Prediction of Disulfide Connectivity in Proteins with Machine-learning Methods and Correlated Mutations

Overview

Journal BMC Bioinformatics

Publisher Biomed Central

Specialty Biology

Date 2013 Feb 2

PMID 23368835

Citations 4

Authors

Castrense Savojardo

Piero Fariselli

Pier Luigi Martelli

Rita Casadio

Affiliations

Soon will be listed here.

Abstract

Background: 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.

Results: 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.

Conclusions: 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.

Citing Articles

Observation selection bias in contact prediction and its implications for structural bioinformatics.

Orlando G, Raimondi D, Vranken W Sci Rep. 2016; 6:36679.

PMID: 27857150 PMC: 5114557. DOI: 10.1038/srep36679.

Soft Computing Methods for Disulfide Connectivity Prediction.

Marquez-Chamorro A, Aguilar-Ruiz J Evol Bioinform Online. 2015; 11:223-9.

PMID: 26523116 PMC: 4620934. DOI: 10.4137/EBO.S25349.

Accurate disulfide-bonding network predictions improve ab initio structure prediction of cysteine-rich proteins.

Yang J, He B, Jang R, Zhang Y, Shen H Bioinformatics. 2015; 31(23):3773-81.

PMID: 26254435 PMC: 5898604. DOI: 10.1093/bioinformatics/btv459.

An Evolutionary View on Disulfide Bond Connectivities Prediction Using Phylogenetic Trees and a Simple Cysteine Mutation Model.

Raimondi D, Orlando G, Vranken W PLoS One. 2015; 10(7):e0131792.

PMID: 26161671 PMC: 4498770. DOI: 10.1371/journal.pone.0131792.

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