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This article is part of the supplement: Selected articles from the 9th Annual Biotechnology and Bioinformatics Symposium (BIOT 2012)

Open Access Research

Dinosolve: a protein disulfide bonding prediction server using context-based features to enhance prediction accuracy

Ashraf Yaseen and Yaohang Li*

Author Affiliations

Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA

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BMC Bioinformatics 2013, 14(Suppl 13):S9  doi:10.1186/1471-2105-14-S13-S9

Published: 1 October 2013

Abstract

Background

Disulfide bonds play an important role in protein folding and structure stability. Accurately predicting disulfide bonds from protein sequences is important for modeling the structural and functional characteristics of many proteins.

Methods

In this work, we introduce an approach of enhancing disulfide bonding prediction accuracy by taking advantage of context-based features. We firstly derive the first-order and second-order mean-force potentials according to the amino acid environment around the cysteine residues from large number of cysteine samples. The mean-force potentials are integrated as context-based scores to estimate the favorability of a cysteine residue in disulfide bonding state as well as a cysteine pair in disulfide bond connectivity. These context-based scores are then incorporated as features together with other sequence and evolutionary information to train neural networks for disulfide bonding state prediction and connectivity prediction.

Results

The 10-fold cross validated accuracy is 90.8% at residue-level and 85.6% at protein-level in classifying an individual cysteine residue as bonded or free, which is around 2% accuracy improvement. The average accuracy for disulfide bonding connectivity prediction is also improved, which yields overall sensitivity of 73.42% and specificity of 91.61%.

Conclusions

Our computational results have shown that the context-based scores are effective features to enhance the prediction accuracies of both disulfide bonding state prediction and connectivity prediction. Our disulfide prediction algorithm is implemented on a web server named "Dinosolve" available at: http://hpcr.cs.odu.edu/dinosolve webcite.