Impact of germline and somatic missense variations on drug binding sites

C. Yan, N. Pattabiraman, Jeremy Goecks, P. Lam, A. Nayak, Y. Pan, J. Torcivia-Rodriguez, A. Voskanian, Q. Wan, R. Mazumder

Research output: Contribution to journalArticle

2 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)128-136
Number of pages9
JournalPharmacogenomics Journal
Volume17
Issue number2
DOIs
StatePublished - Mar 1 2017
Externally publishedYes

Fingerprint

Binding Sites
Pharmaceutical Preparations
Nucleotides
Amino Acids
Proteins
Protein Binding
Pharmaceutical Databases
Exome
Precision Medicine
Germ-Line Mutation
Drug Discovery
Carrier Proteins
Genome
Databases
Technology

ASJC Scopus subject areas

  • Molecular Medicine
  • Genetics
  • Pharmacology

Cite this

Yan, C., Pattabiraman, N., Goecks, J., Lam, P., Nayak, A., Pan, Y., ... Mazumder, R. (2017). Impact of germline and somatic missense variations on drug binding sites. Pharmacogenomics Journal, 17(2), 128-136. https://doi.org/10.1038/tpj.2015.97

Impact of germline and somatic missense variations on drug binding sites. / Yan, C.; Pattabiraman, N.; Goecks, Jeremy; Lam, P.; Nayak, A.; Pan, Y.; Torcivia-Rodriguez, J.; Voskanian, A.; Wan, Q.; Mazumder, R.

In: Pharmacogenomics Journal, Vol. 17, No. 2, 01.03.2017, p. 128-136.

Research output: Contribution to journalArticle

Yan, C, Pattabiraman, N, Goecks, J, Lam, P, Nayak, A, Pan, Y, Torcivia-Rodriguez, J, Voskanian, A, Wan, Q & Mazumder, R 2017, 'Impact of germline and somatic missense variations on drug binding sites', Pharmacogenomics Journal, vol. 17, no. 2, pp. 128-136. https://doi.org/10.1038/tpj.2015.97
Yan, C. ; Pattabiraman, N. ; Goecks, Jeremy ; Lam, P. ; Nayak, A. ; Pan, Y. ; Torcivia-Rodriguez, J. ; Voskanian, A. ; Wan, Q. ; Mazumder, R. / Impact of germline and somatic missense variations on drug binding sites. In: Pharmacogenomics Journal. 2017 ; Vol. 17, No. 2. pp. 128-136.
@article{390fa404ff464ebb8b21c56a6fbeac28,
title = "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.",
author = "C. Yan and N. Pattabiraman and Jeremy Goecks and P. Lam and A. Nayak and Y. Pan and J. Torcivia-Rodriguez and A. Voskanian and Q. Wan and R. Mazumder",
year = "2017",
month = "3",
day = "1",
doi = "10.1038/tpj.2015.97",
language = "English (US)",
volume = "17",
pages = "128--136",
journal = "Pharmacogenomics Journal",
issn = "1470-269X",
publisher = "Nature Publishing Group",
number = "2",

}

TY - JOUR

T1 - Impact of germline and somatic missense variations on drug binding sites

AU - Yan, C.

AU - Pattabiraman, N.

AU - Goecks, Jeremy

AU - Lam, P.

AU - Nayak, A.

AU - Pan, Y.

AU - Torcivia-Rodriguez, J.

AU - Voskanian, A.

AU - Wan, Q.

AU - Mazumder, R.

PY - 2017/3/1

Y1 - 2017/3/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84955612917&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84955612917&partnerID=8YFLogxK

U2 - 10.1038/tpj.2015.97

DO - 10.1038/tpj.2015.97

M3 - Article

C2 - 26810135

AN - SCOPUS:84955612917

VL - 17

SP - 128

EP - 136

JO - Pharmacogenomics Journal

JF - Pharmacogenomics Journal

SN - 1470-269X

IS - 2

ER -