Impact of germline and somatic missense variations on drug binding sites

Abstract

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.

Expanded Analysis of Secondary Germline Findings From Matched Tumor/Normal Sequencing Identifies Additional Clinically Significant Mutations

“…Although the clinical utility of germline LPV/ PV in some genes outside the ACMG list is still unknown, rapid advances of biologic knowledge and new drug development could aid in identifying new actionable alterations to serve as the basis for future clinical trial design and personalized cancer therapy. 36,37 Increasing patient interest exists in knowing about secondary germline findings; however, which results should be returned remains controversial. In this study, a committee of oncologists, a genetic counselor, molecular pathologists, an ethicist, and behavioral scientists believed that in the absence of significant cancer family history, the clinical utility of pathogenic secondary germline findings in moderate penetrance genes like CHEK2 remains with many uncertainties and knowledge gaps; thus, moderate penetrance genes were not returned.…”

In silico analysis of PFN1 related to amyotrophic lateral sclerosis

“…Thus, key amino-acid residues in proteins are essential for maintaining the structural properties of binding sites and for the formation of non-covalent interactions with drug molecules [24]. In this sense, nsSNVs affecting key protein residues can impact drug binding-sites, resulting in alterations in drug binding affinity and selectivity [26].…”

Global spectrum of population‐specific common missense variation in cytochrome P450 pharmacogenes

“…In this respect, tools for mapping, visualization, and interpretation of nonsynonymous SNVs in protein–drug complexes ‐ including proteins for which high‐resolution crystal structures are unavailable ‐ are needed. This has been achieved at a smaller scale for influenza viral proteins (FluSurver, https://flusurver.bii.a-star.edu.sg/) (Lackenby et al, 2018) and available drug‐binding proteins structures (DrugVar) (Yan et al, 2017). Notwithstanding the improved accuracy of these PGx‐specific prediction algorithms, all suspected functional variants should be validated using in vitro CYP assays (Walsky & Obach, 2004) and via in vivo studies, taking into account variant‐specific, substrate‐dependent effects (Marcath et al, 2019) where possible.…”