Pushing the Backbone in Protein-Protein Docking

Conformational changes of proteins that occur upon binding typically confound computational docking algorithms. In this study, we test computational methods to capture protein backbone conformational change related to binding. To address how well existing algorithms can sample bound-like backbones, we query seven techniques including Monte Carlo-based sampling, molecular dynamics, and normal mode analysis. All methods tested rarely sample near-bound states from the unbound conformation. Nevertheless, the direction of the predicted motions overlap with the actual conformational change. We next forced the backbone from the unbound toward the bound conformation to create a family of docking energy landscapes. Seventy percent of docking targets succeed when the unbound backbones is pushed to within 0.6 Å of the bound. Current methods can capture an average of 22% of unbound-bound transitions through conformer selection methods and another 57% through induced-fit methodologies, delineating a stubborn gap (21%) in backbone motion not covered by any current approach.