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This article is part of the supplement: Computational Intelligence in Bioinformatics and Biostatistics: new trends from the CIBB conference series

Open Access Research

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

Castrense Savojardo12, Piero Fariselli12*, Pier Luigi Martelli23 and Rita Casadio23

Author Affiliations

1 Department of Computer Science and Engineering, University of Bologna, Via Mura Anteo Zamboni 7, 41029 Bologna, Italy

2 Biocomputing Group, University of Bologna, via Selmi 3, 40126 Bologna, Italy

3 CIRI-Life Science and Health Technologies/Department of Biology, Via San Giacomo 9/2, 40129, Bologna, Italy

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BMC Bioinformatics 2013, 14(Suppl 1):S10  doi:10.1186/1471-2105-14-S1-S10

Published: 14 January 2013



Recently, 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.


In 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.


In 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.