A Computational approach to predict contact potential and disulfide bond of proteins /

Abstract

Contact map and disulfide bond information of a protein give crucial clues about 3-dimensional structure and function of a protein. In this study, we represent a computational approach to predict both contact maps and disulfide bonds of the residues inside of a protein and these studies are two of the essential steps of protein folding problem. In the first study, we predicted contacting residues of proteins using physical (ordering, length and volume), chemical (hydrophobicity), evolutionary (neighboring) and structural (secondary structure) information by implementing classification techniques, Neural Networks (NNs) and Support Vector Machines (SVMs). As a result, our method predicts 14% of the contacting residues with 0.6% false positive ratio and it performs 9 times better than a random predictor. In the second study, using the same parameters we predicted cysteine residues forming. In this study, we used SVMs, we obtained 63.76% accuracy in disulfide bond prediction.