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Impact of germline and somatic missense variations on drug binding sites
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 variation...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5380835/ https://www.ncbi.nlm.nih.gov/pubmed/26810135 http://dx.doi.org/10.1038/tpj.2015.97 |
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author | Yan, C Pattabiraman, N Goecks, J Lam, P Nayak, A Pan, Y Torcivia-Rodriguez, J Voskanian, A Wan, Q Mazumder, R |
author_facet | Yan, C Pattabiraman, N Goecks, J Lam, P Nayak, A Pan, Y Torcivia-Rodriguez, J Voskanian, A Wan, Q Mazumder, R |
author_sort | Yan, C |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5380835 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-53808352017-04-21 Impact of germline and somatic missense variations on drug binding sites Yan, C Pattabiraman, N Goecks, J Lam, P Nayak, A Pan, Y Torcivia-Rodriguez, J Voskanian, A Wan, Q Mazumder, R Pharmacogenomics J Original Article 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. Nature Publishing Group 2017-03 2016-01-26 /pmc/articles/PMC5380835/ /pubmed/26810135 http://dx.doi.org/10.1038/tpj.2015.97 Text en Copyright © 2016 Macmillan Publishers Limited http://creativecommons.org/licenses/by-nc-sa/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/ |
spellingShingle | Original Article Yan, C Pattabiraman, N Goecks, J 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 |
title | Impact of germline and somatic missense variations on drug binding sites |
title_full | Impact of germline and somatic missense variations on drug binding sites |
title_fullStr | Impact of germline and somatic missense variations on drug binding sites |
title_full_unstemmed | Impact of germline and somatic missense variations on drug binding sites |
title_short | Impact of germline and somatic missense variations on drug binding sites |
title_sort | impact of germline and somatic missense variations on drug binding sites |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5380835/ https://www.ncbi.nlm.nih.gov/pubmed/26810135 http://dx.doi.org/10.1038/tpj.2015.97 |
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