4.7 Article

The Contribution of Missense Mutations in Core and Rim Residues of Protein-Protein Interfaces to Human Disease

Journal

JOURNAL OF MOLECULAR BIOLOGY
Volume 427, Issue 17, Pages 2886-2898

Publisher

ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jmb.2015.07.004

Keywords

protein-protein interaction; core and rim interface; human disease; SAVs; nsSNPs

Funding

  1. Medical Research Council [MR/K021613/1]
  2. Medical Research Council [MR/K021613/1] Funding Source: researchfish
  3. MRC [MR/K021613/1] Funding Source: UKRI

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Missense mutations at protein protein interaction sites, called interfaces, are important contributors to human disease. Interfaces are non-uniform surface areas characterized by two main regions, core and rim, which differ in terms of evolutionary conservation and physicochemical properties. Moreover, within interfaces, only a small subset of residues (hot spots) is crucial for the binding free energy of the protein protein complex. We performed a large-scale structural analysis of human single amino acid variations (SAVs) and demonstrated that disease-causing mutations are preferentially located within the interface core, as opposed to the rim (p < 0.01). In contrast, the interface rim is significantly enriched in polymorphisms, similar to the remaining non-interacting surface. Energetic hot spots tend to be enriched in disease-causing mutations compared to non-hot spots (p = 0.05), regardless of their occurrence in core or rim residues. For individual amino acids, the frequency of substitution into a polymorphism or disease-causing mutation differed to other amino acids and was related to its structural location, as was the type of physicochemical change introduced by the SAV. In conclusion, this study demonstrated the different distribution and properties of disease-causing SAVs and polymorphisms within different structural regions and in relation to the energetic contribution of amino acid in protein protein interfaces, thus highlighting the importance of a structural system biology approach for predicting the effect of SAVs. (C) 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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