  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.
