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.

Figure 1

Workflow for mapping nonsynonymous single-nucleotide variations (nsSNVs) on protein–drug binding sites. ATC, anatomical therapeutic chemical classification; PDB, protein data bank.

Figure 2

Distribution of binding sites and binding sites affecting single-nucleotide variations (SNVs) across 253 drug target proteins. The blue bar indicates the ratio between number of drug binding sites and target protein length, whereas the red bar shows the ratio between number of binding sites affecting SNVs and binding sites.

Figure 3

DrugVar website browser interface. Users can perform searches using Protein Data Bank (PDB) IDs, gene names, UniProtKB accessions and drug names or identifiers.

Figure 4

Circos plot representing the binding connections between 25 antineoplastic agents and their target proteins. Proteins are presented with gene names. Ribbon colors are assigned for visualization purposes and the ribbon width indicates the number of target proteins.

Figure 5

Structural view of protein–drug interactions. (a) Superposition (c-alpha atoms) of imatinib binding to eight target protein X-ray structures (ABL1, LCK, KIT, NQO2, ABL2, SYK, DDR1 and MAPK14). The superimposed protein structures are colored. The blue to red color represents low to high conservation. The ligand is shown bound to protein pockets. (b) Imatinib binding to the same target proteins as shown in (a). Only the side chains of binding sites are shown. (c) Imatinib binding to its target proteins. The side chains of proteins are imatinib binding sites that are mutated.

Figure 6

Structural representation of protein–drug binding sites. (a) Cytochrome P450 3A4 bound to bromocriptine, erythromycin, metyrapone, ritonavir and progesterone, respectively. The drugs are shown in magenta color in the protein pocket bound to amino acid residues that are shown in cyan except the mutated amino acids marked in blue. The yellow color is the heme of Cytochrome P450 3A4 (CYP3A4). (b) Superposition of energy minimized structures for the wild-type carbonic anhydrase 2 (CA2) bound to lacosamide (PDB: 3IEO) and the mutated models (N67K, Q92P and F131L) bound to the same drug. PDB, protein data bank.

Figure 7

Superposition of X-ray crystal structures of carbonic anhydrase 2 (CA2) and its paralogs (CA13, CA7) bound to hydroxyurea. The ribbon structure of CA2 and its paralogs is shown in pink color. The hydroxyurea in the protein pocket is shown in magenta color bound to amino acid residues that are conserved across CA2 and its paralogs.