Study of interface residue pairs is important for understanding the interactions between monomers inside a trimer protein–protein complex. We developed a two-layer support vector machine (SVM) ensemble-classifier that considers physicochemical and geometric properties of amino acids and the influence of surrounding amino acids. Different descriptors and different combinations may give different prediction results. We propose feature combination engineering based on correlation coefficients and F-values. The accuracy of our method is 65.38% in independent test set, indicating biological significance. Our predictions are consistent with the experimental results. It shows the effectiveness and reliability of our method to predict interface residue pairs of protein trimers.

中文翻译：

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预测蛋白质三聚体界面残留对的两层 SVM 集成分类器

界面残基对的研究对于理解三聚体蛋白质-蛋白质复合物中单体之间的相互作用很重要。我们开发了一种两层支持向量机 (SVM) 集成分类器，它考虑了氨基酸的物理化学和几何特性以及周围氨基酸的影响。不同的描述符和不同的组合可能会给出不同的预测结果。我们提出了基于相关系数和 F 值的特征组合工程。我们的方法在独立测试集中的准确率为 65.38%，表明具有生物学意义。我们的预测与实验结果一致。它显示了我们的方法预测蛋白质三聚体的界面残基对的有效性和可靠性。