studies in the visualizing of Drug-DNA interaction at an level of atom and drug designing based on structure. Chosen anticancer drugs with their binding affinities to the selected DNA and Drugs potential anticancer behavior were examined, Out of 2 drug compounds screened, CDGP and DCOP, DCOP have shown least binding energies as -6.88 kcal/mol than CDGP as -5.64 kcal/mol and the interactive studies of drug compounds conformations with RMSD values in accordance to crystal structures is less than or equal to 2.00 Å in focusing on the complete DNA structure (large grid box) done using the Autodock software.The compound with most least binding energy value with low inhibition constant found to have good binding affinity towards the DNA structure and the structural properties studied using DFT also shows good chemical reactivity in accordance with the Docking studies.From the results obtained DCOP found to have good chemical descriptors with Good binding affinity than CDGP.Thus DCOP is the best suited for biological molecular target.

Herbal medicines are multi-component and can exhibit synergistic effects in the treatment of diseases. Sechium edule, Syzigium polyanthum, and Curcuma xanthorrhiza have been used in traditional medicine to reduce serum lipid levels. However, the molecular mechanism was not described clearly, especially as a mixture. Thus, we performed a network pharmacology study combined with molecular docking to find a rational explanation regarding the molecular mechanisms of this antihyperlipidemic formula. According to the network pharmacology study, we predicted that this extract mixture would act as an antihyperlipidemic agent by modulating several pathways including insulin resistance, endocrine resistance, and AMP-activated protein kinase (AMPK) signaling pathway. Based on the topology parameters, we identified 6 significant targets that play an important role in reducing lipid serum levels: HMG-CoA reductase (HMGCR), peroxisome proliferator-activated receptor alpha (PPARA), RAC-alpha serine/threonine-protein kinase (AKT1), epidermal growth factor receptor (EGFR), matrix metalloproteinase-9 (MMP9), and tumor necrosis factor-alpha (TNF). Meanwhile, 8 compounds: β-sitosterol, bisdesmethoxycurcumin, cucurbitacin D, cucurbitacin E, myricetin, phloretin, quercitrin, and rutin were the compounds with a high degree, indicating that these compounds have a multitarget effect. Our consensus docking study revealed that HMGCR was the only protein targeted by all potential compounds, and rutin was the compound with the best consensus docking score for almost all targets. The in vitro study revealed that the extract combination could inhibit HMGCR with an IC50 value of 74.26 µg/mL, indicating that HMGCR inhibition is one of its antihyperlipidemic mechanisms.

Functional interaction of Ras signaling proteins with upstream, negative regulatory GTPase activating proteins (GAPs) represents a crucial step in cellular decision making related to growth and survival. Key components of the catalytic transition state for Ras deactivation by GAP-accelerated hydrolysis of Ras-bound guanosine triphosphate (GTP) are thought to include an arginine residue from the GAP (the arginine finger), a glutamine residue from Ras (Q61), and a water molecule that is likely coordinated by Q61 to engage in nucleophilic attack on GTP. Here, we use in-vitro fluorescence experiments to show that 0.1-100 mM concentrations of free arginine, imidazole, and other small nitrogenous molecule fail to accelerate GTP hydrolysis, even in the presence of the catalytic domain of a mutant GAP lacking its arginine finger (R1276A NF1). This result is surprising given that imidazole can chemically rescue enzyme activity in arginine-to-alanine mutant protein tyrosine kinases (PTKs) that share many active site components with Ras/GAP complexes. Complementary all-atom molecular dynamics (MD) simulations reveal that an arginine finger GAP mutant still functions to enhance Ras Q61-GTP interaction, though less extensively than wild-type GAP. This increased Q61-GTP proximity may promote more frequent fluctuations into configurations that enable GTP hydrolysis as a component of the mechanism by which GAPs accelerate Ras deactivation in the face of arginine finger mutations. The failure of small molecule analogs of arginine to chemically rescue catalytic deactivation of Ras is consistent with the idea that the influence of the GAP goes beyond the simple provision of its arginine finger. However, the failure of chemical rescue in the presence of R1276A NF1 suggests that the GAPs arginine finger is either unsusceptible to rescue due to exquisite positioning or that it is involved in complex multivalent interactions. Therefore, in the context of oncogenic Ras proteins with mutations at codons 12 or 13 that inhibit arginine finger penetration toward GTP, drug-based chemical rescue of GTP hydrolysis may have bifunctional chemical/geometric requirements that are more difficult to satisfy than those that result from arginine-to-alanine mutations in other enzymes for which chemical rescue has been demonstrated.

Regeneration is a homeostatic process that involves the restoration of cells and body parts. Most of the molecular mechanisms and signalling pathways involved in wound healing, such as proliferation, have also been associated with cancer cell growth, suggesting that cancer is an over/unhealed wound. In this study, we examined differentially expressed genes in spinal cord samples from regenerative organisms (axolotl and zebrafish) and nonregenerative organisms (mouse and rat) compared to intact control spinal cord samples using publicly available transcriptomics data and bioinformatics analyses. Based on these gene signatures, we investigated 3 small compounds, namely cucurbitacin I, BMS-754807, and PHA-793887 as potential candidates for the treatment of cancer. The predicted target genes of the repositioned compounds were mainly enriched with the greatest number of genes in cancer pathways. The molecular docking results on the binding affinity between the repositioned compounds and their target genes are also reported. The repositioned 3 small compounds showed anticancer effect both in 2D and 3D cell cultures using the prostate cancer cell line as a model. We propose cucurbitacin I, BMS-754807, and PHA-793887 as potential anticancer drug candidates. Future studies on the mechanisms associated with the revealed gene signatures and anticancer effects of these three small compunds would allow scientists to develop therapeutic approaches to combat cancer. This research contributes to the evaluation of mechanisms and gene signatures that either limit or cause cancer, and to the development of new cancer therapies by establishing a link between regeneration and carcinogenesis.

Oximes are the promising structural scaffold for inhibiting monoamine oxidase (MAO)-B. Eight chalcone-based oxime derivatives were synthesized by microwave-assisted technique, and their ability to inhibit human MAO (hMAO) enzymes were tested. All compounds showed higher inhibitory activity of hMAO-B than hMAO-A. In the CHBO subseries, CHBO4 most potently inhibited hMAO-B with an IC50 value of 0.031 μM, followed by CHBO3 (IC50 = 0.075 μM). In the CHFO subseries, CHFO4 showed the highest inhibition of hMAO-B with an IC50 value of 0.147 μM. Compound CHBO4 had the highest selectivity index (SI) value of 1290.3. However, CHBO3 and CHFO4 showed relatively low SI values of 27.7 and 19.2, respectively. The -Br substituent in the CHBO subseries at the para-position in the B-ring showed higher hMAO-B inhibition than the -F substituent in the CHFO subseries. In both series, hMAO-B inhibition increased with the substituents at para-position in A-ring (-F > -Br > -Cl > -H in order). Compound CHBO4 (-F in A-ring and -Br in B-ring) was 12.6-times potent than the substituents-reversed compound CHFO3 (-Br in A-ring and -F in B-ring; IC50 = 0.391 μM). In the kinetic study, Ki values of CHBO4 and CHFO4 for hMAO-B were 0.010 ± 0.005 and 0.040 ± 0.007 μM, respectively, with competitive inhibitions. Reversibility experiments showed that CHBO4 and CHFO4 were reversible hMAO-B inhibitors. In the cytotoxicity test using the Vero cells by the MTT technique, CHBO4 had low toxicity with an IC50 value of 128.8 µg/mL. In H2O2-induced cells, CHBO4 significantly reduced cell damage by scavenging reactive oxygen species (ROS). Molecular docking and dynamics showed the stable binding mode of the lead molecule CHBO4 on the active site of hMAO-B. These results suggest that CHBO4 is a potent reversible, competitive, and selective hMAO-B inhibitor and can be used as a treatment agent for neurological disorders.

The growing accessibility of large-scale protein interaction data demands extensive research to understand cell organization and its functioning at the network level. Bioinformatics and data mining researchers have extensively studied network clustering to examine the structural and operational features of protein protein interaction (PPI) networks. Clustering PPI networks has proven useful in numerous research over the past two decades for identifying functional modules, understanding the roles of previously unknown proteins, and other purposes. Protein complexes represent one of the essential cellular components for creating biological activities. Inferring protein complexes has been made more accessible by experimental approaches. We offer a novel method that integrates the classification model with local topological data, making it more reliable and efficient. This article describes a decision tree classifier based on topological characteristics of the subgraph for mining protein complexes. The proposed graph-based algorithm is an effective and efficient way to identify protein complexes from large-scale PPI networks. The performance of the proposed algorithm is observed in protein-protein interaction networks of yeast and human in the Database of Interacting Proteins (DIP) and the Biological General Repository for Interaction Datasets (BioGRID) using widely accepted benchmark protein complexes from the comprehensive resource of mammalian protein complexes (CORUM) and the comprehensive catalogue of yeast protein complexes (CYC2008). The outcomes demonstrate that our method can outperform the best-performing supervised, semi-supervised, and unsupervised approaches to detecting protein complexes.

OBJECTIVE: Integrin subunit beta 4 (ITGB4), a receptor for laminins, was an oncoprotein in several malignancies. However, its clinical role in oral squamous cell carcinoma (OSCC) remains unclear. MATERIALS AND METHODS: Firstly, 99 OSCC and 13 normal oral epithelium samples were employed for immunohistochemistry (IHC) for detecting the expression level of ITGB4 protein in OSCC. Subsequently, 971 OSCC and 281 non-cancerous specimens from RNA-seq and 18 microarrays were applied for investigating the expression of ITGB4 mRNA. Furthermore, to explore the potential mechanism of ITGB4 in OSCC, the co-expressed genes of ITGB4 were initially screened using all available datasets, and were further utilized for the gene enrichment analysis. RESULTS: First, IHC showed a distinctively higher expression level of the ITGB4 protein in the OSCC group than that in the normal controls. Second, expression profile from RNA-seq and microarrays reflected that ITGB4 mRNA was dramatically overexpressed in OSCC tissues compared with non-tumor tissues. Third, standardized mean difference (SMD) with the area under the summary receiver operating characteristic (sROC) curve combining all incorporated data revealed that ITGB4 was consistently significantly upregulated in OSCC tissues, with the SMD value being 1.31 and the area under the sROC curve being 0.82. Lastly, 184 upregulated and 179 downregulated co-expressed genes of ITGB4 were utilized for enrichment analysis, which demonstrated that ITGB4 might influence the pathogenesis of OSCC through cell cycle, ECM-receptor interaction and focal adhesion pathways. CONCLUSIONS: ITGB4 might play a pivotal role in the tumorigenesis and progression of OSCC, making it a promising biomarker of OSCC.

We have developed a Triangular Spatial Relationship (TSR)-based computational method for protein structure comparison and motif discovery that is both sequence and structure alignment-free. A protein 3D structure is modeled by all possible triangles that are constructed with every three Cα atoms of amino acids as vertices. Every triangle is represented using an integer (a key). The keys are calculated by a rule-based formula which is a function of a representative length, a representative angle, and the vertex labels associated with amino acids. A 3D structure is thereby represented by a vector of integers (TSR keys). Global or local structure comparisons are achieved by computing all keys or a set of keys, respectively. Many enzymatic reactions and notable marketed drugs are highly stereospecific. Thus, in this paper, we propose a modified key calculation formula by including a mechanism for discriminating mirror-image keys to capture stereo geometry. We assign a positive or a negative sign to the integers representing mirror-image keys. Applying the new key calculation function provides the ability to further discriminate mirror-image keys that were previously considered identical. As the result, applying the mirror-image discrimination capability (i) significantly increases the number of distinct keys; (ii) decreases the number of common keys; (iii) decreases structural similarity; (iv) increases the opportunity to identify specific keys for each type of the receptors. The specific keys identified in this study for the cases of without (not applying) and with (applying) mirror-image discrimination can be considered as the structure signatures that exclusively belong to a certain type of receptors. Applying mirror-image discrimination introduces stereospecificity to keys for allowing more precise modeling of ligand - target interactions. The development of mirror-image TSR keys of Cα atom, in conjunction with the integration of Cα TSR keys with all-atom TSR keys for amino acids and drugs, will lead to a new and promising computational method for aiding drug design and discovery.

Glucose-6-phosphate dehydrogenase (G6PD) deficiency is the most common human enzyme defect that affects more than 500 million people worldwide. Individuals affected with G6PD deficiency may occasionally suffer mild-to-severe chronic hemolytic anemia. Chronic non-spherocytic hemolytic anemia (CNSHA) is a potential result of the Class I G6PD variants. This comparative computational study attempted to correct the defect in variants structure by docking the AG1 molecule to selected Class I G6PD variants [G6PDNashville (Arg393His), G6PDAlhambra (Val394Leu), and G6PDDurham (Lys238Arg)] at the dimer interface and structural NADP+ binding site. It was followed by an analysis of the enzyme conformations before and after binding to the AG1 molecule using the molecular dynamics simulation (MDS) approach, while the severity of CNSHA was determined via root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), hydrogen bonds, salt bridges, radius of gyration (Rg), solvent accessible surface area analysis (SASA), and principal component analysis (PCA). The results revealed that G6PDNashville (Arg393His) and G6PDDurham (Lys238Arg) had lost the direct contact with structural NADP+ and salt bridges at Glu419 - Arg427 and Glu206 - Lys407 were disrupted in all selected variants. Furthermore, the AG1 molecule re-stabilized the enzyme structure by restoring the missing interactions. Bioinformatics approaches were also used to conduct a detailed structural analysis of the G6PD enzyme at a molecular level to understand the implications of these variants toward enzyme function. Our findings suggest that despite the lack of treatment for G6PDD to date, AG1 remains a novel molecule that promotes activation in a variety of G6PD variants.

MicroRNAs (miRNAs) are involved in the regulation of various cellular processes including pathological conditions. MiRNA networks have been extensively researched in age-related degenerative diseases, such as cancer, Alzheimer's disease (AD), and heart failure. Thus, miRNA has been studied from different approaches, in vivo, in vitro, and in silico including miRNA networks. Networks linking diverse biomedical entities unveil information not readily observable by other means. This work focuses on biological networks related to Breast cancer susceptibility 1 (BRCA1) in AD and breast cancer (BC). Using various bioinformatics approaches, we identified subnetworks common to AD and BC that suggest they are linked. According to our results, miR-107 was identified as a potentially good candidate for both AD and BC treatment (targeting BRCA1/2 and PTEN in both diseases), accompanied by miR-146a and miR-17. The analysis also confirmed the involvement of the miR-17-92 cluster, and miR-124-3p, and highlighted the importance of poorly researched miRNAs such as mir-6785 mir-6127, mir-6870, or miR-8485. After filtering the in silico analysis results, we found 49 miRNA molecules that modulate the expression of at least five genes common to both BC and AD. Those 49 miRNAs regulate the expression of 122 genes in AD and 93 genes in BC, from which 26 genes are common genes for AD and BC involved in neuron differentiation and genesis, cell differentiation and migration, regulation of cell cycle, and cancer development. Additionally, the highly enriched pathway was associated with diabetic complications, pointing out possible interplay among molecules underlying BC, AD, and diabetes pathology.

The process of steroidogenesis plays a vital role in human physiology as it governs the biosynthesis of mineralocorticoids, glucocorticoids, and androgens. These three classes of steroid hormones are primarily produced in the adrenal and gonadal glands through steroidogenesis pathways. Initiated by the side chain cleavage of cholesterol (CLR), this process leads to the conversion of cholesterol into pregnenolone and isocaproic aldehyde. The enzyme CYP11A1, encoded by the CYP11A1 gene, plays a key role in catalyzing the side chain cleavage of CLR. Several single nucleotide polymorphisms (SNPs) have been identified in the CYP11A1 gene, which may predispose carriers to disorders associated with abnormal steroidogenesis. Specifically, missense SNPs in the CYP11A1 gene have the potential to negatively impact the interaction between CYP11A1 and CLR, thus affecting the overall metabolome of steroid hormones. In this computational study, we focused on a specific set of missense SNPs reported in the CYP11A1 gene, aiming to identify variants that directly impact the interaction between CYP11A1 and CLR. The three-dimensional structure of the CYP11A1-CLR complex was obtained from the RCSB Protein Data Bank, while missense SNPs in the CYP11A1 gene were retrieved from Ensembl. To predict the most deleterious variants, we utilized the ConSurf server, SIFT, and PolyPhen. Furthermore, we assessed the impact of induced amino acid (AA) substitutions on the CYP11A1-CLR interaction using the PRODIGY server, PyMol, and Ligplot programs. Additionally, molecular dynamics (MD) simulations were conducted to analyze the effects of deleterious variants on the structural dynamics of the CYP11A1-CLR complex. Among the 8096 retrieved variants, we identified ten missense SNPs (E91K, W147G, R151W, R151Q, S391C, V392M, Q395K, Q416E, R460W, and R460Q) as deleterious for the interaction between CYP11A1 and CLR. MD simulations of the CYP11A1-CLR complexes carrying these deleterious AA substitutions revealed that Q416E, W147G, R460Q, and R460W had the most pronounced impacts on the structural dynamics of the complex. Consequently, these missense SNPs were considered the most deleterious ones. Further functional tests are recommended to assess the impact of these four missense SNPs on the enzymatic activity of CYP11A1. Moreover, Genome-Wide Association Studies (GWAS) should be conducted to determine the significance of their association with abnormal steroidogenesis diseases in various patient groups.

Predicting the kinetics of reactions involving nucleic acid strands is a fundamental task in biology and biotechnology. Reaction kinetics can be modeled as an elementary step continuous-time Markov chain, where states correspond to secondary structures and transitions correspond to base pair formation and breakage. Since the number of states in the Markov chain could be large, rates are determined by estimating the mean first passage time from sampled trajectories. As a result, the cost of kinetic predictions becomes prohibitively expensive for rare events with extremely long trajectories. Also problematic are scenarios where multiple predictions are needed for the same reaction, e.g., under different environmental conditions, or when calibrating model parameters, because a new set of trajectories is needed multiple times. We propose a new method, called pathway elaboration, to handle these scenarios. Pathway elaboration builds a truncated continuous-time Markov chain through both biased and unbiased sampling. The resulting Markov chain has moderate state space size, so matrix methods can efficiently compute reaction rates, even for rare events. Also the transition rates of the truncated Markov chain can easily be adapted when model or environmental parameters are perturbed, making model calibration feasible. We illustrate the utility of pathway elaboration on toehold-mediated strand displacement reactions, show that it well-approximates trajectory-based predictions of unbiased elementary step models on a wide range of reaction types for which such predictions are feasible, and demonstrate that it performs better than alternative truncation-based approaches that are applicable for mean first passage time estimation. Finally, in a small study, we use pathway elaboration to optimize the Metropolis kinetic model of Multistrand, an elementary step simulator, showing that the optimized parameters greatly improve reaction rate predictions. Our framework and dataset are available at https://github.com/DNA-and-Natural-Algorithms-Group/PathwayElaboration.

Molecular modeling strategy was adopted to check the biological potential of the imine based molecules against free radical, acetylcholine esterase and butyrylcholine esterase. Three Schiff based compounds as (E)-2-(((4-bromophenyl)imino)methyl)-4-methylphenol (1), (E)-2-(((3-fluorophenyl)imino)methyl)-4-methylphenol (2) and (2E,2E)-2-(2-(2-hydroxy-5-methylbenzylidene)hydrazono)-1,2-diphenylethanone (3) were synthesized with high yield. The synthesized compounds were characterized with the help of modern techniques such as UV, FTIR and NMR while exact structure was depicted with Single Crystal X-Ray diffraction technique which disclosed that compound 1 is orthorhombic, while 2 and 3 are monoclinic. A hybrid functional (B3LYP) method with general basis set of 6-31 G(d,p) were applied to optimize synthesized Schiff bases. The contribution of in-between molecular contacts within a crystalline assembly of compounds were studied using Hirshfeld surface analysis (HS). In order to check the ability of the synthesized compounds toward free radical and enzyme inhibition, in vitro models were used to assess the radical scavenging and enzyme inhibition potential which depicted that compound 3 showed highest potential (57.43 ± 1.0%; DPPH, 75.09 ± 1.0%; AChE and 64.47 ± 1.0%; BChE). The ADMET assessments suggested the drug like properties of the synthesized compounds. It was concluded from results (in vitro and in silico) that synthesized compound have ability to cure the disorder related to free radical and enzyme inhibition. Compound 3 was shown to be the most active compared to other compounds.

In this study, the one-pot synthetic methodology for the preparation of substituted pyrroles with diethyl acetylene-dicarboxylate is reported for the various pyrrole derivatives via the Trifimow synthesis process from oximes. This method also offers the literature as a cyclization pathway using a ytterbium triflate catalyst. Another importance of this study is the use of pyrrole derivatives in pharmaceuticals, biological processes, and agrochemicals. From this point of view, the development of a new catalyst in synthetic organic chemistry and the difference in the method is also important. The syntheses of the target substituted pyrroles are accomplished in high yields. Also, all synthesized structures were confirmed by 1H NMR, 13C NMR, and IR spectra. The DFT computations were leveraged for structural and spectroscopic validation of the compounds. Then, FMO and NBO analyses were subsequently employed to elucidate the reactivity characteristics and intramolecular interactions within these compounds. Also, ADMET indices were ascertained to assess potential pharmacokinetic properties, drug-like qualities, and possible adverse effects of these compounds. Last, optimized molecules were analyzed by molecular docking methods against crystal structures of Bovine Serum Albumin and Leukemia Inhibitory Factor, and their binding affinities, interaction details, and inhibition constants were determined.

Advances in sequencing technology assisted biologists in revealing signatures of DNA cancer mutation process and in demonstrating the mutagenesis behind. However, most of these signatures proposed by majority of work focus only on the type and frequency of mutations, without considering spatial information which is non-negligible in exploring mechanisms of mutation occurrence, e.g., Kataegis. Statistical characterization of the distance between consecutive mutations can give us relative spatial information; however, it ignores location information which is as important as distance information. In this work, we assume that DNA cancer mutations are location-dependent and that integrating the two variables, location and inter-distance, is beneficial to study DNA cancer mutation processes more accurately. Particularly, instead of following a specific distribution over the whole DNA sequence, we found out that the distribution of distance between successive mutations alternates between exponential and power-law distributions. Apart from this, the parameters of either of the two distributions vary with DNA locations. The cancers with kataegis phenomenon, a specific mutation pattern caused by abnormal activity of APOBEC protein family, are more likely to be accompanied by higher parameter values of distance distribution, implying higher occurrence rate of mutation. Therefore, we propose non-homogeneous Poisson and Renewal processes to spatially model DNA cancer mutations and to describe mutation patterns quantitatively and more accurately through a statistical perspective.

The Longest Common Subsequence (LCS) is the problem of finding a subsequence among a set of strings that has two properties of being common to all and the longest. The LCS has applications in computational biology and text editing, among many others. Due to the NP-hardness of the general longest common subsequence, numerous heuristic algorithms and solvers have been proposed to give the best possible solution for different sets of strings. None of them has the best performance for all types of sets. In addition, there is no method to specify the type of a given set of strings. Besides that, the available hyper-heuristic is not efficient and fast enough to solve this problem in real-world applications. This paper proposes a novel hyper-heuristic to solve the longest common subsequence problem using a new criterion to classify a set of strings based on their similarity. To do this, we offer a general stochastic framework to identify the type of a given set of strings. Following that, we introduce the set similarity dichotomizer (S2D) algorithm based on the framework that divides the type of sets into two. This algorithm is introduced for the first time in this paper and opens a new way to go beyond the current LCS solvers. Then, we present our proposed hyper-heuristic that exploits the S2D and one of the internal properties of the given strings to choose the best matching heuristic among a set of heuristics. We compare the results on benchmark datasets with the best heuristics and hyper-heuristics. The results show that our proposed dichotomizer (i.e., S2D) can classify datasets with 98% of accuracy. Also, our proposed hyper-heuristic obtains competitive performance in comparison with the best methods and outperforms best hyper-heuristics for uncorrelated datasets in terms of both quality of solutions and run time factors. All supplementary files, including the source codes and datasets, are publicly available on GitHub.1.