Geng Haijiang, Lu Tao, Lin Xiao, Liu Yu, Yan Fangrong
Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing 210009, China.
Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing 210009, China ; State Key Laboratory of Natural Medicines, School of Life Science and Technology, China Pharmaceutical University, Nanjing 210009, China.
Biochem Res Int. 2015;2015:978193. doi: 10.1155/2015/978193. Epub 2015 Nov 30.
Protein functions through interactions with other proteins and biomolecules and these interactions occur on the so-called interface residues of the protein sequences. Identifying interface residues makes us better understand the biological mechanism of protein interaction. Meanwhile, information about the interface residues contributes to the understanding of metabolic, signal transduction networks and indicates directions in drug designing. In recent years, researchers have focused on developing new computational methods for predicting protein interface residues. Here we creatively used a 181-dimension protein sequence feature vector as input to the Naive Bayes Classifier- (NBC-) based method to predict interaction sites in protein-protein complexes interaction. The prediction of interaction sites in protein interactions is regarded as an amino acid residue binary classification problem by applying NBC with protein sequence features. Independent test results suggested that Naive Bayes Classifier-based method with the protein sequence features as input vectors performed well.
蛋白质通过与其他蛋白质和生物分子相互作用来发挥功能，这些相互作用发生在蛋白质序列所谓的界面残基上。识别界面残基能让我们更好地理解蛋白质相互作用的生物学机制。同时，有关界面残基的信息有助于理解代谢、信号转导网络，并为药物设计指明方向。近年来，研究人员致力于开发预测蛋白质界面残基的新计算方法。在此，我们创造性地使用一个181维的蛋白质序列特征向量作为基于朴素贝叶斯分类器（NBC）方法的输入，以预测蛋白质 - 蛋白质复合物相互作用中的相互作用位点。通过将NBC应用于蛋白质序列特征，蛋白质相互作用中相互作用位点的预测被视为一个氨基酸残基二元分类问题。独立测试结果表明，以蛋白质序列特征作为输入向量的基于朴素贝叶斯分类器的方法表现良好。
