MPA‐Pred: A machine learning approach for predicting the binding affinity of membrane protein–protein complexes

Abstract

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.