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Protein sector analysis for the clustering of disease-associated mutations.

ABSTRACT: The importance of mutations in disease phenotype has been studied, with information available in databases such as OMIM. However, it remains a research challenge for the possibility of clustering amino acid residues based on an underlying interaction, such as co-evolution, to understand how mutations in these related sites can lead to different disease phenotypes.This paper presents an integrative approach to identify groups of co-evolving residues, known as protein sectors. By studying a protein family using multiple sequence alignments and statistical coupling analysis, we attempted to determine if it is possible that these groups of residues could be related to disease phenotypes. After the protein sectors were identified, disease-associated residues within these groups of amino acids were mapped to a structure representing the protein family. In this study, we used the proposed pipeline to analyze two test cases of spermine synthase and Rab GDP dissociation inhibitor.The results suggest that there is a possible link between certain groups of co-evolving residues and different disease phenotypes. The pipeline described in this work could also be used to study other protein families associated with human diseases.

SUBMITTER: Guevara-Coto J

PROVIDER: S-EPMC4304181 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Protein sector analysis for the clustering of disease-associated mutations.

Guevara-Coto Jose J Schwartz Charles E CE Wang Liangjiang L

BMC genomics 20141216

<h4>Background</h4>The importance of mutations in disease phenotype has been studied, with information available in databases such as OMIM. However, it remains a research challenge for the possibility of clustering amino acid residues based on an underlying interaction, such as co-evolution, to understand how mutations in these related sites can lead to different disease phenotypes.<h4>Results</h4>This paper presents an integrative approach to identify groups of co-evolving residues, known as pr ...[more]

PMID: 25559331

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Project description:The effects of disease mutations on protein structure and function have been extensively investigated, and many predictors of the functional impact of single amino acid substitutions are publicly available. The majority of these predictors are based on protein structure and evolutionary conservation, following the assumption that disease mutations predominantly affect folded and conserved protein regions. However, the prevalence of the intrinsically disordered proteins (IDPs) and regions (IDRs) in the human proteome together with their lack of fixed structure and low sequence conservation raise a question about the impact of disease mutations in IDRs. Here, we investigate annotated missense disease mutations and show that 21.7% of them are located within such intrinsically disordered regions. We further demonstrate that 20% of disease mutations in IDRs cause local disorder-to-order transitions, which represents a 1.7-2.7 fold increase compared to annotated polymorphisms and neutral evolutionary substitutions, respectively. Secondary structure predictions show elevated rates of transition from helices and strands into loops and vice versa in the disease mutations dataset. Disease disorder-to-order mutations also influence predicted molecular recognition features (MoRFs) more often than the control mutations. The repertoire of disorder-to-order transition mutations is limited, with five most frequent mutations (R?W, R?C, E?K, R?H, R?Q) collectively accounting for 44% of all deleterious disorder-to-order transitions. As a proof of concept, we performed accelerated molecular dynamics simulations on a deleterious disorder-to-order transition mutation of tumor protein p63 and, in agreement with our predictions, observed an increased ?-helical propensity of the region harboring the mutation. Our findings highlight the importance of mutations in IDRs and refine the traditional structure-centric view of disease mutations. The results of this study offer a new perspective on the role of mutations in disease, with implications for improving predictors of the functional impact of missense mutations.

Dataset Information

Protein sector analysis for the clustering of disease-associated mutations.

Publications

Protein sector analysis for the clustering of disease-associated mutations.

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