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Utilizing protein structure to identify non-random somatic mutations

BACKGROUND: Human cancer is caused by the accumulation of somatic mutations in tumor suppressors and oncogenes within the genome. In the case of oncogenes, recent theory suggests that there are only a few key “driver” mutations responsible for tumorigenesis. As there have been significant pharmacolo...

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Autores principales: Ryslik, Gregory A, Cheng, Yuwei, Cheung, Kei-Hoi, Modis, Yorgo, Zhao, Hongyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3691676/
https://www.ncbi.nlm.nih.gov/pubmed/23758891
http://dx.doi.org/10.1186/1471-2105-14-190
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author Ryslik, Gregory A
Cheng, Yuwei
Cheung, Kei-Hoi
Modis, Yorgo
Zhao, Hongyu
author_facet Ryslik, Gregory A
Cheng, Yuwei
Cheung, Kei-Hoi
Modis, Yorgo
Zhao, Hongyu
author_sort Ryslik, Gregory A
collection PubMed
description BACKGROUND: Human cancer is caused by the accumulation of somatic mutations in tumor suppressors and oncogenes within the genome. In the case of oncogenes, recent theory suggests that there are only a few key “driver” mutations responsible for tumorigenesis. As there have been significant pharmacological successes in developing drugs that treat cancers that carry these driver mutations, several methods that rely on mutational clustering have been developed to identify them. However, these methods consider proteins as a single strand without taking their spatial structures into account. We propose an extension to current methodology that incorporates protein tertiary structure in order to increase our power when identifying mutation clustering. RESULTS: We have developed iPAC (identification of Protein Amino acid Clustering), an algorithm that identifies non-random somatic mutations in proteins while taking into account the three dimensional protein structure. By using the tertiary information, we are able to detect both novel clusters in proteins that are known to exhibit mutation clustering as well as identify clusters in proteins without evidence of clustering based on existing methods. For example, by combining the data in the Protein Data Bank (PDB) and the Catalogue of Somatic Mutations in Cancer, our algorithm identifies new mutational clusters in well known cancer proteins such as KRAS and PI3KC α. Further, by utilizing the tertiary structure, our algorithm also identifies clusters in EGFR, EIF2AK2, and other proteins that are not identified by current methodology. The R package is available at: http://www.bioconductor.org/packages/2.12/bioc/html/iPAC.html. CONCLUSION: Our algorithm extends the current methodology to identify oncogenic activating driver mutations by utilizing tertiary protein structure when identifying nonrandom somatic residue mutation clusters.
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spelling pubmed-36916762013-06-28 Utilizing protein structure to identify non-random somatic mutations Ryslik, Gregory A Cheng, Yuwei Cheung, Kei-Hoi Modis, Yorgo Zhao, Hongyu BMC Bioinformatics Methodology Article BACKGROUND: Human cancer is caused by the accumulation of somatic mutations in tumor suppressors and oncogenes within the genome. In the case of oncogenes, recent theory suggests that there are only a few key “driver” mutations responsible for tumorigenesis. As there have been significant pharmacological successes in developing drugs that treat cancers that carry these driver mutations, several methods that rely on mutational clustering have been developed to identify them. However, these methods consider proteins as a single strand without taking their spatial structures into account. We propose an extension to current methodology that incorporates protein tertiary structure in order to increase our power when identifying mutation clustering. RESULTS: We have developed iPAC (identification of Protein Amino acid Clustering), an algorithm that identifies non-random somatic mutations in proteins while taking into account the three dimensional protein structure. By using the tertiary information, we are able to detect both novel clusters in proteins that are known to exhibit mutation clustering as well as identify clusters in proteins without evidence of clustering based on existing methods. For example, by combining the data in the Protein Data Bank (PDB) and the Catalogue of Somatic Mutations in Cancer, our algorithm identifies new mutational clusters in well known cancer proteins such as KRAS and PI3KC α. Further, by utilizing the tertiary structure, our algorithm also identifies clusters in EGFR, EIF2AK2, and other proteins that are not identified by current methodology. The R package is available at: http://www.bioconductor.org/packages/2.12/bioc/html/iPAC.html. CONCLUSION: Our algorithm extends the current methodology to identify oncogenic activating driver mutations by utilizing tertiary protein structure when identifying nonrandom somatic residue mutation clusters. BioMed Central 2013-06-13 /pmc/articles/PMC3691676/ /pubmed/23758891 http://dx.doi.org/10.1186/1471-2105-14-190 Text en Copyright © 2013 Ryslik et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methodology Article
Ryslik, Gregory A
Cheng, Yuwei
Cheung, Kei-Hoi
Modis, Yorgo
Zhao, Hongyu
Utilizing protein structure to identify non-random somatic mutations
title Utilizing protein structure to identify non-random somatic mutations
title_full Utilizing protein structure to identify non-random somatic mutations
title_fullStr Utilizing protein structure to identify non-random somatic mutations
title_full_unstemmed Utilizing protein structure to identify non-random somatic mutations
title_short Utilizing protein structure to identify non-random somatic mutations
title_sort utilizing protein structure to identify non-random somatic mutations
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3691676/
https://www.ncbi.nlm.nih.gov/pubmed/23758891
http://dx.doi.org/10.1186/1471-2105-14-190
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