NeEMO: a method using residue interaction networks to improve prediction of protein stability upon mutation

BMC Genomics

Research

Alberto JM Martin¹  Ian Walsh¹  Silvio CE Tosatto¹  Manuel Giollo²  Carlo Ferrari³ 

[1] Department of Biomedical Sciences, University of Padova, Viale G. Colombo 3, 35131, Padova, Italy;Department of Biomedical Sciences, University of Padova, Viale G. Colombo 3, 35131, Padova, Italy;Department of Information Engineering, University of Padova, Via Gradenigo 6, 35121, Padova, Italy;Department of Information Engineering, University of Padova, Via Gradenigo 6, 35121, Padova, Italy;

关键词: Amino Acid Change;    Stability Change;    Stability Prediction;    Relative Solvent Accessibility;    Thermodynamic Energy;   

DOI  :  10.1186/1471-2164-15-S4-S7

来源: Springer

【 摘 要 】

BackgroundThe rapid growth of un-annotated missense variants poses challenges requiring novel strategies for their interpretation. From the thermodynamic point of view, amino acid changes can lead to a change in the internal energy of a protein and induce structural rearrangements. This is of great relevance for the study of diseases and protein design, justifying the development of prediction methods for variant-induced stability changes.ResultsHere we propose NeEMO, a tool for the evaluation of stability changes using an effective representation of proteins based on residue interaction networks (RINs). RINs are used to extract useful features describing interactions of the mutant amino acid with its structural environment. Benchmarking shows NeEMO to be very effective, allowing reliable predictions in different parts of the protein such as β-strands and buried residues. Validation on a previously published independent dataset shows that NeEMO has a Pearson correlation coefficient of 0.77 and a standard error of 1 Kcal/mol, outperforming nine recent methods. The NeEMO web server can be freely accessed from URL: http://protein.bio.unipd.it/neemo/.ConclusionsNeEMO offers an innovative and reliable tool for the annotation of amino acid changes. A key contribution are RINs, which can be used for modeling proteins and their interactions effectively. Interestingly, the approach is very general, and can motivate the development of a new family of RIN-based protein structure analyzers. NeEMO may suggest innovative strategies for bioinformatics tools beyond protein stability prediction.

【 授权许可】

Unknown   
© Giollo et al; licensee BioMed Central Ltd. 2014. This article is published under license to BioMed Central Ltd. 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. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.