Membrane protein–protein interactions are essential for several functions including cell signaling, ion transport, and enzymatic activity. These interactions are mainly dictated by their binding affinities. Although several methods are available for predicting the binding affinity of protein–protein complexes, there exists no specific method for membrane protein–protein complexes. In this work, we collected the experimental binding affinity data for a set of 114 membrane protein–protein complexes and derived several structure and sequence-based features. Our analysis on the relationship between binding affinity and the features revealed that the important factors mainly depend on the type of membrane protein and the functional class of the protein. Specifically, aromatic and charged residues at the interface, and aromatic-aromatic and electrostatic interactions are found to be important to understand the binding affinity. Further, we developed a method, MPA-Pred, for predicting the binding affinity of membrane protein–protein complexes using a machine learning approach. It showed an average correlation and mean absolute error of 0.83 and 0.91 kcal/mol, respectively, using the jack-knife test on a set of 114 complexes. We have also developed a web server and it is available at https://web.iitm.ac.in/bioinfo2/MPA-Pred/. This method can be used for predicting the affinity of membrane protein–protein complexes at a large scale and aid to improve drug design strategies.

中文翻译：

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MPA-Pred：一种预测膜蛋白-蛋白复合物结合亲和力的机器学习方法

膜蛋白-蛋白质相互作用对于细胞信号传导、离子运输和酶活性等多种功能至关重要。这些相互作用主要由它们的结合亲和力决定。尽管有多种方法可用于预测蛋白质-蛋白质复合物的结合亲和力，但对于膜蛋白-蛋白质复合物还没有特定的方法。在这项工作中，我们收集了一组 114 种膜蛋白-蛋白质复合物的实验结合亲和力数据，并得出了一些基于结构和序列的特征。我们对结合亲和力与特征之间关系的分析表明，重要因素主要取决于膜蛋白的类型和蛋白质的功能类别。具体而言，发现界面处的芳香族和带电残基以及芳香族-芳香族和静电相互作用对于理解结合亲和力非常重要。此外，我们开发了一种方法 MPA-Pred，用于使用机器学习方法预测膜蛋白-蛋白质复合物的结合亲和力。对一组 114 个复合物进行折刀测试，结果显示平均相关性和平均绝对误差分别为 0.83 和 0.91 kcal/mol。我们还开发了一个 Web 服务器，可从 https://web.iitm.ac.in/bioinfo2/MPA-Pred/ 获取。该方法可用于大规模预测膜蛋白-蛋白质复合物的亲和力，并有助于改进药物设计策略。