Electronic Thesis/Dissertation


Germline and Somatic Non-Synonymous Single Nucleotide Variations in Drug Binding Sites Open Access

Advancements in next-generation sequencing (NGS) technologies is generating huge amount of data. However, the challenge of translating NGS data to actionable clinical interpretation remains. We have combined in a comprehensive manner, germline and somatic non-synonymous single-nucleotide variations (nsSNV) that impact drug binding sites to investigate the prevalence of such variations. The integrated data thus generated in conjunction with exome or whole-genome sequencing data 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. 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 them alter amino acid-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 datasets revealed 3,133 nsSNVs affecting amino acid-drug binding sites. Based on protein similarity and conservation of amino acid-drug binding sites, 81 paralogs were identified as additional proteins that can serve as alternative and potential drug targets. Information on these key drugs, their protein binding sites 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 ( http://hive.biochemistry.gwu.edu/tools/drugvar) where nsSNVs from individual patient can be checked by scanning against DrugVar to determine if any of the single-nucleotide variations affect the binding of any drug in the database.

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