Classification of Protein Quaternary Structure based on Support Vector Machines and Combinatorial Forecast

Objective:To establish a method of automatically identifying protein structures based on support vector machine for improving the present classification accuracies.Methods:The former four methods of feature extraction fi'om the amino acid sequences were improved,and then an effective combinatorial forecast model was established based on linear and non-linear method.Results:The classification precision of the four improved models has increased by 2-3 % over before.Then,combinatorial forecast was further introduced,and the classification precision has increased by 2-3 % again.Finally,the precision of independent testing set exceeded 90 %. Conclusion:The results indicate that protein primary sequence contains quaternary structure information.And the combinatorial forecast method can effectively integrate with several kinds of methods of feature value extraction in the primary sequences.