Machine Learning Approaches for Protein⁻Protein Interaction Hot Spot Prediction: Progress and Comparative Assessment

Abstract

Hot spots are the subset of interface residues that account for most of the binding free energy, and they play essential roles in the stability of protein binding. Effectively identifying which specific interface residues of protein⁻protein complexes form the hot spots is critical for understanding the principles of protein interactions, and it has broad application prospects in protein design and drug development. Experimental methods like alanine scanning mutagenesis are labor-intensive and time-consuming. At present, the experimentally measured hot spots are very limited. Hence, the use of computational approaches to predicting hot spots is becoming increasingly important. Here, we describe the basic concepts and recent advances of machine learning applications in inferring the protein⁻protein interaction hot spots, and assess the performance of widely used features, machine learning algorithms, and existing state-of-the-art approaches. We also discuss the challenges and future directions in the prediction of hot spots.

Keywords: hot spots; machine learning; performance evaluation; protein-protein interaction.

Figure 1

Overview of machine learning approaches to predicting protein–protein interaction hot spots. For the binding of interface residues in protein–protein interactions, a large number and variety of features are extracted from diverse data sources. Then feature extraction and feature selection approaches are used for dimensionality reduction. Finally, the machine learning-based prediction models are trained and applied to make predictions of hot spots.

Figure 2

A Venn diagram showing the number of correctly predicted residues from the three machine learning algorithms for the independent dataset (BID18).