A Comprehensive Survey of Scoring Functions for Protein Docking Models

Abstract

The protein-protein docking problem is fundamental to our understanding of how proteins interact. It is also important because of its relevance to drug discovery and vaccine design. Docking methods typically involve two steps. The first step generates many possible candidate conformations, while the second step evaluates the candidates using some scoring function. Despite numerous experiments with many innovative scoring functions, a good scoring function for docking remains elusive. Deep learning models offer alternatives to using explicit empirical or mathematical functions for scoring protein-protein complexes. In this study, we perform a comprehensive survey of the state-of-the-art scoring functions by considering the most popular and highly performant approaches for scoring protein-protein complexes, evaluating their strengths and weaknesses to aid researchers in understanding progress made in this field. Our survey compares the state-of-the-art in classical and deep learning-based scoring methods.