应用支持向量机预测蛋白质相互作用位点

Prediction of Protein-Protein Interaction Sites Using Support Vector Machine

蛋白质相互作用位点的识别对于突变设计和预测蛋白质相互作用的网络是非常重要的.基于支持向量机学习方法,该文提出一种用于预测蛋白质相互作用位点的有效数据属性抽取方法,该方法利用蛋白质的序列信息、蛋白质残基的可及表面积和进化率来构造向量,通过十倍交叉验证来对数据进行训练和预测.实际计算的结果显示,该方法的准确率为72.19%,比只利用序列信息和进化率信息的方法提高了5.71%.

Identification of protein-protein interaction sites is essential for the mutant design and prediction of protein-protein networks. This paper proposes a method for predicting protein-protein interaction sites by combining support vector machine （SVM） and the sequence profiles, the accessible surface area （ASA） and the evolution rate of a residue. The dataset is trained and tested using 10-fold cross-validation. Accuracy of the proposed method is 72.91%, 5.71% higher than that of the method only using the sequence profiles and the evolution rate of a residue.