Prediction of Disulfide Bonding Pattern Based on Support Vector Machine with Parameters Tuned by Multiple Trajectory Search

The prediction of the location of disulfide bridges helps solving the protein folding problem. Most of previous works on disulfide connectivity pattern prediction use the prior knowledge of the bonding state of cysteines. In this study an effective method is proposed to predict disulfide connectivity pattern without the prior knowledge of cysteins’bonding state. To the best of our knowledge, without the prior knowledge of the bonding state of cysteines, the best accuracy rate reported in the literature for the prediction of the overall disulfide connectivity pattern (Qp) and that of disulfide bridge prediction (Qc) are 48% and 51% respectively for the dataset SPX. In this study, the cystein position difference, the cystein index difference, the predicted secondary structure of protein and the PSSM score are used as features. The support vector machine (SVM) is trained to compute the connectivity probabilities of cysteine pairs. An evolutionary algorithm called the multiple trajectory search (MTS) is integrated with the SVM training to tune the parameters for the SVM and the window sizes for the predicted secondary structure and the PSSM. The maximum weight perfect matching algorithm is then used to find the disulfide connectivity pattern. Testing our method on the same dataset SPX, the accuracy rates are 54.5% and 60% for disulfide connectivity pattern prediction and disulfide bridge prediction when the bonding state of cysteines is not known in advance. Key-Words: Disulfide bonding pattern, SVM, multiple trajectory search