Support vector machines for prediction of peptidyl prolyl cis/trans isomerization

Abstract

A new method for peptidyl prolyl cis/trans isomerization prediction based on the theory of support vector machines (SVM) was introduced. The SVM represents a new approach to supervised pattern classification and has been successfully applied to a wide range of pattern recognition problems. In this study, six training datasets consisting of different length local sequence respectively were used. The polynomial kernel functions with different parameter d were chosen. The test for the independent testing dataset and the jackknife test were both carried out. When the local sequence length was 20-residue and the parameter d = 8, the SVM method archived the best performance with the correct rate for the cis and trans forms reaching 70.4 and 69.7% for the independent testing dataset, 76.7 and 76.6% for the jackknife test, respectively. Matthew's correlation coefficients for the jackknife test could reach about 0.5. The results obtained through this study indicated that the SVM method would become a powerful tool for predicting peptidyl prolyl cis/trans isomerization.