Supporting precision medicine by data mining across multi-disciplines: an integrative approach for generating comprehensive linkages between single nucleotide variants (SNVs) and drug-binding sites

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Abstract

Motivation:

Genetic variants in drug targets and metabolizing enzymes often have important functional implications, including altering the efficacy and toxicity of drugs. Identifying single nucleotide variants (SNVs) that contribute to differences in drug response and understanding their underlying mechanisms are fundamental to successful implementation of the precision medicine model. This work reports an effort to collect, classify and analyze SNVs that may affect the optimal response to currently approved drugs.

Results:

An integrated approach was taken involving data mining across multiple information resources including databases containing drugs, drug targets, chemical structures, protein-ligand structure complexes, genetic and clinical variations as well as protein sequence alignment tools. We obtained 2640 SNVs of interest, most of which occur rarely in populations (minor allele frequency < 0.01). Clinical significance of only 9.56% of the SNVs is known in ClinVar, although 79.02% are predicted as deleterious. The examples here demonstrate that even if the mapped SNVs predicted as deleterious may not result in significant structural modifications, they can plausibly modify the protein-drug interactions, affecting selectivity and drug-binding affinity. Our analysis identifies potentially deleterious SNVs present on drug-binding residues that are relevant for further studies in the context of precision medicine.

Availability and Implementation:

Data are available from Supplementary information file.

Contact:

yanli.wang@nih.gov

Supplementary information:

Supplementary Tables S1-S5 are available at Bioinformatics online.

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