Protein functional annotation by homology

Impact of germline and somatic missense variations on drug binding sites

Advancements in next-generation sequencing (NGS) technologies are generating a vast amount of data. This exacerbates the current challenge of translating NGS data into actionable clinical interpretations. We have comprehensively combined germline and somatic nonsynonymous single-nucleotide variations (nsSNVs) that affect drug binding sites in order to investigate their prevalence. The integrated data thus generated in conjunction with exome or whole-genome sequencing can be used to identify patients who may not respond to a specific drug because of alterations in drug binding efficacy due to nsSNVs in the target protein's gene. To identify the nsSNVs that may affect drug binding, protein–drug complex structures were retrieved from Protein Data Bank (PDB) followed by identification of amino acids in the protein–drug binding sites using an occluded surface method. Then, the germline and somatic mutations were mapped to these amino acids to identify which of these alter protein–drug binding sites. Using this method we identified 12 993 amino acid–drug binding sites across 253 unique proteins bound to 235 unique drugs. The integration of amino acid–drug binding sites data with both germline and somatic nsSNVs data sets revealed 3133 nsSNVs affecting amino acid–drug binding sites. In addition, a comprehensive drug target discovery was conducted based on protein structure similarity and conservation of amino acid–drug binding sites. Using this method, 81 paralogs were identified that could serve as alternative drug targets. In addition, non-human mammalian proteins bound to drugs were used to identify 142 homologs in humans that can potentially bind to drugs. In the current protein–drug pairs that contain somatic mutations within their binding site, we identified 85 proteins with significant differential gene expression changes associated with specific cancer types. Information on protein–drug binding predicted drug target proteins and prevalence of both somatic and germline nsSNVs that disrupt these binding sites can provide valuable knowledge for personalized medicine treatment. A web portal is available where nsSNVs from individual patient can be checked by scanning against DrugVar to determine whether any of the SNVs affect the binding of any drug in the database.

Structure-function analysis of hepatitis C virus envelope glycoproteins E1 and E2

Hepatitis C virus (HCV) is the leading cause of chronic liver disease in humans. The envelope proteins of HCV are potential candidates for vaccine development. The absence of three-dimensional (3D) structures for the functional domain of HCV envelope proteins [E1.E2] monomer complex has hindered overall understanding of the virus infection, and also structure-based drug design initiatives. In this study, we report a 3D model containing both E1 and E2 proteins of HCV using the recently published structure of the core domain of HCV E2 and the functional part of E1, and investigate immunogenic implications of the model. HCV [E1.E2] molecule is modeled by using aa205-319 of E1 to aa421-716 of E2. Published experimental data were used to further refine the [E1.E2] model. Based on the model, we predict 77 exposed residues and several antigenic sites within the [E1.E2] that could serve as vaccine epitopes. This study identifies eight peptides which have antigenic propensity and have two or more sequentially exposed amino acids and 12 singular sites are under negative selection pressure that can serve as vaccine or therapeutic targets. Our special interest is 285FLVGQLFTFSPRRHW299 which has five negatively selected sites (L286, V287, G288, T292, and G303) with three of them sequential and four amino acids exposed (F285, L286, T292, and R296). This peptide in the E1 protein maps to dengue envelope vaccine target identified previously by our group. Our model provides for the first time an overall view of both the HCV envelope proteins thereby allowing researchers explore structure-based drug design approaches.