Novel thiazolidine-2,4-dione derivatives, 11a-g, were designed, and synthesized targeting the VEGFR-2 protein. The in vitro studies indicated the abilities of the synthesized derivatives to inhibit VEGFR-2 and prevent the growth of two different cancer cell types, HepG2 and MCF-7. Compound 11 f exhibited the strongest anti-VEGFR-2 activity (IC50 = 0.053 µM). As well, compound 11 f showed impressive anti-proliferative activity against the mentioned cancer cell lines with IC50 values of 0.64 ± 0.01 and 0.53 ± 0.04 µM, respectively. Additionally, compound 11 f arrested the MCF-7 cell cycle at the S phase and increased the overall apoptosis percentage. Furthermore, cell migration assay revealed that compound 11 f has a significant ability to prevent migration and healing potentialities of MCF-7. Moreover, computational studies were used to conduct the molecular investigation of the VEGFR-2-11 f complex. The kinetic and structural features of the complex were examined using molecular dynamics simulations and molecular docking. Besides, Principal component analysis (PCA) was used to explain the dynamics of the VEGFR-2-11 f complex at various spatial scales. The DFT calculations also provided further clarity regarding compound 11 f's structural and electronic features. To evaluate how closely the developed compounds might look like drugs, ADMET and toxicity experiments were computed. To conclude, the presented study demonstrates the potential of compound 11 f as a viable anti-cancer drug, which can serve as a prototype for future structural modifications and further biological investigations.

A group of theobromine derivatives was designed based on the key pharmacophoric characteristics of VEGFR-2 inhibitors. HepG2 and MCF-7 cancer cell lines were used to test the obtained compounds for their in vitro anti-proliferative activities. Compound 15 (2-(3,7-Dimethyl-2,6-dioxo-2,3,6,7-tetrahydro-1H-purin-1-yl)-N-(4-(1-(2-(4-hydroxybenzoyl)hydrazono)ethyl) phenyl)acetamide) was the most potent cytotoxic member against MCF-7 (IC50 = 0.42 µM) and HepG2 (IC50 = 0.22 µM). The effectiveness of VEGFR-2 inhibition was assessed for compound 15, and its IC50 value was calculated to be 0.067 µM. Additional cellular mechanistic investigations showed that compound 15 dramatically increased the population of apoptotic HepG2 cells in both early and late apoptosis. The investigation of apoptotic markers confirmed that compound 15 upregulated the levels of BAX (2.26-fold) and downregulated the levels of Bcl-2 (4.4-fold). The molecular docking investigations, MM-GPSA, PLIP studies, and MD simulations validated the potential of compound 15 to be a VEGFR-2 inhibitor. DFT calculations have been completed to comprehend how the electrical charge is distributed within compound 15 and to predict how it would bond to VEGFR-2. Lastly, ADMET prediction showed that the designed members have drug-like characteristics and minimal levels of toxicity. In conclusion, our in vitro and in silico investigations showed that compound 15 exhibited promising apoptotic anticancer potential through the suppression of VEGFR-2.

Predicting protein stability change upon variation through a computational approach is a valuable tool to unveil the mechanisms of mutation-induced drug failure and develop immunotherapy strategies. Some previous machine learning-based techniques exhibit anti-symmetric bias toward destabilizing situations, whereas others struggle with generalization to unseen examples. To address these issues, we propose a gated graph neural network-based approach to predict changes in protein stability upon mutation. The model uses message passing to encode the links between the molecular structure and property after eliminating the non-mutant structure and creating input feature vectors. While doing so, it also incorporates the coordinates of the raw atoms to provide spatial insights into the chemical systems. We test the model on the Ssym, Myoglobin, Broom, and p53 datasets to demonstrate the generalization performance. Compared to existing approaches, our proposed method achieves improved linearity with symmetry in less time. The code for this study is available at: https://github.com/HongzhouTang/Pros-GNN.

Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB), remains a major threat to global health, with the emergence of multi-drug and extensively drug-resistant strains posing a serious challenge. Thereby, understanding the molecular basis of MTB virulence and disease pathogenesis is critical for developing effective therapeutic strategies. Targeting proteins involved in central metabolism has been recognized as a promising therapeutic approach to combat MTB. In this regard, the enzyme AckA of the acetate metabolic pathway which produces acetate from acetyl phosphate, is an important drug target for various pathogenic organisms. Therefore, this study aimed to identify potential AckA inhibitors through in silico methods, including molecular modeling, molecular dynamics simulation (MDS), and high-throughput virtual screening (HTVS) followed by ADMETox, MMGBSA, Density Functional Theory (DFT) calculations. HTVS of one million compounds from the ZINC database against AckA resulted in the top five hits (ZINC82048449, ZINC1219737510, ZINC1771921358, ZINC119699567, and ZINC1427100376) with better binding affinity and optimal binding free energy. MDS studies on complexes revealed that key residues, Asn195, Asp266, Phe267, Gly314, and Asn318 played a significant role in stable interactions of the top-ranked compounds to AckA. These outcomes provide insights into the optimal binding of the leads to inhibit the acetate pathway and aid in the rational design of novel therapeutic agents. Thus, the identified leads may act as promising compounds for targeting AckA and may serve as a potential therapeutic modality for treating TB. Our findings offer valuable insights into the inhibition of the acetate pathway, while also serving as a blueprint for rational drug design. The identified leads hold promise as compelling compounds for targeting AckA, thereby offering a potential therapeutic avenue for tackling TB. Thus, our study uncovers a pathway toward promising TB therapeutics by elucidating AckA inhibitors. By leveraging in silico methodologies, potent compounds that hold the potential to thwart AckA's role in MTB's acetate pathway have been unveiled. This breakthrough fosters optimism in the quest for novel and effective TB treatments, addressing a global health challenge with renewed vigor.

The coronavirus (COVID-19) has mutated into several variants, and evidence says that new variants are more transmissible than existing variants. Even with full-scale vaccination efforts, the theoretical threshold for eradicating COVID-19 appears out of reach. This article proposes an artificial intelligence(AI) based intelligent prediction model called Deep-SIQRV(Susceptible-Infected-Quarantined-Recovered-Vaccinated) to simulate the spreading of COVID-19. While many models assume that vaccination provides lifetime protection, we focus on the impact of waning immunity caused by the conversion of vaccinated individuals back to susceptible ones. Unlike existing models, which assume that all coronavirus-infected individuals have the same infection rate, the proposed model considers the various infection rates to analyze transmission laws and trends. Next, we consider the influence of prevention and control strategies, such as media marketing and law enforcement, on the spread of the epidemic. We employed the PAN-LDA model to extract features from COVID-19-related discussions on social media and online news articles. Moreover, the Long Short Term Memory(LSTM) model and Evolution Strategies(ES) are used to optimize transmission rates of infection and other model parameters, respectively. The experimental results on epidemic data from various Indian states demonstrate that persons infected with coronavirus had a more significant infection rate within four to nine days after infection, which corresponds to the actual transmission laws of the epidemic. The experimental results show that the proposed model has good prediction ability and obtains the Mean Absolute Percentage Error(MAPE) of 0.875%, 0.965%, 0.298%, and 0.215% for the next eight days in Maharashtra, Kerala, Karnataka, and Delhi, respectively. Our findings highlight the significance of using vaccination data, COVID-19-related posts, and information generated by the government's tremendous efforts in the prediction calculation process.

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.