The double-membrane-bound architecture of mitochondria, essential for ATP production, sub-divides the organelle into inter-membrane space (IMS) and matrix. IMS and matrix possess contrasting oxido-reductive environments and discrete protein quality control (PQC) machineries resulting inherent differences in their protein folding environments. To understand the nature of stress response elicited by equivalent proteotoxic stress to these sub-mitochondrial compartments, we took misfolding and aggregation-prone stressor proteins and fused it to well described signal sequences to specifically target and impart stress to yeast mitochondrial IMS or matrix. We show, mitochondrial proteotoxicity leads to growth arrest of yeast cells of varying degrees depending on nature of stressor proteins and the intra-mitochondrial location of stress. Next, by employing transcriptomics and proteomics, we report a comprehensive stress response elicited by stressor proteins specifically targeted to mitochondrial matrix or IMS. A general response to proteotoxic stress by mitochondria-targeted misfolded proteins is mitochondrial fragmentation, and an adaptive abrogation of mitochondrial respiration with concomitant upregulation of glycolysis. Beyond shared stress responses, specific signatures due to stress within mitochondrial sub-compartments are also revealed. We report that stress-imparted by bipartite signal sequence-fused stressor proteins to IMS, leads to specific upregulation of IMS-chaperones and TOM complex components. In contrast, matrix-targeted stressors lead to specific upregulation of matrix-chaperones and cytosolic PQC components. Finally, by systematic genetic interaction using deletion strains of differentially upregulated genes, we found prominent modulatory role of TOM complex components during IMS-stress response. In contrast, VMS1 markedly modulates the stress response originated from matrix.

The role of intrinsically disordered protein regions (IDRs) in cellular processes has become increasingly evident over the last years. These IDRs continue to challenge structural biology experiments because they lack a well-defined conformation, and bioinformatics approaches that accurately delineate disordered protein regions remain essential for their identification and further investigation. Typically, these predictors use the protein amino acid sequence, without taking into account likely sequence-dependent emergent properties, such as protein backbone dynamics. Here we present DisoMine, a method that predicts protein'long disorder' with recurrent neural networks from simple predictions of protein dynamics, secondary structure and early folding. The tool is fast and requires only a single sequence, making it applicable for large-scale screening, including poorly studied and orphan proteins. DisoMine is a top performer in its category and compares well to disorder prediction approaches using evolutionary information. DisoMine is freely available through an interactive webserver at https://bio2byte.be/disomine/.

The soluble cytoplasmic ATPase motor protein SecA powers protein transport across the Escherichia coli inner membrane via the SecYEG translocon. Although dimeric in solution, SecA associates monomerically with SecYEG during secretion according to several crystallographic and cryo-EM structural studies. The steps SecA follows from its dimeric cytoplasmic state to its active SecYEG monomeric state are largely unknown. We have previously shown that dimeric SecA in solution dissociates into monomers upon electrostatic binding to negatively charged lipid vesicles formed from E. coli lipids. Here we address the question of the disposition of SecA on the membrane prior to binding to membrane embedded SecYEG. We mutated to cysteine, one at a time, 25 surface-exposed residues of a Cys-free SecA. To each of these we covalently linked the polarity-sensitive fluorophore NBD whose intensity and fluorescence wavelength-shift change upon vesicle binding report on the the local membrane polarity. We established from these measurements the disposition of SecA bound to the membrane in the absence of SecYEG. Our results confirmed that SecA is anchored in the membrane interface primarily by the positive charges of the N terminus domain. But we found that a region of the nucleotide binding domain II is also important for binding. Both domains are rich in positively charged residues, consistent with electrostatic interactions playing the major role in membrane binding. Selective replacement of positively charged residues in these domains with alanine resulted in weaker binding to the membrane, which allowed us to quantitate the relative importance of the domains in stabilizing SecA on membranes. Fluorescence quenchers inside the vesicles had little effect on NBD fluorescence, indicating that SecA does not penetrate significantly across the membrane. Overall, the topology of SecA on the membrane is consistent with the conformation of SecA observed in crystallographic and cryo-EM structures of SecA-SecYEG complexes, suggesting that SecA can switch between the membrane-associated and the translocon-associated states without significant changes in conformation.

The fidelity of initiator tRNA (i-tRNA) selection in the ribosomal P-site is a key step in translation initiation. The highly conserved three consecutive G:C base pairs (3GC pairs) in the i-tRNA anticodon stem play a crucial role in its selective binding in the P-site. Mutations in the 3GC pairs (3GC mutant) render the i-tRNA inactive in initiation. Here, we show that a mutation (E265K) in the unique C-terminal tail domain of RluD, a large ribosomal subunit pseudouridine synthase, results in compromised fidelity of initiation and allows initiation with the 3GC mutant i-tRNA. RluD modifies the uridine residues in H69 to pseudouridines. However, the role of its C-terminal tail domain remained unknown. The E265K mutation does not diminish the pseudouridine synthase activity of RluD, or the growth phenotype of Escherichia coli, or cause any detectable defects in the ribosomal assembly in our assays. However, in our in vivo analyses, we observed that the E265K mutation resulted in increased retention of the ribosome binding factor A (RbfA) on 30S suggesting a new role of RluD in contributing to RbfA release, a function which may be attributed to its (RluD) C-terminal tail domain. The studies also reveal that deficiency of RbfA release from 30S compromises the fidelity of i-tRNA selection in the ribosomal P-site.

High performance affinity reagents are essential tools to enable biologists to profile the cellular location and composition of macromolecular complexes undergoing dynamic reorganization. To support further development of such tools, we have assembled a high-throughput phage display pipeline to generate Fab-based affinity reagents that target different dynamic forms of a large macromolecular complex, using the Chromosomal Passenger Complex (CPC), as an example. The CPC is critical for the maintenance of chromosomal and cytoskeleton processes during cell division. The complex contains 4 protein components: Aurora B kinase, survivin, borealin and INCENP. The CPC acts as a node to dynamically organize other partnering subcomplexes to build multiple functional structures during mitotic progression. Using phage display mutagenesis, a cohort of synthetic antibodies (sABs) were generated against different domains of survivin, borealin and INCENP. Immunofluorescence established that a set of these sABs can discriminate between the form of the CPC complex in the midbody versus the spindle. Others localize to targets, which appear to be less organized, in the nucleus or cytoplasm. This differentiation suggests that different CPC epitopes have dynamic accessibility depending upon the mitotic state of the cell. An Immunoprecipitation/Mass Spectrometry analysis was performed using sABs that bound specifically to the CPC in either the midbody or MT spindle macromolecular assemblies. Thus, sABs can be exploited as high performance reagents to profile the accessibility of different components of the CPC within macromolecular assemblies during different stages of mitosis suggesting this high throughput approach will be applicable to other complex macromolecular systems.

Cation selectivity and coupling are important attributes of cation-coupled symporters. Salmonella typhimurium melibiose permease (MelBSt) catalyzes the co-transport of galactosides with cations (H+, Li+, or Na+). 3-D crystal structures of MelBSt have revealed the molecular recognition for sugar substrates, but the cation binding and coupling mechanisms have not been defined to atomic levels. In its human homolog MFSD2A, a lethal mutation was mapped at its Na+-binding pocket; however, none of the structures in this subfamily resolved its cation binding. In this study, molecular dynamics simulations reveal the binding interactions of Na+ and Li+ with MelBSt. Interestingly, Thr121, the lethal mutation position in MFSD2A, forms stable interaction with Na+ but is at a distance from Li+. Most mutations among 11 single-site Thr121 mutants of MelBSt exhibited little effects on the galactoside binding, but largely altered the cation selectivity with severe inhibitions on Na+ binding. Few mutants (Pro and Ala) completely lost the Na+ binding and Na+-coupled transport, but their Li+ or H+ modes of activity were largely retained. It can be concluded that Thr121 is necessary for Na+ binding, but not required for the binding of H+ or Li+, so a subset of the Na+-binding pocket is enough for Li+ binding. In addition, the protein stability for some mutants can be only retained in the presence of Li+, but not by Na+ due to the lack of affinity. This finding, together with other identified thermostable mutants, supports that the charge balance of the cation-binding site plays an important role in MelBSt protein stability.

Pathogenic Staphylococcus aureus actively acquires iron from human hemoglobin (Hb) using the IsdH surface receptor. Heme extraction is mediated by a tri-domain unit within the receptor that contains its second (N2) and third (N3) NEAT domains joined by a helical linker domain. Extraction occurs within a dynamic complex, in which receptors engage each globin chain; the N2 domain tightly binds to Hb, while substantial inter-domain motions within the receptor enable its N3 domain to transiently distort the globin's heme pocket. Using molecular simulations coupled with Markov modeling, along with stopped-flow experiments to quantitatively measure heme transfer kinetics, we show that directed inter-domain motions within the receptor play a critical role in the extraction process. The directionality of N3 domain motion and the rate of heme extraction is controlled by amino acids within a short, flexible inter-domain tether that connects the N2 and linker domains. In the wild-type receptor directed motions originating from the tether enable the N3 domain to populate configurations capable of distorting Hb's pocket, whereas mutant receptors containing altered tethers are less able to adopt these conformers and capture heme slowly via indirect processes in which Hb first releases heme into the solvent. Thus, our results show inter-domain motions within the IsdH receptor play a critical role in its ability to extract heme from Hb and highlight the importance of directed motions by the short, unstructured, amino acid sequence connecting the domains in controlling the directionality and magnitude of these functionally important motions.

Assembly of human T-cell leukemia virus type 1 (HTLV-1) particles is initiated by the trafficking of virally encoded Gag polyproteins to the inner leaflet of the plasma membrane (PM). Gag-PM interactions are mediated by the matrix (MA) domain, which contains a myristoyl group (myr) and a basic patch formed by lysine and arginine residues. For many retroviruses, Gag-PM interactions are mediated by phosphatidylinositol 4,5-bisphosphate [PI(4,5)P2]; however, previous studies suggested that HTLV-1 Gag-PM interactions and therefore virus assembly are less dependent on PI(4,5)P2. We have recently shown that PI(4,5)P2 binds directly to HTLV-1 unmyristoylated MA [myr(-)MA] and that myr(-)MA binding to membranes is significantly enhanced by inclusion of phosphatidylserine (PS) and PI(4,5)P2. Herein, we employed structural, biophysical, biochemical, mutagenesis, and cell-based assays to identify residues involved in MA-membrane interactions. Our data revealed that the lysine-rich motif (Lys47, Lys48, and Lys51) constitutes the primary PI(4,5)P2-binding site. Furthermore, we show that arginine residues 3, 7, 14 and 17 located in the unstructured N-terminus are essential for MA binding to membranes containing PS and/or PI(4,5)P2. Substitution of lysine and arginine residues severely attenuated virus-like particle production, but only the lysine residues could be clearly correlated with reduced PM binding. These results support a mechanism by which HTLV-1 Gag targeting to the PM is mediated by a trio engagement of the myr group, Arg-rich and Lys-rich motifs. These findings advance our understanding of a key step in retroviral particle assembly.

Plasmodium falciparum expresses clonally variant proteins on the surface of infected erythrocytes to evade the host immune system. The clonally variant multigene families include var, rifin, and stevor, which express Erythrocyte Membrane Protein 1 (EMP1), Repetitive Interspersed Families of polypeptides (RIFINs), and Sub-telomeric Variable Open Reading frame (STEVOR) proteins, respectively. The rifins are the largest multigene family and are essentially involved in the RBC rosetting, the hallmark of severe malaria. The molecular regulators that control the RIFINs expression in Plasmodium spp. have not been reported so far. This study reports a chromodomain-containing protein (PfCDP) that binds to H3K9me3 modification on P. falciparum chromatin. Conditional deletion of the chromodomain (CD) gene in P. falciparum using an inducible DiCre-LoxP system leads to selective up-regulation of a subset of virulence genes, including rifins, a few var, and stevor genes. Further, we show that PfCDP conditional knockout (PfΔCDP) promotes RBC rosette formation. This study provides the first evidence of an epigenetic regulator mediated control on a subset of RIFINs expression and RBC rosetting by P. falciparum.

Ribonuclease H2 (RNase H2) is a member of the ribonuclease H family of enzymes involved in removal of RNA from RNA-DNA hybrids as well as ribonucleotides which get misincorporated into the genomic DNA. Recent studies have shown that RNase H2 function is also needed for successful DNA repair through NHEJ events where DNA pol µ uses ribonucleotides during the gap filling stage. Mammalian RNase H2 is composed of three subunits, RNASEH2A, RNASEH2B and RNASEH2C. There have been studies suggesting changes in expression of these genes in various cancers of breast, prostate, colon, liver, and kidney. In this study, we have investigated the functional role of RNASEH2A and RNASEH2B in leukemic T-cells, MOLT4 and Jurkat. shRNA mediated knockdown of RNASEH2A/ RNASEH2B expression led to reduced cell survival and increase in apoptotic cell population. Importantly, knockdown of RNASEH2A or RNASEH2B, led to cell cycle arrest at S phase and increased number of 53BP1 foci due to abrogation of NHEJ. Interestingly, RNASEH2A or RNASEH2B depleted cells showed significantly retarded DSB repair kinetics compared to scrambled shRNA control, when exposed to ionizing radiation suggesting that NHEJ is abrogated due to loss of RNASEH2 activity in T-ALL cells. Thus, we uncover the importance of RNase H2 function in leukemic cells and suggest that it can be targeted for cancer therapy.

There are hundreds of RNA binding proteins in the human genome alone and their interactions with messenger and other RNAs in a cell regulate every step in an RNA's life cycle. To understand this interplay of proteins and RNA it is important to be able to know which protein binds which RNA how strongly and where. Here, we introduce RBPBind, a web-based tool for the quantitative prediction of the interaction of single-stranded RNA binding proteins with target RNAs that fully takes into account the effect of RNA secondary structure on binding affinity. Given a user-specified RNA and a protein selected from a set of several RNA-binding proteins, RBPBind computes their binding curve and effective binding constant. The server also computes the probability that, at a given protein concentration, a protein molecule will bind to any particular nucleotide along the RNA. The sequence specificity of the protein-RNA interaction is parameterized from public RNAcompete experiments and integrated into the recursions of the Vienna RNA package to simultaneously take into account protein binding and RNA secondary structure. We validate our approach by comparison to experimentally determined binding affinities of the HuR protein for several RNAs of different sequence contexts from the literature, showing that integration of raw sequence affinities into RNA secondary structure prediction significantly improves the agreement between computationally predicted and experimentally measured binding affinities. Our resource thus provides a quick and easy way to obtain reliable predicted binding affinities and locations for single-stranded RNA binding proteins based on RNA sequence alone.

PDBx/mmCIF, Protein Data Bank Exchange (PDBx) macromolecular Crystallographic Information Framework (mmCIF), has become the data standard for structural biology. With its early roots in the domain of small-molecule crystallography, PDBx/mmCIF provides an extensible data representation that is used for deposition, archiving, remediation, and public dissemination of experimentally determined three-dimensional (3D) structures of biological macromolecules by the Worldwide Protein Data Bank (wwPDB, wwpdb.org). Extensions of PDBx/mmCIF are similarly used for computed structure models by ModelArchive (modelarchive.org), integrative/hybrid structures by PDB-Dev (pdb-dev.wwpdb.org), small angle scattering data by Small Angle Scattering Biological Data Bank SASBDB (sasbdb.org), and for models computed generated with the AlphaFold 2.0 deep learning software suite (alphafold.ebi.ac.uk). Community-driven development of PDBx/mmCIF spans three decades, involving contributions from researchers, software and methods developers in structural sciences, data repository providers, scientific publishers, and professional societies. Having a semantically rich and extensible data framework for representing a wide range of structural biology experimental and computational results, combined with expertly curated 3D biostructure data sets in public repositories, accelerates the pace of scientific discovery. Herein, we describe the architecture of the PDBx/mmCIF data standard, tools used to maintain representations of the data standard, governance, and processes by which data content standards are extended, plus community tools/software libraries available for processing and checking the integrity of PDBx/mmCIF data. Use cases exemplify how the members of the Worldwide Protein Data Bank have used PDBx/mmCIF as the foundation for its pipeline for delivering Findable, Accessible, Interoperable, and Reusable (FAIR) data to many millions of users worldwide.

During the last few decades, the study of microbial ecology has been enabled by molecular and genomic data. DNA sequencing has revealed the surprising extent of microbial diversity and how microbial processes run global ecosystems. However, significant gaps in our understanding of the microbial world remain, and one example is that microbial eukaryotes, or protists, are still largely neglected. To address this gap, we used gene expression data from 17 protist species to create protist.guru: an online database equipped with tools for identifying co-expressed genes, gene families, and co-expression clusters enriched for specific biological functions. Here, we show how our database can be used to reveal genes involved in essential pathways, such as the synthesis of secondary carotenoids in Haematococcus lacustris. We expect protist.guru to serve as a valuable resource for protistologists, as well as a catalyst for discoveries and new insights into the biological processes of microbial eukaryotes. AVAILABILITY: The database and co-expression networks are freely available from http://protist.guru/. The expression matrices and sample annotations are found in the supplementary data.

While bacteria can be beneficial to our health, their deadly pathogenic potential has been an ever-present concern exacerbated by the emergence of drug-resistant strains. As such, there is a pressing urgency for an enhanced understanding of their gene function and regulation, which could mediate the development of novel antimicrobials. Transcriptomic analyses have been established as insightful and indispensable to the functional characterization of genes and identification of new biological pathways, but in the context of bacterial studies, they remain limited to species-specific datasets. To address this, we integrated the genomic and transcriptomic data of the 17 most notorious and researched bacterial pathogens, creating bacteria.guru, an interactive database that can identify, visualize, and compare gene expression profiles, coexpression networks, functionally enriched clusters, and gene families across species. Through illustrating antibiotic resistance mechanisms in P. aeruginosa, we demonstrate that bacteria.guru could potentially aid in discovering multi-faceted antibiotic targets and, overall, facilitate future bacterial research. AVAILABILITY: The database and coexpression networks are freely available from https://bacteria.guru/. Sample annotations can be found in the supplemental data.

3D structures of RNAs are the basis for understanding their biological functions. However, experimentally solved RNA 3D structures are very limited. Therefore, many computational methods have been proposed to solve this problem, including our 3dRNA. 3dRNA is an automated template-based method of building RNA 3D structures from sequences and secondary structures by using the smallest secondary elements (SSEs) (http://biophy.hust.edu.cn/new/3dRNA). The first version of 3dRNA simply predicts an assembled structure for a target RNA. Later, it is improved to generate a set of assembled models and a method to further optimize them using experimental or theoretical restraints. In particular, pseudoknot base pairings are treated as restraints to solve the problem of no 3D templates for pseudoknots. Here 3dRNA is further extended to predict the 3D structures of circular RNAs since thousands of circular RNAs have been found recently but no 3D structures of them have been determined up to now. We show that circular RNAs can be divided into four types and two types show similar 3D structures with their linear counterparts while two types very different. We also show that the predicted structures of circular RNAs can bind to their ligands more stable than those of their linear counterparts, consistent with experimental results.

Despite the huge impact of data resources in genomics and structural biology, until now there has been no central archive for biological data for all imaging modalities. The BioImage Archive is a new data resource at the European Bioinformatics Institute (EMBL-EBI) designed to fill this gap. In its initial development BioImage Archive accepts bioimaging data associated with publications, in any format, from any imaging modality from the molecular to the organism scale, excluding medical imaging. The BioImage Archive will ensure reproducibility of published studies that derive results from image data and reduce duplication of effort. Most importantly, the BioImage Archive will help scientists to generate new insights through reuse of existing data to answer new biological questions, and provision of training, testing and benchmarking data for development of tools for image analysis. The archive is available at https://www.ebi.ac.uk/bioimage-archive/.

Hereditary diseases tend to manifest clinically in few selected tissues. Knowledge of those tissues is important for better understanding of disease mechanisms, which often remain elusive. However, information on the tissues inflicted by each disease is not easily obtainable. Well-established resources, such as the Online Mendelian Inheritance in Man (OMIM) database and Human Phenotype Ontology (HPO), report on a spectrum of disease manifestations, yet do not highlight the main inflicted tissues. The Organ-Disease Annotations (ODiseA) database contains 4,357 thoroughly-curated annotations for 2,181 hereditary diseases and 45 inflicted tissues. Additionally, ODiseA reports 692 annotations of 635 diseases and the pathogenic tissues where they emerge. ODiseA can be queried by disease, disease gene, or inflicted tissue. Owing to its expansive, high-quality annotations, ODiseA serves as a valuable and unique tool for biomedical and computational researchers studying genotype-phenotype relationships of hereditary diseases. ODiseA is available at https://netbio.bgu.ac.il/odisea.

The mining of the massive amounts of biomedical information is hindered by the still scarce representation of these data using formal vocabularies and ontologies, which is necessary for cross-linking conceptual entities between different resources and, in general, representing the information in a computer-tractable way. Basic things such as retrieving a comprehensive list of associations between complex diseases and their reported symptoms or underlying biological processes, given in terms of formal identifiers, are not trivial and, in many cases, these have to be generated by manual curation or inferred/predicted from indirect evidences. In this work, using a text-mining approach based on detecting significant co-mentions in the scientific literature, we generated a resource with millions of relationships between thousands of terms representing diseases, symptoms, biological processes, molecular functions and cellular compartments, all given in terms of formal identifiers of these terms in the main resources dealing with them. We show some examples that highlight the differences between these relationships and those that are available in other resources. These relationships can be queried and inspected in an interactive web interface freely available at: https://sysbiol.cnb.csic.es/CoMent.

The identification of new drugs for novel therapeutic targets requires the screening of libraries containing tens of thousands of compounds. While experimental screenings are assisted by high-throughput technologies, in target-based biophysical assays, such as differential scanning fluorimetry (DSF), the analysis steps must be calculated manually, often combining several software packages. To simplify the determination of the melting temperature (Tm) of the target and the change induced by ligand binding (ΔTm), we developed the HTSDSF explorer, a versatile, all-in-one, user-friendly application suite. Implemented as a server-client application, in the primary screenings, HTSDSF explorer pre-analyzes and displays the Tm and ΔTm results interactively, thereby allowing the user to study hundreds of conditions and select the primary hits in minutes. This application also allows the determination of preliminary binding constants (KD) through a series of subsequent dose-response assays on the primary hits, thereby facilitating the ranking of validated hits and the advance of drug discovery efforts.

Tissue contexts are extremely valuable when studying protein functions and their associated phenotypes. Recently, the study of proteins in tissue contexts was greatly facilitated by the availability of thousands of tissue transcriptomes. To provide access to these data we developed the TissueNet integrative database that displays protein-protein interactions (PPIs) in tissue contexts. Through TissueNet, users can create tissue-sensitive network views of the PPI landscape of query proteins. Unlike other tools, TissueNet output networks highlight tissue-specific and broadly expressed proteins, as well as over- and under-expressed proteins per tissue. The TissueNet v.3 upgrade has a much larger dataset of proteins and PPIs, and represents 125 adult tissues and seven embryonic tissues. Thus, TissueNet provides an extensive, quantitative, and user-friendly interface to study the roles of human proteins in adulthood and embryonic stages. TissueNet v3 is freely available at https://netbio.bgu.ac.il/tissuenet3.

Experimental biologists are often left alone with the task to download, process, and analyze big datasets in order to perform correlation or other simpler analyses. To address these issues, we introduce EviCor, a handy toolbox for exploration of data from large public resources such as The Cancer Genome Atlas and The Cancer Cell Line Encyclopedia, complemented with follow-up information on same samples, which couples omics datasets with drug response profiles (https://www.evicor.org/). The data was processed for easy retrieval from the server-side database and includes pre-computed drug-feature correlation tables. Using information from multiple independent sources, the task-oriented web interface presents relations between phenotype, single-molecule, and pathway variables with graphical, statistical, and network analysis tools. Building custom multivariate models is enabled via user-friendly web interface and programmatic access via RESTinterface. Project code is available at https://github.com/aveviort/HyperSet.

Knowing which proteins interact with each other is essential information for understanding how most biological processes at the cellular and organismal level operate and how their perturbation can cause disease. Continuous technical and methodological advances over the last two decades have led to many genome-wide systematically-generated protein-protein interaction (PPI) maps. To help store, visualize, analyze and disseminate these specialized experimental datasets via the web, we developed the freely-available Open-source Protein Interaction Platform (openPIP) as a customizable web portal designed to host experimental PPI maps. Such a portal is often required to accompany a paper describing the experimental data set, in addition to depositing the data in a standard repository. No coding skills are required to set up and customize the database and web portal. OpenPIP has been used to build the databases and web portals of two major protein interactome maps, the Human and Yeast Reference Protein Interactome maps (HuRI and YeRI, respectively). OpenPIP is freely available as a ready-to-use Docker container for hosting and sharing PPI data with the scientific community at http://openpip.baderlab.org/ and the source code can be downloaded from https://github.com/BaderLab/openPIP/.

Visualizing single-cell transcriptomics data in an informative way is a major challenge in biological data analysis. Clustering of cells is a prominent analysis step and the results are usually visualized in a planar embedding of the cells using methods like PCA, t-SNE, or UMAP. Given a cluster of cells, one frequently searches for the genes highly expressed specifically in that cluster. At this point, visualization is usually replaced by studying a list of differentially expressed genes. Association Plots are derived from correspondence analysis and constitute a planar visualization of the features which characterize a given cluster of observations. We have adapted Association Plots to address the challenge of visualizing cluster-specific genes in large single-cell data sets. Our method is made available as a free R package called APL. We demonstrate the application of APL and Association Plots to single-cell RNA-seq data on two example data sets. First, we present how to delineate novel marker genes using Association Plots with the example of Peripheral Blood Mononuclear Cell data. Second, we show how to apply Association Plots for annotating cell clusters to known cell types using Association Plots and a predefined list of marker genes. To do this we will use data from the human cell atlas of fetal gene expression. Results from Association Plots will also be compared to methods for deriving differentially expressed genes, and we will show the integration of APL with Gene Ontology Enrichment.

CellDepot containing over 270 datasets from 8 species and many tissues serves as an integrated web application to empower scientists in exploring single-cell RNA-seq (scRNA-seq) datasets and comparing the datasets among various studies through a user-friendly interface with advanced visualization and analytical capabilities. To begin with, it provides an efficient data management system that users can upload single cell datasets and query the database by multiple attributes such as species and cell types. In addition, the graphical multi-logic, multi-condition query builder and convenient filtering tool backed by MySQL database system, allows users to quickly find the datasets of interest and compare the expression of gene(s) across these. Moreover, by embedding the cellxgene VIP tool, CellDepot enables fast exploration of individual dataset in the manner of interactivity and scalability to gain more refined insights such as cell composition, gene expression profiles, and differentially expressed genes among cell types by leveraging more than 20 frequently applied plotting functions and high-level analysis methods in single cell research. In summary, the web portal available at http://celldepot.bxgenomics.com, prompts large scale single cell data sharing, facilitates meta-analysis and visualization, and encourages scientists to contribute to the single-cell community in a tractable and collaborative way. Finally, CellDepot is released as open-source software under MIT license to motivate crowd contribution, broad adoption, and local deployment for private datasets.

Computational approaches for predicting protein-protein interfaces are extremely useful for understanding and modelling the quaternary structure of protein assemblies. In particular, partner-specific binding site prediction methods allow delineating the specific residues that compose the interface of protein complexes. In recent years, new machine learning and other algorithmic approaches have been proposed to solve this problem. However, little effort has been made in finding better training datasets to improve the performance of these methods. With the aim of vindicating the importance of the training set compilation procedure, in this work we present BIPSPI+, a new version of our original server trained on carefully curated datasets that outperforms our original predictor. We show how prediction performance can be improved by selecting specific datasets that better describe particular types of protein interactions and interfaces (e.g. homo/hetero). In addition, our upgraded web server offers a new set of functionalities such as the sequence-structure prediction mode, hetero- or homo-complex specialization and the guided docking tool that allows to compute 3D quaternary structure poses using the predicted interfaces. BIPSPI+ is freely available at https://bipspi.cnb.csic.es.

Cell-penetrating peptides (CPPs) translocate into the cell as various biologically active conjugates and possess numerous biomedical applications. Several machine learning-based predictors have been proposed in the past, but they mostly focus on identifying only CPPs. We proposed a two-layered predictor in 2018 in order to predict CPPs and their uptake efficiency simultaneously. While MLCPP has gained widespread access to research, further improvements are needed to enhance its practical application. A new version of MLCPP is presented in this study called MLCPP 2.0, an interpretable stacking model that identifies CPPs and their strength of uptake efficiency. We updated the benchmarking dataset, explored 17 different sequence-based feature encoding algorithms, and used seven different conventional machine learning classifiers. With multiple 10-fold cross-validation, we constructed 119 baseline models whose predicted probability values were merged and treated as a new feature vector. In a systematic way, a feature set and a classifier are identified that are optimal for predicting the CPP and uptake efficiency separately. The MLCPP 2.0 model achieved outstanding performance on the independent test set, significantly outperforming the existing state-of-the-art predictors. Hence, we expect that our proposed MLCPP 2.0 will facilitate the design of hypothesis-driven experiments by enabling the discovery of novel CPPs. MLCPP 2.0 is freely accessible at https://balalab-skku.org/mlcpp2/.

Myristoylation (MYR) is a protein modification where a myristoyl group is covalently attached to an exposed (N-terminal) glycine residue. Glycine myristoylation occurs during protein translation (co-translation) or after (post-translation). Myristoylated proteins have a role in signal transduction, apoptosis, and pathogen-mediated processes and their prediction can help in functionally annotating the fraction of proteins undergoing MYR in different proteomes. Here we present SVMyr, a web server allowing the detection of both co- and post-translational myristoylation sites, based on Support Vector Machines (SVM). The input encodes composition and physicochemical features of the octapeptides, known to act as substrates and to physically interact with N-myristoyltransferases (NMTs), the enzymes catalyzing the myristoylation reaction. The method, adopting a cross validation procedure, scores with values of Area Under the Curve (AUC) and Matthews Correlation Coefficient (MCC) of 0.92 and 0.61, respectively. When benchmarked on an independent dataset including experimentally detected 88 medium/high confidence co-translational myristoylation sites and 528 negative examples, SVMyr outperforms available methods, with AUC and MCC equal to 0.91 and 0.58, respectively. A unique feature of SVMyr is the ability to predict post-translational myristoylation sites by coupling the trained SVMs with the detection of caspase cleavage sites, identified by searching regular motifs matching upstream caspase cleavage sites, as reported in literature. Finally, SVMyr confirms 96% of the UniProt set of the electronically annotated myristoylated proteins (31,048) and identifies putative myristoylomes in eight different proteomes, highlighting also new putative NMT substrates. SVMyr is freely available through a user-friendly web server at https://busca.biocomp.unibo.it/lipipred.

Identification of environment specific marker-features is one of the key objectives of many metagenomic studies. It aims to identify such features in microbiome datasets that may serve as markers of the contrasting or comparable states. Hypothesis testing and black-box machine learnt models which are conventionally used for identification of these features are generally not exhaustive, especially because they generally do-not provide any quantifiable relevance (context) of/between the identified features. We present MarkerML web-server, that seeks to leverage the emergence of interpretable machine learning for facilitating the contextual discovery of metagenomic features of interest. It does so through a comprehensive and automated application of the concept of Shapley Additive Explanations in companionship to the compositionality accounted hypothesis testing for the multi-variate microbiome datasets. MarkerML not only helps in identification of marker-features, but also enables insights into the role and inter-dependence of the identified features in driving the decision making of the supervised machine learnt model. Generation of high quality and intuitive visualizations spanning prediction effect plots, model performance reports, feature dependency plots, Shapley and abundance informed cladograms (Sungrams), hypothesis tested violin plots along-with necessary provisions for excluding the participant bias and ensuring reproducibility of results, further seek to make the platform a useful asset for the scientists in the field of microbiome (and even beyond). The MarkerML web-server is freely available for the academic community at https://microbiome.igib.res.in/markerml/.

N7-methylguanosine (m7G) is an essential, ubiquitous, and positively charged modification at the 5' cap of eukaryotic mRNA, modulating its export, translation, and splicing processes. Although several machine learning (ML)-based computational predictors for m7G have been developed, all utilized specific computational framework. This study is the first instance we explored four different computational frameworks and identified the best approach. Based on that we developed a novel predictor, THRONE (A three-layer ensemble predictor for identifying human RNA N7-methylguanosine sites) to accurately identify m7G sites from the human genome. THRONE employs a wide range of sequence-based features inputted to several ML classifiers and combines these models through ensemble learning. The three-step ensemble learning is as follows: 54 baseline models were constructed in the first layer and the predicted probability of m7G was considered as a new feature vector for the sequential step. Subsequently, six meta-models were created using the new feature vector and their predicted probability was yet again considered as novel features. Finally, random forest was deemed as the best super classifier learner for the final prediction using a systematic approach incorporated with novel features. Interestingly, THRONE outperformed other existing methods in the prediction of m7G sites on both cross-validation analysis and independent evaluation. The proposed method is publicly accessible at: http://thegleelab.org/THRONE/ and expects to help the scientific community identify the putative m7G sites and formulate a novel testable biological hypothesis.

Measuring evolutionary rates at the residue level is indispensable for gaining structural and functional insights into proteins. State-of-the-art tools for estimating rates take as input a large set of homologous proteins, a probabilistic model of evolution and a phylogenetic tree. However, a gap exists when only few or no homologous proteins can be found, e.g., orphan proteins. In addition, such tools do not take the three-dimensional (3D) structure of the protein into account. The association between the 3D structure and site-specific rates can be learned using machine-learning regression tools from a cohort of proteins for which both the structure and a large set of homologs exist. Here we present EvoRator, a user-friendly web server that implements a machine-learning regression algorithm to predict site-specific evolutionary rates from protein structures. We show that EvoRator outperforms predictions obtained using traditional physicochemical features, such as relative solvent accessibility and weighted contact number. We also demonstrate the application of EvoRator in three common scenarios that arise in protein evolution research: (1) orphan proteins for which no (or few) homologs exist; (2) When homologous sequences exist, our algorithm contrasts structure-based estimates of the evolutionary rates and the phylogeny-based estimates. This allows detecting sites that are likely conserved due to functional rather than structural constraints; (3) Algorithms that only rely on homologous sequence often fail to accurately measure the evolutionary rates of positions in gapped sequence alignments, which frequently occurs as a result of a clade-specific insertion. Our algorithm makes use of training data and known 3D structure of such gapped positions to predict their evolutionary rates. EvoRator is freely available for all users at: https://evorator.tau.ac.il/.

A significant proportion of proteins comprise multiple domains. Domain-domain docking is a tool that predicts multi-domain protein structures when individual domain structures can be accurately predicted but when domain orientations cannot be predicted accurately. GalaxyDomDock predicts an ensemble of domain orientations from given domain structures by docking. Such information would also be beneficial in elucidating the functions of proteins that have multiple states with different domain orientations. GalaxyDomDock is an ab initio domain-domain docking method based on GalaxyTongDock, a previously developed protein-protein docking method. Infeasible domain orientations for the given linker are effectively screened out from the docked conformations by a geometric filter, using the Dijkstra algorithm. In addition, domain linker conformations are predicted by adopting a loop sampling method FALC. The proposed GalaxyDomDock outperformed existing ab initio domain-domain docking methods, such as AIDA and Rosetta, in performance tests on the Rosetta benchmark set of two-domain proteins. GalaxyDomDock also performed better than or comparable to AIDA on the AIDA benchmark set of two-domain proteins and two-domain proteins containing discontinuous domains, including the benchmark set in which each domain of the set was modeled by the recent version of AlphaFold. The GalaxyDomDock web server is freely available as a part of GalaxyWEB at http://galaxy.seoklab.org/domdock.

Rapid progress in structural modeling of proteins and their interactions is powered by advances in knowledge-based methodologies along with better understanding of physical principles of protein structure and function. The pool of structural data for modeling of proteins and protein-protein complexes is constantly increasing due to the rapid growth of protein interaction databases and Protein Data Bank. The GWYRE (Genome Wide PhYRE) project capitalizes on these developments by advancing and applying new powerful modeling methodologies to structural modeling of protein-protein interactions and genetic variation. The methods integrate knowledge-based tertiary structure prediction using Phyre2 and quaternary structure prediction using template-based docking by a full-structure alignment protocol to generate models for binary complexes. The predictions are incorporated in a comprehensive public resource for structural characterization of the human interactome and the location of human genetic variants. The GWYRE resource facilitates better understanding of principles of protein interaction and structure/function relationships. The resource is available at http://www.gwyre.org.

Proteome-wide identification of protein-protein interactions is a formidable task which has yet to be sufficiently addressed by experimental methodologies. Many computational methods have been developed to predict proteome-wide interaction networks, but few leverage both the sensitivity of structural information and the wide availability of sequence data. We present PEPPI, a pipeline which integrates structural similarity, sequence similarity, functional association data, and machine learning-based classification through a naïve Bayesian classifier model to accurately predict protein-protein interactions at a proteomic scale. Through benchmarking against a set of 798 ground truth interactions and an equal number of non-interactions, we have found that PEPPI attains 4.5% higher AUROC than the best of other state-of-the-art methods. As a proteomic-scale application, PEPPI was applied to model the interactions which occur between SARS-CoV-2 and human host cells during coronavirus infection, where 403 high-confidence interactions were identified with predictions covering 73% of a gold standard dataset from PSICQUIC and demonstrating significant complementarity with the most recent high-throughput experiments. PEPPI is available both as a webserver and in a standalone version and should be a powerful and generally applicable tool for computational screening of protein-protein interactions.

Detecting viral and vector integration events is a key step when investigating interactions between viral and host genomes. This is relevant in several fields, including virology, cancer research and gene therapy. For example, investigating integrations of wild-type viruses such as human papillomavirus and hepatitis B virus has proven to be crucial for understanding the role of these integrations in cancer. Furthermore, identifying the extent of vector integration is vital for determining the potential for genotoxicity in gene therapies. To address these questions, we developed isling, the first tool specifically designed for identifying viral integrations in both wild-type and vector from next-generation sequencing data. Isling addresses complexities in integration behaviour including integration of fragmented genomes and integration junctions with ambiguous locations in a host or vector genome, and can also flag possible vector recombinations. We show that isling is up to 1.6-fold faster and up to 170% more accurate than other viral integration tools, and performs well on both simulated and real datasets. Isling is therefore an efficient and application-agnostic tool that will enable a broad range of investigations into viral and vector integration. These include comparisons between integrations of wild-type viruses and gene therapy vectors, as well as assessing the genotoxicity of vectors and understanding the role of viruses in cancer.

Exploring the functional effect of a non-synonymous coding variant at the protein level requires multiple pieces of information to be interpreted appropriately. This is particularly important when embarking on the study of a potentially pathogenic variant linked to a rare or monogenic disease. Whereas accurate protein stability predictions alone are generally informative, other effects, such as disruption of post-translational modifications or weakened ligand binding, may also contribute to the disease phenotype. Furthermore, consideration of nearby variants that are found in the healthy population may strengthen or refute a given mechanistic hypothesis. Whilst there are several bioinformatics tools available that score a genetic variant in terms of deleteriousness, there is no single tool that assembles multiple effects of a variant on the encoded protein, beyond structural stability, and presents them on the structure for inspection. Venus is a web application which, given a protein substitution, rapidly estimates the predicted effect on protein stability of the variant, flags if the variant affects a post-translational modification site, a predicted linear motif or known annotation, and determines the effect on protein stability of variants which affect nearby residues and have been identified in healthy populations. Venus is built upon Michelanglo and the results can be exported to it, allowing them to be annotated and shared with other researchers. Venus is freely accessible at https://venus.cmd.ox.ac.uk and its source code is openly available at https://github.com/CMD-Oxford/Michelanglo-and-Venus.

PubChem (https://pubchem.ncbi.nlm.nih.gov) is a public chemical database at the U.S. National Institutes of Health. Visited by millions of users every month, it plays a role as a key chemical information resource for biomedical research communities. Data in PubChem is from hundreds of contributors and organized into multiple collections by record type. Among these are the Protein, Gene, Pathway, and Taxonomy data collections. Records in these collections contain information on chemicals related to a given biological target (i.e., protein, gene, pathway, or taxon), helping users to analyze and interpret the biological activity data of molecules. In addition, annotations about the biological targets are collected from authoritative or curated data sources and integrated into the four collections. The content can be programmatically accessed through PubChem's web service interfaces (including PUG View). A machine-readable representation of this content is also provided within PubChemRDF.

Protein-carbohydrate interactions play an important role in several biological processes. The mutation of amino acid residues in carbohydrate-binding proteins may alter the binding affinity, affect the functions and lead to diseases. Elucidating the factors influencing the binding affinity change (ΔΔG) of protein-carbohydrate complexes upon mutation is a challenging task. In this work, we have collected the experimental data for the binding affinity change of 318 unique mutants and related with sequence and structural features of amino acid residues at the mutant sites. We found that accessible surface area, secondary structure, mutation preference, conservation score, hydrophobicity and contact energies are important to understand the binding affinity change upon mutation. We have developed multiple regression equations for predicting the binding affinity change upon mutation and our method showed an average correlation of 0.74 and a mean absolute error of 0.70 kcal/mol between experimental and predicted ΔΔG on a 10-fold cross-validation. Further, we have validated our method using an independent test data set of 124 (62 unique) mutations, which showed a correlation and MAE of 0.79 and 0.56 kcal/mol, respectively. We have developed a web server PCA-MutPred, Protein-CArbohydrate complex Mutation affinity Predictor, for predicting the change in binding affinity of protein-carbohydrate complexes and it is freely accessible at https://web.iitm.ac.in/bioinfo2/pcamutpred. We suggest that the method could be a useful resource for designing protein-carbohydrate complexes with desired affinities.

Expression of mRNA is often regulated by the binding of a small RNA (miRNA, snoRNA, siRNA). While the pairing contribution to the net free energy is well parameterized and can be computed in O(N) time, the cost of removing pre-existing mRNA secondary structure has not received sufficient attention. Conventional methods for computing the unfolding free energy of a target mRNA are costly, scaling like the cube of the number of target bases O(N3). Here we introduce a model to describe the unfolding costs of the binding site, which features surprisingly big differences in the free energy parameters for the four bases. The model is implemented in our O(N) algorithm, BindOligoNet. Donor splice site prediction is more accurate when using our calculation of spliceosomal U1-snRNA to mRNA net binding free energy. Our base-dependent free energies also correlate with efficient ribosome docking near the start codon.

The advent of single-cell sequencing is providing unprecedented opportunities to disentangle tissue complexity and investigate cell identities and functions. However, the analysis of single cell data is a challenging, multi-step process that requires both advanced computational skills and biological sensibility. When dealing with single cell RNA-seq (scRNA-seq) data, the presence of technical artifacts, noise, and biological biases imposes to first identify, and eventually remove, unreliable signals from low-quality cells and unwanted sources of variation that might affect the efficacy of subsequent downstream modules. Pre-processing and quality control (QC) of scRNA-seq data is a laborious process consisting in the manual combination of different computational strategies to quantify QC-metrics and define optimal sets of pre-processing parameters. Here we present popsicleR, a R package to interactively guide skilled and unskilled command line-users in the pre-processing and QC analysis of scRNA-seq data. The package integrates, into several main wrapper functions, methods derived from widely used pipelines for the estimation of quality-control metrics, filtering of low-quality cells, data normalization, removal of technical and biological biases, and for cell clustering and annotation. popsicleR starts from either the output files of the Cell Ranger pipeline from 10X Genomics or from a feature-barcode matrix of raw counts generated from any scRNA-seq technology. Open-source code, installation instructions, and a case study tutorial are freely available at https://github.com/bicciatolab/popsicleR.

The Protein Circular Dichroism Data Bank (PCDDB) [https://pcddb.cryst.bbk.ac.uk] is an established resource for the biological, biophysical, chemical, bioinformatics, and molecular biology communities. It is a freely-accessible repository of validated protein circular dichroism (CD) spectra and associated sample and metadata, with entries having links to other bioinformatics resources including, amongst others, structure (PDB), AlphaFold, and sequence (UniProt) databases, as well as to published papers which produced the data and cite the database entries. It includes primary (unprocessed) and final (processed) spectral data, which are available in both text and pictorial formats, as well as detailed sample and validation information produced for each of the entries. Recently the metadata content associated with each of the entries, as well as the number and structural breadth of the protein components included, have been expanded. The PCDDB includes data on both wild-type and mutant proteins, and because CD studies primarily examine proteins in solution, it also contains examples of the effects of different environments on their structures, plus thermal unfolding/folding series. Methods for both sequence and spectral comparisons are included. The data included in the PCDDB complement results from crystal, cryo-electron microscopy, NMR spectroscopy, bioinformatics characterisations and classifications, and other structural information available for the proteins via links to other databases. The entries in the PCDDB have been used for the development of new analytical methodologies, for interpreting spectral and other biophysical data, and for providing insight into structures and functions of individual soluble and membrane proteins and protein complexes.

Computational modeling of nucleic acids plays an important role in molecular biology, enhancing our general understanding of the relationship between structure and function. Biophysical studies have provided a wealth of information on how double-helical DNA responds to proteins and other molecules in its local environment but far less understanding of the larger scale structural responses found in protein-decorated loops and minicircles. Current computational models of DNA range from detailed all-atom molecular dynamics studies, which produce rich time and spatially dependent depictions of small DNA fragments, to coarse-grained simulations, which sacrifice detailed physical and chemical information to treat larger-scale systems. The treatment of DNA used here, at the base-pair step level with rigid-body parameters, allows one to develop models hundreds of base pairs long from local, sequence-specific features found from experiment. The emDNA software takes advantage of this framework, producing optimized structures of DNA at thermal equilibrium with built-in or user-generated elastic models. The program, in combination with the case studies included in this article, allows users of any skill level to develop and investigate mesoscale models of their own design. The functionality of emDNA includes a tool to incorporate experiment-specific configurations, e.g., protein-bound and/or melted DNA from known high-resolution structures, within higher-order 3D models by fixing the orientation and position of user-specified base pairs. The software provides a new avenue into multiscale genetic modeling, giving a wide range of users a deeper understanding of DNA mesoscale organization and the opportunity to pose new questions in genetic research. The publicly available emDNA software, including build instructions and usage information, is available on GitHub (https://nicocvn.github.io/emDNA/).

Protein mapping distributes many copies of different molecular probes on the surface of a target protein in order to determine binding hot spots, regions that are highly preferable for ligand binding. While mapping of X-ray structures by the FTMap server is inherently static, this limitation can be overcome by the simultaneous analysis of multiple structures of the protein. FTMove is an automated web server that implements this approach. From the input of a target protein, by PDB code, the server identifies all structures of the protein available in the PDB, runs mapping on them, and combines the results to form binding hot spots and binding sites. The user may also upload their own protein structures, bypassing the PDB search for similar structures. Output of the server consists of the consensus binding sites and the individual mapping results for each structure - including the number of probes located in each binding site, for each structure. This level of detail allows the users to investigate how the strength of a binding site relates to the protein conformation, other binding sites, and the presence of ligands or mutations. In addition, the structures are clustered on the basis of their binding properties. The use of FTMove is demonstrated by application to 22 proteins with known allosteric binding sites; the orthosteric and allosteric binding sites were identified in all but one case, and the sites were typically ranked among the top five. The FTMove server is publicly available at https://ftmove.bu.edu.