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

BMC Bioinformatics

Research

Yaohang Li¹  Ashraf Yaseen¹ 

[1] Department of Computer Science, Old Dominion University, 23529, Norfolk, VA, USA;

关键词: Cysteine Residue;    Protein Data Bank;    Bonding State;    Neural Network Training;    Position Specific Scoring Matrix;   

DOI  :  10.1186/1471-2105-14-S13-S9

来源: Springer

【 摘 要 】

BackgroundDisulfide 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.MethodsIn 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.ResultsThe 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%.ConclusionsOur 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.

【 授权许可】

CC BY   
© Yaseen and Li; licensee BioMed Central Ltd. 2013