SARS-CoV-2 is the causative agent of COVID-19, the ongoing global pandemic. It has posed a worldwide challenge to human health as no effective treatment is currently available to combat the disease. Its severity has led to unprecedented collaborative initiatives for therapeutic solutions against COVID-19. Studies resorting to structure-based drug design for COVID-19 are plethoric and show good promise. Structural biology provides key insights into 3D structures, critical residues/mutations in SARS-CoV-2 proteins, implicated in infectivity, molecular recognition and susceptibility to a broad range of host species. The detailed understanding of viral proteins and their complexes with host receptors and candidate epitope/lead compounds is the key to developing a structure-guided therapeutic design. Since the discovery of SARS-CoV-2, several structures of its proteins have been determined experimentally at an unprecedented speed and deposited in the Protein Data Bank. Further, specialized structural bioinformatics tools and resources have been developed for theoretical models, data on protein dynamics from computer simulations, impact of variants/mutations and molecular therapeutics. Here, we provide an overview of ongoing efforts on developing structural bioinformatics tools and resources for COVID-19 research. We also discuss the impact of these resources and structure-based studies, to understand various aspects of SARS-CoV-2 infection and therapeutic development. These include (i) understanding differences between SARS-CoV-2 and SARS-CoV, leading to increased infectivity of SARS-CoV-2, (ii) deciphering key residues in the SARS-CoV-2 involved in receptor-antibody recognition, (iii) analysis of variants in host proteins that affect host susceptibility to infection and (iv) analyses facilitating structure-based drug and vaccine design against SARS-CoV-2.

PLOS Computational Biology launched its 'Topic Pages' project as a way to help fill important gaps in Wikipedia's coverage of computational biology content and to credit authors for their contributions.Topic Pages are written in the style of a Wikipedia article and are openly and publicly peer reviewed on the PLOS Wiki before being published in our PLOS journals, with a second, editable version posted to Wikipedia.Six years on, PLOS Computational Biology has published 11 Topic Pages covering a good range of subjects, from the Hypercycle to Approximate Bayesian Computation.The published articles have been widely viewed on Wikipedia as well as in the journal and well received by the community.We are welcoming submissions for further PLOS Computational Biology Topic Pages.We are looking for topics in computational biology that are of interest to our readership, the broader scientific community, and the public at large and that are not yet covered or insufficiently covered (i.e., exist as a 'stub') in Wikipedia.Last year, PLOS Genetics joined the Topic Pages initiative, as detailed in this blog post.We are also exploring how the Topic Pages approach could be extended to include Wikidata, the community-curated database connecting concepts covered in any Wikipedia article with the Semantic Web [1].For instance, data from more and more research-related databases are being integrated with Wikidata or its semantic core, Wikibase.This creates the need to formalize data models: How should concepts like a disease outbreak, a cell-cycle checkpoint, a sequencer, biomineralization, or a functional magnetic resonance imaging (fMRI) data set be modelled in Wikidata or Wikibase?Conversely, what workflows allow us to collect information about such concepts in Wikidata, to interlink it with related information, to validate it, and to keep it up to date?Or, how can the data from Wikidata be explored or put to use in other contexts relevant to computational biology?We are working on establishing the editorial workflows to handle such Wikidata-focused Topic Pages and would welcome submissions to test these waters.For some inspiration, we suggest taking a look at Wikidata-based tools for browsing microbial genomes [2], scholarly publications [3], or software and file formats [4].The Author Guidelines for Wikipedia-focused Topic Pages are available here.If you've noticed a gap in Wikipedia's coverage of particular computational biology topics, we want to hear from you! Please send ideas for Topic Pages to [email protected].

While there has been much debate about the coverage and quality of Wikipedia (starting with an article in 2005 [1]), there is no doubt about its value (and increasing role) as a reference source and starting point for in-depth research. For example, within the biomedical sciences, there have been recent articles about the accuracy and completeness of drug information in Wikipedia [2], Wikipedia as a source of information in nursing care [3] and mental disorders [4], and making biological databases available through Wikipedia [5].

MOTIVATION: Protein interaction networks contain a wealth of biological information, but their large size often hinders cross-organism comparisons. We present OrthoNets, a Cytoscape plugin that displays protein-protein interaction (PPI) networks from two organisms simultaneously, highlighting orthology relationships and aggregating several types of biomedical annotations. OrthoNets also allows PPI networks derived from experiments to be overlaid on networks extracted from public databases, supporting the identification and verification of new interactors. Any newly identified PPIs can be validated by checking whether their orthologs interact in another organism. AVAILABILITY: OrthoNets is freely available at http://wodaklab.org/orthonets/.

BACKGROUND: Genome scale data on protein interactions are generally represented as large networks, or graphs, where hundreds or thousands of proteins are linked to one another. Since proteins tend to function in groups, or complexes, an important goal has been to reliably identify protein complexes from these graphs. This task is commonly executed using clustering procedures, which aim at detecting densely connected regions within the interaction graphs. There exists a wealth of clustering algorithms, some of which have been applied to this problem. One of the most successful clustering procedures in this context has been the Markov Cluster algorithm (MCL), which was recently shown to outperform a number of other procedures, some of which were specifically designed for partitioning protein interactions graphs. A novel promising clustering procedure termed Affinity Propagation (AP) was recently shown to be particularly effective, and much faster than other methods for a variety of problems, but has not yet been applied to partition protein interaction graphs. RESULTS: In this work we compare the performance of the Affinity Propagation (AP) and Markov Clustering (MCL) procedures. To this end we derive an unweighted network of protein-protein interactions from a set of 408 protein complexes from S. cervisiae hand curated in-house, and evaluate the performance of the two clustering algorithms in recalling the annotated complexes. In doing so the parameter space of each algorithm is sampled in order to select optimal values for these parameters, and the robustness of the algorithms is assessed by quantifying the level of complex recall as interactions are randomly added or removed to the network to simulate noise. To evaluate the performance on a weighted protein interaction graph, we also apply the two algorithms to the consolidated protein interaction network of S. cerevisiae, derived from genome scale purification experiments and to versions of this network in which varying proportions of the links have been randomly shuffled. CONCLUSION: Our analysis shows that the MCL procedure is significantly more tolerant to noise and behaves more robustly than the AP algorithm. The advantage of MCL over AP is dramatic for unweighted protein interaction graphs, as AP displays severe convergence problems on the majority of the unweighted graph versions that we tested, whereas MCL continues to identify meaningful clusters, albeit fewer of them, as the level of noise in the graph increases. MCL thus remains the method of choice for identifying protein complexes from binary interaction networks.

MOTIVATION: Epistatic or genetic interactions, representing the effects of mutating one gene on the phenotypes caused by mutations in one or more distinct genes, can be very helpful for uncovering functional relationships between genes. Recently, the epistatic miniarray profiles (E-MAP) method has emerged as a powerful approach for identifying such interactions systematically. For E-MAP data analysis, hierarchical clustering is used to partition genes into groups on the basis of the similarity between their global interaction profiles, and the resulting descriptions assign each gene to only one group, thereby ignoring the multifunctional roles played by most genes. RESULTS: Here, we present the original local coherence detection (LCD) algorithm for identifying groups of functionally related genes from E-MAP data in a manner that allows individual genes to be assigned to more than one functional group. This enables investigation of the pleiotropic nature of gene function. The performance of our algorithm is illustrated by applying it to two E-MAP datasets and an E-MAP-like in silico dataset for the yeast Saccharomyces cerevisiae. In addition to recapitulating the majority of the functional modules and many protein complexes reported previously, our algorithm uncovers many recently documented and novel multifunctional relationships between genes and gene groups. Our algorithm hence represents a valuable tool for uncovering new roles for genes with annotated functions and for mapping groups of genes and proteins into pathways.

BACKGROUND: Most methods for predicting functional sites in protein 3D structures, rely on information on related proteins and cannot be applied to proteins with no known relatives. Another limitation of these methods is the lack of a well annotated set of functional sites to use as benchmark for validating their predictions. Experimental findings and theoretical considerations suggest that residues involved in function often contribute unfavorably to the native state stability. We examine the possibility of systematically exploiting this intrinsic property to identify functional sites using an original procedure that detects destabilizing regions in protein structures. In addition, to relate destabilizing regions to known functional sites, a novel benchmark consisting of a diverse set of hand-curated protein functional sites is derived. RESULTS: A procedure for detecting clusters of destabilizing residues in protein structures is presented. Individual residue contributions to protein stability are evaluated using detailed atomic models and a force-field successfully applied in computational protein design. The most destabilizing residues, and some of their closest neighbours, are clustered into destabilizing regions following a rigorous protocol. Our procedure is applied to high quality apo-structures of 63 unrelated proteins. The biologically relevant binding sites of these proteins were annotated using all available information, including structural data and literature curation, resulting in the largest hand-curated dataset of binding sites in proteins available to date. Comparing the destabilizing regions with the annotated binding sites in these proteins, we find that the overlap is on average limited, but significantly better than random. Results depend on the type of bound ligand. Significant overlap is obtained for most polysaccharide- and small ligand-binding sites, whereas no overlap is observed for most nucleic acid binding sites. These differences are rationalised in terms of the geometry and energetics of the binding site. CONCLUSION: We find that although destabilizing regions as detected here can in general not be used to predict binding sites in protein structures, they can provide useful information, particularly on the location of functional sites that bind polysaccharides and small ligands. This information can be exploited in methods for predicting function in protein structures with no known relatives. Our publicly available benchmark of hand-curated functional sites in proteins should help other workers derive and validate new prediction methods.

BACKGROUND: In structural genomics, an important goal is the detection and classification of protein-protein interactions, given the structures of the interacting partners. We have developed empirical energy functions to identify native structures of protein-protein complexes among sets of decoy structures. To understand the role of amino acid diversity, we parameterized a series of functions, using a hierarchy of amino acid alphabets of increasing complexity, with 2, 3, 4, 6, and 20 amino acid groups. Compared to previous work, we used the simplest possible functional form, with residue-residue interactions and a stepwise distance-dependence. We used increased computational resources, however, constructing 290,000 decoys for 219 protein-protein complexes, with a realistic docking protocol where the protein partners are flexible and interact through a molecular mechanics energy function. The energy parameters were optimized to correctly assign as many native complexes as possible. To resolve the multiple minimum problem in parameter space, over 64000 starting parameter guesses were tried for each energy function. The optimized functions were tested by cross validation on subsets of our native and decoy structures, by blind tests on series of native and decoy structures available on the Web, and on models for 13 complexes submitted to the CAPRI structure prediction experiment. RESULTS: Performance is similar to several other statistical potentials of the same complexity. For example, the CAPRI target structure is correctly ranked ahead of 90% of its decoys in 6 cases out of 13. The hierarchy of amino acid alphabets leads to a coherent hierarchy of energy functions, with qualitatively similar parameters for similar amino acid types at all levels. Most remarkably, the performance with six amino acid classes is equivalent to that of the most detailed, 20-class energy function. CONCLUSION: This suggests that six carefully chosen amino acid classes are sufficient to encode specificity in protein-protein interactions, and provide a starting point to develop more complicated energy functions.

MOTIVATION: Analyzing the networks of interactions between genes and proteins has become a central theme in systems biology. Versatile software tools for interactively displaying and analyzing these networks are therefore very much in demand. The public-domain open software environment Cytoscape has been developed with the goal of facilitating the design and development of such software tools by the scientific community. RESULTS: We present GenePro, a plugin to Cytoscape featuring a set of versatile tools that greatly facilitates the visualization and analysis of protein networks derived from high-throughput interactions data and the validation of various methods for parsing these networks into meaningful functional modules. AVAILABILITY: The GenePro plugin is available at the website http://genepro.ccb.sickkids.ca.

MOTIVATION: Several pattern discovery methods have been proposed to detect over-represented motifs in upstream sequences of co-regulated genes, and are for example used to predict cis-acting elements from clusters of co-expressed genes. The clusters to be analyzed are often noisy, containing a mixture of co-regulated and non-co-regulated genes. We propose a method to discriminate co-regulated from non-co-regulated genes on the basis of counts of pattern occurrences in their non-coding sequences. METHODS: String-based pattern discovery is combined with discriminant analysis to classify genes on the basis of putative regulatory motifs. RESULTS: The approach is evaluated by comparing the significance of patterns detected in annotated regulons (positive control), random gene selections (negative control) and high-throughput regulons (noisy data) from the yeast Saccharomyces cerevisiae. The classification is evaluated on the annotated regulons, and the robustness and rejection power is assessed with mixtures of co-regulated and random genes.

Biochemical pathways such as metabolic, regulatory or signal transduction pathways can be viewed as interconnected processes forming an intricate network of functional and physical interactions between molecular species in the cell. The amount of information available on such pathways for different organisms is increasing very rapidly. This is offering the possibility of performing various analyses on the structure of the full network of pathways for one organism as well as across different organisms, and has therefore generated interest in developing databases for storing and managing this information. Analysing these networks remains far from straightforward owing to the nature of the databases, which are often heterogeneous, incomplete or inconsistent. Pathway analysis is hence a challenging problem in systems biology and in bioinformatics. Various forms of data models have been devised for the analysis of biochemical pathways. This paper presents an overview of the types of models used for this purpose, concentrating on those concerned with the structural aspects of biochemical networks. In particular, the different types of data models found in the literature are classified using a unified framework. In addition, how these models have been used in the analysis of biochemical networks is described. This enables us to underline the strengths and weaknesses of the different approaches, as well as to highlight relevant future research directions.

MOTIVATION: Comparing the 3D structures of two proteins or analyzing the structural changes undergone by a protein upon ligand binding or when it crystallizes under different conditions, can be both tricky and tedious, especially when the two proteins are distantly related, or when the structural changes are complex. Readily accessible tools for performing these tasks automatically and reliably should therefore be welcome. RESULTS: We describe a web interface to several automatic procedures for performing pairwise structure superposition in a flexible manner, for detailed analyses of conformational changes and for displaying the results in a pictorial fashion. AVAILABILITY: This interface can be accessed at the Brussels and Cuba Web sites, respectively: http://www.ucmb.ulb.ac.be/SCMBB/Tools.htmland http://bio.cigb.edu.cu.

This paper describes how biological function can be represented in terms of molecular activities and processes. It presents several key features of a data model that is based on a conceptual description of the network of interactions between molecular entities within the cell and between cells. This model is implemented in the aMAZE database that presently deals with information on metabolic pathways, gene regulation, sub- or supracellular locations, and transport. It is shown that this model constitutes a useful generalisation of data representations currently implemented in metabolic pathway databases, and that it can furthermore include multiple schemes for categorising and classifying molecular entities, activities, processes and localisations. In particular, we highlight the flexibility offered by our system in representing multiple molecular activities and their control, in viewing biological function at different levels of resolution and in updating this view as our knowledge evolves.