Prediction of Protein-Protein Interaction Sites Using Only Sequence Information and Using Both Sequence and Structural Information
Prediction of Protein-Protein Interaction Sites Using Only Sequence Information and Using Both Sequence and Structural Information
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DOI：
10.2197/ipsjdc.4.217
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发表时间：
2008-03
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影响因子：
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通讯作者：
Masanori Kakuta;Shugo Nakamura;K. Shimizu
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文献类型：
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作者：
Masanori Kakuta;Shugo Nakamura;K. Shimizu
蛋白质-蛋白质相互作用在许多生物活动中起着重要作用。我们发展了两种预测蛋白质-蛋白质相互作用位点残基的方法。一种方法仅使用序列信息，另一种方法同时使用序列和结构信息。我们使用支持向量机（SVM）与位置特异性评分矩阵（PSSM）作为序列信息和可及表面积（阿萨）的极性和非极性原子作为结构信息。SVM分两个阶段使用。在第一阶段中，通过将顺序相邻残基的PSSM或将空间相邻残基的PSSM和ASA作为特征来预测相互作用残基。第二阶段充当过滤器以细化预测结果。同时使用序列和结构信息的预测器的召回率和准确率分别为73.6%和50.5%。我们发现，使用PSSM代替氨基酸出现频率是我们的方法改进的主要因素。
Protein-protein interactions play an important role in a number of biological activities. We developed two methods of predictingprotein-protein interaction site residues. One method uses only sequence information and the other method uses both sequence and structural information. We used support vector machine (SVM) with a position specific scoring matrix (PSSM) as sequence information and accessible surface area(ASA) of polar and non-polar atoms as structural information. SVM is used in two stages. In the first stage, an interaction residue is predicted by taking PSSMs of sequentially neighboring residues or taking PSSMs and ASAs of spatially neighboring residues as features. The second stage acts as a filter to refine the prediction results. The recall and precision of the predictor using both sequence and structural information are 73.6% and 50.5%, respectively. We found that using PSSM instead of frequency of amino acid appearance was the main factor of improvement of our methods.