Disulfide Bonding Pattern Prediction Server Based on Normalized Pair Distance by MODELLER

Prediction of the protein structure is one of the most important problems in the computational biology, and it remains one of the biggest challenges in the structural biology. Disulfide bonds play an import structural role in stabilizing protein conformations. For the protein-folding prediction, a correct prediction of disulfide bridges can greatly reduce the search space. The prediction of disulfide bonding pattern helps, to a certain degree, predicts the 3D structure of a protein and hence its function since disulfide bonds imposes geometrical constraints on the protein backbones. Then, the protein 3D structure related features called normalized pair distance (NPD) vector were imposed as the features for designing the classifier based on the support vector machine (SVM). The classifier was trained to compute the connectivity probabilities of cysteine pairs. In addition, a genetic algorithm was integrated with the SVM model to tune the parameters of the SVM and the window sizes for the features. The maximum weighted perfect matching algorithm was then used to find the disulfide connectivity pattern. In this study, the experimental results show that the accuracies rate reaches 91.7% for the prediction of the overall disulfide connectivity pattern (Q_P) and that of disulfide bridge prediction (Q_C) is 94.2% for dataset SP39.