From Traditional Methods to Deep Learning Approaches: Advances in Protein–Protein Docking

Abstract

Protein–protein interactions play a crucial role in human biological processes, and deciphering their structural information and interaction patterns is essential for drug development. The high costs of experimental structure determination have brought computational protein–protein docking methods into the spotlight. Traditional docking algorithms, which hinge on a sampling-scoring framework, heavily rely on extensive sampling of candidate poses and customized scoring functions based on the geometric and chemical compatibility between proteins. However, these methods face challenges related to sampling efficiency and stability. The advent of deep learning (DL) has ushered in data-driven docking methods that demonstrate significant advantages, particularly boosting the efficiency of protein–protein docking. We systematically review the historical development of protein–protein docking from traditional approaches to DL techniques and provide insights into emerging technologies in this field. Moreover, we summarize the commonly used datasets and evaluation metrics in protein–protein docking. We expect that this review can offer valuable guidance for the development of more efficient protein–protein docking algorithms.