Prediction of disulfide connectivity in proteins with machine-learning methods and correlated mutations

BMC Bioinformatics

Research

Pier Luigi Martelli¹  Rita Casadio¹  Castrense Savojardo²  Piero Fariselli² 

[1] Biocomputing Group, University of Bologna, via Selmi 3, 40126, Bologna, Italy;CIRI-Life Science and Health Technologies/Department of Biology, Via San Giacomo 9/2, 40129, Bologna, Italy;Department of Computer Science and Engineering, University of Bologna, Via Mura Anteo Zamboni 7, 41029, Bologna, Italy;Biocomputing Group, University of Bologna, via Selmi 3, 40126, Bologna, Italy;

关键词: Mutual Information;    Disulfide Bond;    Support Vector Regression;    Bonding State;    Average Mutual Information;   

DOI  :  10.1186/1471-2105-14-S1-S10

来源: Springer

【 摘 要 】

BackgroundRecently, information derived by correlated mutations in proteins has regained relevance for predicting protein contacts. This is due to new forms of mutual information analysis that have been proven to be more suitable to highlight direct coupling between pairs of residues in protein structures and to the large number of protein chains that are currently available for statistical validation. It was previously discussed that disulfide bond topology in proteins is also constrained by correlated mutations.ResultsIn this paper we exploit information derived from a corrected mutual information analysis and from the inverse of the covariance matrix to address the problem of the prediction of the topology of disulfide bonds in Eukaryotes. Recently, we have shown that Support Vector Regression (SVR) can improve the prediction for the disulfide connectivity patterns. Here we show that the inclusion of the correlated mutation information increases of 5 percentage points the SVR performance (from 54% to 59%). When this approach is used in combination with a method previously developed by us and scoring at the state of art in predicting both location and topology of disulfide bonds in Eukaryotes (DisLocate), the per-protein accuracy is 38%, 2 percentage points higher than that previously obtained.ConclusionsIn this paper we show that the inclusion of information derived from correlated mutations can improve the performance of the state of the art methods for predicting disulfide connectivity patterns in Eukaryotic proteins. Our analysis also provides support to the notion that improving methods to extract evolutionary information from multiple sequence alignments greatly contributes to the scoring performance of predictors suited to detect relevant features from protein chains.

【 授权许可】

Unknown   
© Savojardo et al.; licensee BioMed Central Ltd. 2013. 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.