Advances to tackle backbone flexibility in protein docking

TL;DR

This paper reviews recent advances in protein-protein docking methods that address backbone flexibility, including enhanced sampling, internal coordinate formulations, and machine learning, aiming to improve accuracy in modeling flexible protein complexes.

Contribution

It highlights new computational techniques that significantly improve the ability to predict protein complexes with backbone conformational changes.

Findings

Enhanced sampling techniques reduce time-scale limitations.

Internal coordinate formulations capture realistic motions.

Machine learning guides docking and predicts binding sites.

Abstract

Computational docking methods can provide structural models of protein-protein complexes, but protein backbone flexibility upon association often thwarts accurate predictions. In recent blind challenges, medium or high accuracy models were submitted in less than 20% of the "difficult" targets (with significant backbone change or uncertainty). Here, we describe recent developments in protein-protein docking and highlight advances that tackle backbone flexibility. In molecular dynamics and Monte Carlo approaches, enhanced sampling techniques have reduced time-scale limitations. Internal coordinate formulations can now capture realistic motions of monomers and complexes using harmonic dynamics. And machine learning approaches adaptively guide docking trajectories or generate novel binding site predictions from deep neural networks trained on protein interfaces. These tools poise the field to break through the longstanding challenge of correctly predicting complex structures with significant conformational change.