Improved protein docking by predicted interface residues.
Scoring docking solutions is a difficult task, and many methods have
been developed for this purpose. In docking, only a handful of the
hundreds of thousands of models generated by docking algorithms are
acceptable, causing difficulties when developing scoring functions.
Today’s best scoring functions can significantly increase the number of
top-ranked models but still fails for most targets. Here, we examine the
possibility of utilising predicted residues on a protein-protein
interface to score docking models generated during the scan stage of a
docking algorithm. Many methods have been developed to infer the
portions of a protein surface that interact with another protein, but
most have not been benchmarked using docking algorithms. Different
interface prediction methods are systematically tested for scoring
>300.000 low-resolution rigid-body template free docking
decoys. Overall we find that BIPSPI is the best method to identify
interface amino acids and score docking solutions. Further, using BIPSPI
provides better docking results than state of the art scoring functions,
with >12% of first ranked docking models being acceptable.
Additional experiments indicated precision as a high-importance metric
when estimating interface prediction quality, focusing on docking
constraints production. We also discussed several limitations for the
adoption of interface predictions as constraints in a docking protocol.