Selecting near-native conformations from the immense number of conformations 
generated by docking programs remains a major challenge in molecular docking. We 
introduce DockRank, a novel approach to scoring docked conformations based on 
the degree to which the interface residues of the docked conformation match a 
set of predicted interface residues. DockRank uses interface residues predicted 
by partner-specific sequence homology-based protein-protein interface predictor 
(PS-HomPPI), which predicts the interface residues of a query protein with a 
specific interaction partner. We compared the performance of DockRank with 
several state-of-the-art docking scoring functions using Success Rate (the 
percentage of cases that have at least one near-native conformation among the 
top m conformations) and Hit Rate (the percentage of near-native conformations 
that are included among the top m conformations). In cases where it is possible 
to obtain partner-specific (PS) interface predictions from PS-HomPPI, DockRank 
consistently outperforms both (i) ZRank and IRAD, two state-of-the-art 
energy-based scoring functions (improving Success Rate by up to 4-fold); and 
(ii) Variants of DockRank that use predicted interface residues obtained from 
several protein interface predictors that do not take into account the binding 
partner in making interface predictions (improving success rate by up to 
39-fold). The latter result underscores the importance of using partner-specific 
interface residues in scoring docked conformations. We show that DockRank, when 
used to re-rank the conformations returned by ClusPro, improves upon the 
original ClusPro rankings in terms of both Success Rate and Hit Rate. DockRank 
is available as a server at http://einstein.cs.iastate.edu/DockRank/.