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Understanding the impacts of missense mutations on structures and functions of human cancer-related genes: A preliminary computational analysis of the COSMIC Cancer Gene Census
Genomics and genome screening are proving central to the study of cancer. However, a good appreciation of the protein structures coded by cancer genes is also invaluable, especially for the understanding of functions, for assessing ligandability of potential targets, and for designing new drugs. To...
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Published in: | PloS one 2019-07, Vol.14 (7), p.e0219935 |
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description | Genomics and genome screening are proving central to the study of cancer. However, a good appreciation of the protein structures coded by cancer genes is also invaluable, especially for the understanding of functions, for assessing ligandability of potential targets, and for designing new drugs. To complement the wealth of information on the genetics of cancer in COSMIC, the most comprehensive database for cancer somatic mutations available, structural information obtained experimentally has been brought together recently in COSMIC-3D. Even where structural information is available for a gene in the Cancer Gene Census, a list of genes in COSMIC with substantial evidence supporting their impacts in cancer, this information is quite often for a single domain in a larger protein or for a single protomer in a multiprotein assembly. Here, we show that over 60% of the genes included in the Cancer Gene Census are predicted to possess multiple domains. Many are also multicomponent and membrane-associated molecular assemblies, with mutations recorded in COSMIC affecting such assemblies. However, only 469 of the gene products have a structure represented in the PDB, and of these only 87 structures have 90-100% coverage over the sequence and 69 have less than 10% coverage. As a first step to bridging gaps in our knowledge in the many cases where individual protein structures and domains are lacking, we discuss our attempts of protein structure modelling using our pipeline and investigating the effects of mutations using two of our in-house methods (SDM2 and mCSM) and identifying potential driver mutations. This allows us to begin to understand the effects of mutations not only on protein stability but also on protein-protein, protein-ligand and protein-nucleic acid interactions. In addition, we consider ways to combine the structural information with the wealth of mutation data available in COSMIC. We discuss the impacts of COSMIC missense mutations on protein structure in order to identify and assess the molecular consequences of cancer-driving mutations. |
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However, a good appreciation of the protein structures coded by cancer genes is also invaluable, especially for the understanding of functions, for assessing ligandability of potential targets, and for designing new drugs. To complement the wealth of information on the genetics of cancer in COSMIC, the most comprehensive database for cancer somatic mutations available, structural information obtained experimentally has been brought together recently in COSMIC-3D. Even where structural information is available for a gene in the Cancer Gene Census, a list of genes in COSMIC with substantial evidence supporting their impacts in cancer, this information is quite often for a single domain in a larger protein or for a single protomer in a multiprotein assembly. Here, we show that over 60% of the genes included in the Cancer Gene Census are predicted to possess multiple domains. Many are also multicomponent and membrane-associated molecular assemblies, with mutations recorded in COSMIC affecting such assemblies. However, only 469 of the gene products have a structure represented in the PDB, and of these only 87 structures have 90-100% coverage over the sequence and 69 have less than 10% coverage. As a first step to bridging gaps in our knowledge in the many cases where individual protein structures and domains are lacking, we discuss our attempts of protein structure modelling using our pipeline and investigating the effects of mutations using two of our in-house methods (SDM2 and mCSM) and identifying potential driver mutations. This allows us to begin to understand the effects of mutations not only on protein stability but also on protein-protein, protein-ligand and protein-nucleic acid interactions. In addition, we consider ways to combine the structural information with the wealth of mutation data available in COSMIC. We discuss the impacts of COSMIC missense mutations on protein structure in order to identify and assess the molecular consequences of cancer-driving mutations.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0219935</identifier><identifier>PMID: 31323058</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Acids ; Assemblies ; Biochemistry ; Bioinformatics ; Biology and Life Sciences ; Biomarkers, Tumor ; Cancer ; Cancer genetics ; Cancer research ; Cancer screening ; Cell proliferation ; Census ; Censuses ; Computational Biology - methods ; Computer applications ; Databases, Genetic ; Domains ; Gene mutation ; Genes ; Genetic aspects ; Genetics ; Genomes ; Genomics ; Genomics - methods ; Humans ; Identification methods ; Ligands ; Missense mutation ; Models, Molecular ; Mutation ; Mutation, Missense ; Neoplasms - diagnosis ; Neoplasms - genetics ; Nucleic acids ; Oncogene Proteins - chemistry ; Oncogene Proteins - genetics ; Oncogene Proteins - metabolism ; Patient outcomes ; Protein Conformation ; Protein structure ; Proteins ; Research and Analysis Methods ; Structure-Activity Relationship</subject><ispartof>PloS one, 2019-07, Vol.14 (7), p.e0219935</ispartof><rights>COPYRIGHT 2019 Public Library of Science</rights><rights>2019 Malhotra et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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However, a good appreciation of the protein structures coded by cancer genes is also invaluable, especially for the understanding of functions, for assessing ligandability of potential targets, and for designing new drugs. To complement the wealth of information on the genetics of cancer in COSMIC, the most comprehensive database for cancer somatic mutations available, structural information obtained experimentally has been brought together recently in COSMIC-3D. Even where structural information is available for a gene in the Cancer Gene Census, a list of genes in COSMIC with substantial evidence supporting their impacts in cancer, this information is quite often for a single domain in a larger protein or for a single protomer in a multiprotein assembly. Here, we show that over 60% of the genes included in the Cancer Gene Census are predicted to possess multiple domains. Many are also multicomponent and membrane-associated molecular assemblies, with mutations recorded in COSMIC affecting such assemblies. However, only 469 of the gene products have a structure represented in the PDB, and of these only 87 structures have 90-100% coverage over the sequence and 69 have less than 10% coverage. As a first step to bridging gaps in our knowledge in the many cases where individual protein structures and domains are lacking, we discuss our attempts of protein structure modelling using our pipeline and investigating the effects of mutations using two of our in-house methods (SDM2 and mCSM) and identifying potential driver mutations. This allows us to begin to understand the effects of mutations not only on protein stability but also on protein-protein, protein-ligand and protein-nucleic acid interactions. In addition, we consider ways to combine the structural information with the wealth of mutation data available in COSMIC. We discuss the impacts of COSMIC missense mutations on protein structure in order to identify and assess the molecular consequences of cancer-driving mutations.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>31323058</pmid><doi>10.1371/journal.pone.0219935</doi><tpages>e0219935</tpages><orcidid>https://orcid.org/0000-0002-7670-1626</orcidid><orcidid>https://orcid.org/0000-0002-3165-9081</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Acids Assemblies Biochemistry Bioinformatics Biology and Life Sciences Biomarkers, Tumor Cancer Cancer genetics Cancer research Cancer screening Cell proliferation Census Censuses Computational Biology - methods Computer applications Databases, Genetic Domains Gene mutation Genes Genetic aspects Genetics Genomes Genomics Genomics - methods Humans Identification methods Ligands Missense mutation Models, Molecular Mutation Mutation, Missense Neoplasms - diagnosis Neoplasms - genetics Nucleic acids Oncogene Proteins - chemistry Oncogene Proteins - genetics Oncogene Proteins - metabolism Patient outcomes Protein Conformation Protein structure Proteins Research and Analysis Methods Structure-Activity Relationship |
title | Understanding the impacts of missense mutations on structures and functions of human cancer-related genes: A preliminary computational analysis of the COSMIC Cancer Gene Census |
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