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Comprehensive Analysis of Constraint on the Spatial Distribution of Missense Variants in Human Protein Structures.
Sivley, R Michael; Dou, Xiaoyi; Meiler, Jens; Bush, William S; Capra, John A.
Afiliação
  • Sivley RM; Department of Biomedical Informatics, Vanderbilt University, Nashville, TN 37232, USA.
  • Dou X; Department of Computer Science, Vanderbilt University, Nashville, TN 37212, USA.
  • Meiler J; Department of Biomedical Informatics, Vanderbilt University, Nashville, TN 37232, USA; Department of Chemistry, Vanderbilt University, Nashville, TN 37212, USA; Center for Structural Biology, Vanderbilt University, Nashville, TN 37212, USA.
  • Bush WS; Department for Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH 44106, USA; Institute for Computational Biology, Case Western Reserve University, Cleveland, OH 44106, USA. Electronic address: wsb36@case.edu.
  • Capra JA; Department of Biomedical Informatics, Vanderbilt University, Nashville, TN 37232, USA; Department of Computer Science, Vanderbilt University, Nashville, TN 37212, USA; Center for Structural Biology, Vanderbilt University, Nashville, TN 37212, USA; Department of Biological Sciences, Vanderbilt Univer
Am J Hum Genet ; 102(3): 415-426, 2018 03 01.
Article em En | MEDLINE | ID: mdl-29455857
ABSTRACT
The spatial distribution of genetic variation within proteins is shaped by evolutionary constraint and provides insight into the functional importance of protein regions and the potential pathogenicity of protein alterations. Here, we comprehensively evaluate the 3D spatial patterns of human germline and somatic variation in 6,604 experimentally derived protein structures and 33,144 computationally derived homology models covering 77% of all human proteins. Using a systematic approach, we quantify differences in the spatial distributions of neutral germline variants, disease-causing germline variants, and recurrent somatic variants. Neutral missense variants exhibit a general trend toward spatial dispersion, which is driven by constraint on core residues. In contrast, germline disease-causing variants are generally clustered in protein structures and form clusters more frequently than recurrent somatic variants identified from tumor sequencing. In total, we identify 215 proteins with significant spatial constraints on the distribution of disease-causing missense variants in experimentally derived protein structures, only 65 (30%) of which have been previously reported. This analysis identifies many clusters not detectable from sequence information alone; only 12% of proteins with significant clustering in 3D were identified from similar analyses of linear protein sequence. Furthermore, spatial analyses of mutations in homology-based structural models are highly correlated with those from experimentally derived structures, supporting the use of computationally derived models. Our approach highlights significant differences in the spatial constraints on different classes of mutations in protein structure and identifies regions of potential function within individual proteins.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Proteínas / Mutação de Sentido Incorreto Limite: Humans Idioma: En Ano de publicação: 2018 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Proteínas / Mutação de Sentido Incorreto Limite: Humans Idioma: En Ano de publicação: 2018 Tipo de documento: Article