Deep learning in modeling protein complex structures: From contact prediction to end-to-end approaches

Protein–protein interactions play crucial roles in many biological processes. Traditionally, protein complex structures are normally built by protein–protein docking. With the rapid development of artificial intelligence and its great success in monomer protein structure prediction, deep learning has widely been applied to modeling protein–protein complex structures through inter-protein contact prediction and end-to-end approaches in the past few years. This article reviews the recent advances of deep-learning-based approaches in modeling protein–protein complex structures as well as their advantages and limitations. Challenges and possible future directions are also briefly discussed in applying deep learning for the prediction of protein complex structures.