Search

Abstract The COVID-19 disease is an ongoing global health concern. Although vaccination provides some protection, people are still susceptible to re-infection. Ostensibly, certain populations or clinical groups may be more vulnerable. Factors causing these differences are unclear and whilst socioeconomic and cultural differences are likely to be important, human genetic factors could influence susceptibility. Experimental studies indicate SARS-CoV-2 uses innate immune suppression as a strategy to speed-up entry and replication into the host cell. Therefore, it is necessary to understand the impact of variants in immunity-associated human proteins on susceptibility to COVID-19. In this work, we analysed missense coding variants in several SARS-CoV-2 proteins and their human protein interactors that could enhance binding affinity to SARS-CoV-2. We curated a dataset of 19 SARS-CoV-2: human protein 3D-complexes, from the experimentally determined structures in the Protein Data Bank and models built using AlphaFold2-multimer, and analysed the impact of missense variants occurring in the protein–protein interface region. We analysed 468 missense variants from human proteins and 212 variants from SARS-CoV-2 proteins and computationally predicted their impacts on binding affinities for the human viral protein complexes. We predicted a total of 26 affinity-enhancing variants from 13 human proteins implicated in increased binding affinity to SARS-CoV-2. These include key-immunity associated genes (TOMM70, ISG15, IFIH1, IFIT2, RPS3, PALS1, NUP98, AXL, ARF6, TRIMM, TRIM25) as well as important spike receptors (KREMEN1, AXL and ACE2). We report both common (e.g., Y13N in IFIH1) and rare variants in these proteins and discuss their likely structural and functional impact, using information on known and predicted functional sites. Potential mechanisms associated with immune suppression implicated by these variants are discussed. Occurrence of certain predicted affinity-enhancing variants should be monitored as they could lead to increased susceptibility and reduced immune response to SARS-CoV-2 infection in individuals/populations carrying them. Our analyses aid in understanding the potential impact of genetic variation in immunity-associated proteins on COVID-19 susceptibility and help guide drug-repurposing strategies.

A new protein domain classification protocol incorporating deep learning strategies for detecting sequence and structure similarities between domain is used to systematically study and analyse the predicted AlphaFold2 structural models for proteins of 21 organisms.

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 affecting transmissibility, disease severity and re-infection risk. Studies suggest that the - N-terminal domain (NTD) of the spike protein may have a role in facilitating virus entry via sialic-acid receptor binding. Furthermore, most VOCs include novel NTD variants. Despite global sequence and structure similarity, most sialic-acid binding pockets in NTD vary across coronaviruses. Our work suggests ongoing evolutionary tuning of the sugar-binding pockets and recent analyses have shown that NTD insertions in VOCs tend to lie close to loops.We extended the structural characterisation of these sugar-binding pockets and explored whether variants could enhance sialic acid-binding. 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 and Theta variants) frequently lie close to 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. These binding observations are supported by molecular dynamics simulations (MD).We examined the similarity of NTD across Betacoronaviruses to determine whether the sugar-binding pockets are sufficiently similar to be exploited in drug design. Whilst most pockets are too structurally variable, we detected a previously unknown highly structurally conserved pocket which can be investigated in pursuit of a generic pan-Betacoronavirus drug. Our structure-based analyses help rationalise the effects of VOCs and provide hypotheses for experiments. Our findings suggest a strong need for experimental monitoring of changes in NTD of VOCs.

Protein kinases are important targets for treating human disorders, and they are the second most targeted families after G-protein coupled receptors. Several resources provide classification of kinases into evolutionary families (based on sequence homology); however, very few systematically classify functional families (FunFams) comprising evolutionary relatives that share similar functional properties. We have developed the FunFam-MARC (Multidomain ARchitecture-based Clustering) protocol, which uses multi-domain architectures of protein kinases and specificity-determining residues for functional family classification. FunFam-MARC predicts 2210 kinase functional families (KinFams), which have increased functional coherence, in terms of EC annotations, compared to the widely used KinBase classification. Our protocol provides a comprehensive classification for kinase sequences from >10,000 organisms. We associate human KinFams with diseases and drugs and identify 28 druggable human KinFams, i.e., enriched in clinically approved drugs. Since relatives in the same druggable KinFam tend to be structurally conserved, including the drug-binding site, these KinFams may be valuable for shortlisting therapeutic targets. Information on the human KinFams and associated 3D structures from AlphaFold2 are provided via our CATH FTP website and Zenodo. This gives the domain structure representative of each KinFam together with information on any drug compounds available. For 32% of the KinFams, we provide information on highly conserved residue sites that may be associated with specificity.

Computational Saturation Mutagenesis is an in-silico approach that employs systematic mutagenesis of each amino acid residue in the protein to all other amino acid types, and predicts changes in thermodynamic stability and affinity to the other subunits/protein counterparts, ligands and nucleic acid molecules. The data thus generated are useful in understanding the functional consequences of mutations in antimicrobial resistance phenotypes. In this study, we applied computational saturation mutagenesis to three important drug-targets in Mycobacterium leprae (M. leprae) for the drugs dapsone, rifampin and ofloxacin namely Dihydropteroate Synthase (DHPS), RNA Polymerase (RNAP) and DNA Gyrase (GYR), respectively. M. leprae causes leprosy and is an obligate intracellular bacillus with limited protein structural information associating mutations with phenotypic resistance outcomes in leprosy. Experimentally solved structures of DHPS, RNAP and GYR of M. leprae are not available in the Protein Data Bank, therefore, we modelled the structures of these proteins using template-based comparative modelling and introduced systematic mutations in each model generating 80,902 mutations and mutant structures for all the three proteins. Impacts of mutations on stability and protein-subunit, protein-ligand and protein-nucleic acid affinities were computed using various in-house developed and other published protein stability and affinity prediction software. A consensus impact was estimated for each mutation using qualitative scoring metrics for physicochemical properties and by a categorical grouping of stability and affinity predictions. We developed a web database named HARP (a database of Hansen's Disease Antimicrobial Resistance Profiles), which is accessible at the URL - https://harp-leprosy.org and provides the details to each of these predictions.

ABSTRACTOf 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.

Background Dengue is one of the most common arboviral diseases prevalent worldwide and is caused by Dengue viruses (genus Flavivirus, family Flaviviridae). There are four serotypes of Dengue Virus (DENV-1 to DENV-4), each of which is further subdivided into distinct genotypes. DENV-2 is frequently associated with severe dengue infections and epidemics. DENV-2 consists of six genotypes such as Asian/American, Asian I, Asian II, Cosmopolitan, American and sylvatic. Comparative genomic study was carried out to infer population structure of DENV-2 and to analyze the role of evolutionary and spatiotemporal factors in emergence of diversifying lineages. Methods Complete genome sequences of 990 strains of DENV-2 were analyzed using Bayesian-based population genetics and phylogenetic approaches to infer genetically distinct lineages. The role of spatiotemporal factors, genetic recombination and selection pressure in the evolution of DENV-2 is examined using the sequence-based bioinformatics approaches. Results DENV-2 genetic structure is complex and consists of fifteen subpopulations/lineages. The Asian/American genotype is observed to be diversified into seven lineages. The Asian I, Cosmopolitan and sylvatic genotypes were found to be subdivided into two lineages, each. The populations of American and Asian II genotypes were observed to be homogeneous. Significant evidence of episodic positive selection was observed in all the genes, except NS4A. Positive selection operational on a few codons in envelope gene confers antigenic and lineage diversity in the American strains of Asian/American genotype. Selection on codons of non-structural genes was observed to impact diversification of lineages in Asian I, cosmopolitan and sylvatic genotypes. Evidence of intra/inter-genotype recombination was obtained and the uncertainty in classification of recombinant strains was resolved using the population genetics approach. Discussion Complete genome-based analysis revealed that the worldwide population of DENV-2 strains is subdivided into fifteen lineages. The population structure of DENV-2 is spatiotemporal and is shaped by episodic positive selection and recombination. Intra-genotype diversity was observed in four genotypes (Asian/American, Asian I, cosmopolitan and sylvatic). Episodic positive selection on envelope and non-structural genes translates into antigenic diversity and appears to be responsible for emergence of strains/lineages in DENV-2 genotypes. Understanding of the genotype diversity and emerging lineages will be useful to design strategies for epidemiological surveillance and vaccine design.

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.

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.