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Understanding the mechanisms of protein function is indispensable for many biological applications, such as protein engineering and drug design. However, experimental annotations are sparse, and therefore, theoretical strategies are needed to fill the gap. Here, we present the latest developments in building functional subclassifications of protein superfamilies and using evolutionary conservation to detect functional determinants, for example, catalytic-, binding- and specificity-determining residues important for delineating the functional families. We also briefly review other features exploited for functional site detection and new machine learning strategies for combining multiple features.

Databases of three-dimensional structures of proteins (and their associated molecules) provide: (a) Curated repositories of coordinates of experimentally determined structures, including extensive metadata; for instance information about provenance, details about data collection and interpretation, and validation of results. (b) Information-retrieval tools to allow searching to identify entries of interest and provide access to them. (c) Links among databases, especially to databases of amino-acid and genetic sequences, and of protein function; and links to software for analysis of amino-acid sequence and protein structure, and for structure prediction. (d) Collections of predicted three-dimensional structures of proteins. These will become more and more important after the breakthrough in structure prediction achieved by AlphaFold2. The single global archive of experimentally determined biomacromolecular structures is the Protein Data Bank (PDB). It is managed by wwPDB, a consortium of five partner institutions: the Protein Data Bank in Europe (PDBe), the Research Collaboratory for Structural Bioinformatics (RCSB), the Protein Data Bank Japan (PDBj), the BioMagResBank (BMRB), and the Electron Microscopy Data Bank (EMDB). In addition to jointly managing the PDB repository, the individual wwPDB partners offer many tools for analysis of protein and nucleic acid structures and their complexes, including providing computer-graphic representations. Their collective and individual websites serve as hubs of the community of structural biologists, offering newsletters, reports from Task Forces, training courses, and "helpdesks," as well as links to external software.Many specialized projects are based on the information contained in the PDB. Especially important are SCOP, CATH, and ECOD, which present classifications of protein domains.

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.

Coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 is an ongoing pandemic that causes significant health/socioeconomic burden. Variants of concern (VOCs) have emerged which may affect transmissibility, disease severity and re-infection risk. Most studies focus on the receptor-binding domain (RBD) of the Spike protein. However, some studies suggest that the Spike N-terminal domain (NTD) may have a role in facilitating virus entry via sialic-acid receptor binding. Furthermore, most VOCs include novel NTD variants. Recent analyses demonstrated that NTD insertions in VOCs tend to lie close to loop regions likely to be involved in binding sialic acids. We extended the structural characterisation of these putative sugar binding pockets and explored whether variants could enhance the binding to sialic acids and therefore to the host membrane, thereby contributing to increased transmissibility. We found that recent NTD insertions in VOCs (i.e., Gamma, Delta and Omicron variants) and emerging variants of interest (VOIs) (i.e., Iota, Lambda, Theta variants) frequently lie close to known and putative sugar-binding pockets. For some variants, including the recent Omicron VOC, we find increases in predicted sialic acid binding energy, compared to the original SARS-CoV-2, which may contribute to increased transmission. We examined the similarity of NTD across a range of related Betacoronaviruses to determine whether the putative sugar-binding pockets are sufficiently similar to be exploited in drug design. Despite global sequence and structure similarity, most sialic-acid binding pockets of NTD vary across related coronaviruses. Typically, SARS-CoV-2 possesses additional loops in these pockets that increase contact with polysaccharides. Our work suggests ongoing evolutionary tuning of the sugar-binding pockets in the virus. Whilst three of the pockets are too structurally variable to be amenable to pan Betacoronavirus drug design, we detected a fourth pocket that is highly structurally conserved and could therefore be investigated in pursuit of a generic drug. Our structure-based analyses help rationalise the effects of VOCs and provide hypotheses for experiments. For example, the Omicron variant, which has increased binding to sialic acids in pocket 3, has a rather unique insertion near pocket 3. Our work suggests a strong need for experimental monitoring of VOC changes in NTD.

CATH (https://www.cathdb.info) identifies domains in protein structures from wwPDB and classifies these into evolutionary superfamilies, thereby providing structural and functional annotations. There are two levels: CATH-B, a daily snapshot of the latest domain structures and superfamily assignments, and CATH+, with additional derived data, such as predicted sequence domains, and functionally coherent sequence subsets (Functional Families or FunFams). The latest CATH+ release, version 4.3, significantly increases coverage of structural and sequence data, with an addition of 65,351 fully-classified domains structures (+15%), providing 500 238 structural domains, and 151 million predicted sequence domains (+59%) assigned to 5481 superfamilies. The FunFam generation pipeline has been re-engineered to cope with the increased influx of data. Three times more sequences are captured in FunFams, with a concomitant increase in functional purity, information content and structural coverage. FunFam expansion increases the structural annotations provided for experimental GO terms (+59%). We also present CATH-FunVar web-pages displaying variations in protein sequences and their proximity to known or predicted functional sites. We present two case studies (1) putative cancer drivers and (2) SARS-CoV-2 proteins. Finally, we have improved links to and from CATH including SCOP, InterPro, Aquaria and 2DProt.

SARS-CoV-2 has a zoonotic origin and was transmitted to humans via an undetermined intermediate host, leading to infections in humans and other mammals. To enter host cells, the viral spike protein (S-protein) binds to its receptor, ACE2, and is then processed by TMPRSS2. Whilst receptor binding contributes to the viral host range, S-protein:ACE2 complexes from other animals have not been investigated widely. To predict infection risks, we modelled S-protein:ACE2 complexes from 215 vertebrate species, calculated changes in the energy of the complex caused by mutations in each species, relative to human ACE2, and correlated these changes with COVID-19 infection data. We also analysed structural interactions to better understand the key residues contributing to affinity. We predict that mutations are more detrimental in ACE2 than TMPRSS2. Finally, we demonstrate phylogenetically that human SARS-CoV-2 strains have been isolated in animals. Our results suggest that SARS-CoV-2 can infect a broad range of mammals, but few fish, birds or reptiles. Susceptible animals could serve as reservoirs of the virus, necessitating careful ongoing animal management and surveillance.

Genome3D consortium is a collaborative project involving protein structure prediction and annotation resources developed by six world-leading structural bioinformatics groups, based in the United Kingdom (namely Blundell, Murzin, Gough, Sternberg, Orengo, and Jones). The main objective of Genome3D serves as a common portal to provide both predicted models and annotations of proteins in model organisms, using several resources developed by these labs such as CATH-Gene3D, DOMSERF, pDomTHREADER, PHYRE, SUPERFAMILY, FUGUE/TOCATTA, and VIVACE. These resources primarily use SCOP- and/or CATH-based protein domain assignments. Another objective of Genome3D is to compare structural classifications of protein domains in CATH and SCOP databases and to provide a consensus mapping of CATH and SCOP protein superfamilies. CATH/SCOP mapping analyses led to the identification of total of 1429 consensus superfamilies.Currently, Genome3D provides structural annotations for ten model organisms, including Homo sapiens, Arabidopsis thaliana, Mus musculus, Escherichia coli, Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, Plasmodium falciparum, Staphylococcus aureus, and Schizosaccharomyces pombe. Thus, Genome3D serves as a common gateway to each structure prediction/annotation resource and allows users to perform comparative assessment of the predictions. It, thus, assists researchers to broaden their perspective on structure/function predictions of their query protein of interest in selected model organisms.

Genome3D consortium is a collaborative project involving protein structure prediction and annotation resources developed by six world-leading structural bioinformatics groups, based in the United Kingdom (namely Blundell, Murzin, Gough, Sternberg, Orengo, and Jones). The main objective of Genome3D serves as a common portal to provide both predicted models and annotations of proteins in model organisms, using several resources developed by these labs such as CATH-Gene3D, DOMSERF, pDomTHREADER, PHYRE, SUPERFAMILY, FUGUE/TOCATTA, and VIVACE. These resources primarily use SCOP- and/or CATH-based protein domain assignments. Another objective of Genome3D is to compare structural classifications of protein domains in CATH and SCOP databases and to provide a consensus mapping of CATH and SCOP protein superfamilies. CATH/SCOP mapping analyses led to the identification of total of 1429 consensus superfamilies.Currently, Genome3D provides structural annotations for ten model organisms, including Homo sapiens, Arabidopsis thaliana, Mus musculus, Escherichia coli, Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, Plasmodium falciparum, Staphylococcus aureus, and Schizosaccharomyces pombe. Thus, Genome3D serves as a common gateway to each structure prediction/annotation resource and allows users to perform comparative assessment of the predictions. It, thus, assists researchers to broaden their perspective on structure/function predictions of their query protein of interest in selected model organisms.

Of the more than 190 distinct species of Mycobacterium genus, many are economically and clinically important pathogens of humans or animals. Among those mycobacteria that infect humans, three species namely Mycobacterium tuberculosis (causative agent of tuberculosis), Mycobacterium leprae (causative agent of leprosy) and Mycobacterium abscessus (causative agent of chronic pulmonary infections) pose concern to global public health. Although antibiotics have been successfully developed to combat each of these, the emergence of drug-resistant strains is an increasing challenge for treatment and drug discovery. Here we describe the impact of the rapid expansion of genome sequencing and genome/pathway annotations that have greatly improved the progress of structure-guided drug discovery. We focus on the applications of comparative genomics, metabolomics, evolutionary bioinformatics and structural proteomics to identify potential drug targets. The opportunities and challenges for the design of drugs for M. tuberculosis, M. leprae and M. abscessus to combat resistance are discussed.

Three related mycobacteria are the cause of widespread infections in man and are the focus of intense research and drug-discovery efforts in the face of growing antimicrobial resistance.Mycobacterium tuberculosis, the causative agent of tuberculosis, is currently one of the top ten causes of death in the world according to WHO;M.abscessus, a group of non-tuberculous mycobacteria causes lung infections and other opportunistic infections in humans; andM.leprae, the causative agent of leprosy, remains endemic in tropical countries. There is an urgent need to design alternatives to conventional treatment strategies, due to the increase in resistance to standard antibacterials. In this study, we present a comparative analysis of chokepoint and essentiality datasets that will provide insight into the development of new treatment regimes. We illustrate the key metabolic pathways shared between these three organisms and identify drug targets with a wide metabolic impact that are common to the three species. We demonstrate that 72% of the chokepoint enzymes are proteins essential toMycobacterium tuberculosis. We show also that 78% of the drug targets, prioritized based on their presence in multiple paths on the metabolic network, are present in pathways shared byM. tuberculosis, M.lepraeandM.abscessus, including biosynthesis of amino acids, carbohydrates, cell structures, fatty acid and lipid biosynthesis. A further 17% is found in the prioritised pathways shared betweenM. tuberculosisandM.abscessus. We have performed comparative structure modelling of potential drug targets identified using our analysis in order to assess druggability and demonstrate the importance of chokepoint analysis in terms of drug target identification.AUTHOR SUMMARYComputer simulation studies to design new drugs against mycobacteria

We report synthesis of hydrogenated nanocrystalline silicon (nc-Si:H) thin films by using conventional plasma enhanced chemical vapor deposition (PE-CVD) system from gas mixture of pure silane (SiH4) and hydrogen (H2). We investigated the effect of RF power on structural, optical and electrical properties using various characterization techniques including Raman spectroscopy, FTIR spectroscopy, UV–visible spectroscopy etc. Low angle XRD and Raman spectroscopy analysis revealed that the RF power in PE-CVD is a critical process parameter to induce nanocrystallization in Si:H films. The FTIR spectroscopy analysis results indicate that with increase in RF power the predominant hydrogen bonding in films shifts from Si–H to Si–H2 and (Si–H2)n bonded species bonded species. However, the bonded hydrogen content didn’t show particular trend with change in RF power. The UV–visible spectroscopy analysis shows that the band tail width (E04–ETauc) with increase in RF power. The defect density and Urbach energy also increases with increase in RF power. The highest dark conductivity (and lowest charge carrier activation energy) was obtained for the film deposited at RF power of 125 W indicating that 125 W is optimized RF power of our PE-CVD unit. At this optimized RF power nc-Si:H films with crystallite size ~3.7 nm having good degree of crystallinity (~86.7 %) and high band gap (ETauc ~ 2.01 eV and E04 ~ 2.58 eV) were obtained with a low hydrogen content (6.2 at.%) at moderately high deposition rate (0.24 nm/s).

The spread of dengue disease has become a global public health concern. Dengue is caused by dengue virus, which is a mosquito-borne arbovirus of the genus Flavivirus, family Flaviviridae. There are four dengue virus serotypes (1-4), each of which is known to trigger mild to severe disease. Dengue virus serotype 4 (DENV-4) has four genotypes and is increasingly being reported to be re-emerging in various parts of the world. Therefore, the population structure and factors shaping the evolution of DENV-4 strains across the world were studied using genome-based population genetic, phylogenetic and selection pressure analysis methods. The population genomics study helped to reveal the spatiotemporal structure of the DENV-4 population and its primary division into two spatially distinct clusters: American and Asian. These spatial clusters show further time-dependent subdivisions within genotypes I and II. Thus, the DENV-4 population is observed to be stratified into eight genetically distinct lineages, two of which are formed by American strains and six of which are formed by Asian strains. Episodic positive selection was observed in the structural (E) and non-structural (NS2A and NS3) genes, which appears to be responsible for diversification of Asian lineages in general and that of modern lineages of genotype I and II in particular. In summary, the global DENV-4 population is stratified into eight genetically distinct lineages, in a spatiotemporal manner with limited recombination. The significant role of adaptive evolution in causing diversification of DENV-4 lineages is discussed. The evolution of DENV-4 appears to be governed by interplay between spatiotemporal distribution, episodic positive selection and intra/inter-genotype recombination.

Dengue virus serotype 3 (DENV-3), one of the four serotypes of Dengue viruses, is geographically diverse. There are five distinct genotypes (I-V) of DENV-3. Emerging strains and lineages of DENV-3 are increasingly being reported. Availability of genomic data for DENV-3 strains provides opportunity to study its population structure. Complete genome sequences are available for 860 strains of four genotypes (I, II, III and V) isolated worldwide and were analyzed using population genetics and evolutionary approaches to map landscape of genomic diversity. DENV-3 population is observed to be stratified into five major subpopulations. Genotype I and II formed independent subpopulations while genotype III is subdivided into three subpopulations (GIII-a, GIII-b and GIII-c) and is therefore heterogeneous. Genotypes I, II and GIII-a subpopulations comprise of Asian strains whereas GIII-c comprises of American strains. GIII-b subpopulation includes mainly of American strains along with a few strains from Sri Lanka. Genetic admixture is predominantly observed in Sri Lankan strains of genotype III and all strains of genotype V. Inter-genotype recombination was observed to occur in non-structural region of several Asian strains whereas extent of recombination was limited in American strains. Significant positive selection was found to be operational on all genes and observed to be the main driving force of genetic diversity. Positive selection was strongly operational on the branches leading to Asian genotypes and helped to delineate the genetic differences between Asian and American lineages. Thus, inter-genotype recombination, migration and adaptive evolution are the major determinants of evolution of DENV-3.

Rhinoviruses (RV) are increasingly being reported to cause mild to severe infections of respiratory tract in humans. RV are antigenically the most diverse species of the genus Enterovirus and family Picornaviridae. There are three species of RV (RV-A, -B and -C), with 80, 32 and 55 serotypes/types, respectively. Antigenic variation is the main limiting factor for development of a cross-protective vaccine against RV.Serotyping of Rhinoviruses is carried out using cross-neutralization assays in cell culture. However, these assays become laborious and time-consuming for the large number of strains. Alternatively, serotyping of RV is carried out by alignment-based phylogeny of both protein and nucleotide sequences of VP1. However, serotyping of RV based on alignment-based phylogeny is a multi-step process, which needs to be repeated every time a new isolate is sequenced. In view of the growing need for serotyping of RV, an alignment-free method based on "return time distribution" (RTD) of amino acid residues in VP1 protein has been developed and implemented in the form of a web server titled RV-Typer. RV-Typer accepts nucleotide or protein sequences as an input and computes return times of di-peptides (k = 2) to assign serotypes. The RV-Typer performs with 100% sensitivity and specificity. It is significantly faster than alignment-based methods. The web server is available at http://bioinfo.net.in/RV-Typer/home.html.

Resistance to azole antifungals is a significant problem in Candida albicans. An understanding of resistance at molecular level is essential for the development of strategies to tackle resistance and rationale design of newer antifungals and target-based molecular approaches. This study presents the first evaluation of molecular mechanisms associated with fluconazole resistance in clinical C.albicans isolates from India. Target site (ERG11) alterations were determined by DNA sequencing, whereas real-time PCRs were performed to quantify target and efflux pump genes (CDR1, CDR2, MDR1) in 87 [Fluconazole susceptible (n = 30), susceptible-dose dependent (n = 30) and resistant (n = 27)] C.albicans isolates. Cross-resistance to fluconazole, ketoconazole and itraconazole was observed in 74.1% isolates. Six amino acid substitutions were identified, including 4 (E116D, F145L, E226D, I437V) previously reported ones and 2 (P406L, Q474H) new ones. CDR1 over-expression was seen in 77.7% resistant isolates. CDR2 was exclusively expressed with CDR1 and their concomitant over-expression was associated with azole cross-resistance. MDR1 and ERG11 over-expression did not seem to be associated with resistance. Our results show that drug efflux mediated by Adenosine-5'-triphosphate (ATP)-binding cassette transporters, especially CDR1 is the predominant mechanism of fluconazole resistance and azole cross-resistance in C. albicans and indicate the need for research directed towards developing strategies to tackle efflux mediated resistance to salvage azoles.

Rhinoviruses, formerly known as Human rhinoviruses, are the most common cause of air-borne upper respiratory tract infections in humans. Rhinoviruses belong to the family Picornaviridae and are divided into three species namely, Rhinovirus A, -B and -C, which are antigenically diverse. Genetic recombination is found to be one of the important causes for diversification of Rhinovirus species. Although emerging lineages within Rhinoviruses have been reported, their population structure has not been studied yet. The availability of complete genome sequences facilitates study of population structure, genetic diversity and underlying evolutionary forces, such as mutation, recombination and selection pressure. Analysis of complete genomes of Rhinoviruses using a model-based population genetics approach provided a strong evidence for existence of seven genetically distinct subpopulations. As a result of diversification, Rhinovirus A and -C populations are divided into four and two subpopulations, respectively. Genetically, the Rhinovirus B population was found to be homogeneous. Intra-species recombination was observed to be prominent in Rhinovirus A and -C species. Significant evidence of episodic positive selection was obtained for several sites within coding sequences of structural and non-structural proteins. This corroborates well with known phenotypic properties such as antigenicity of structural proteins. Episodic positive selection appears to be responsible for emergence of new lineages especially in Rhinovirus A. In summary, the Rhinovirus population is an ensemble of seven distinct lineages. In case of Rhinovirus A, intra-species recombination and episodic positive selection contribute to its further diversification. In case of Rhinovirus C, intra- and inter-species recombinations are responsible for observed diversity. Population genetics approach was further useful to analyze phylogenetic tree topologies pertaining to recombinant strains, especially when trees are derived using complete genomes. Understanding of population structure serves as a foundation for designing new vaccines and drugs as well as to explain emergence of drug resistance amongst subpopulations.