The accurate scoring of rigid-body docking orientations represents one of the 
major difficulties in protein-protein docking prediction. Other challenges are 
the development of faster and more efficient sampling methods and the 
introduction of receptor and ligand flexibility during simulations. Overall, 
good discrimination of near-native docking poses from the very early stages of 
rigid-body protein docking is essential step before applying more costly 
interface refinement to the correct docking solutions. Here we explore a simple 
approach to scoring of rigid-body docking poses, which has been implemented in a 
program called pyDock. The scheme is based on Coulombic electrostatics with 
distance dependent dielectric constant, and implicit desolvation energy with 
atomic solvation parameters previously adjusted for rigid-body protein-protein 
docking. This scoring function is not highly dependent on specific geometry of 
the docking poses and therefore can be used in rigid-body docking sets generated 
by a variety of methods. We have tested the procedure in a large benchmark set 
of 80 unbound docking cases. The method is able to detect a near-native solution 
from 12,000 docking poses and place it within the 100 lowest-energy docking 
solutions in 56% of the cases, in a completely unrestricted manner and without 
any other additional information. More specifically, a near-native solution will 
lie within the top 20 solutions in 37% of the cases. The simplicity of the 
approach allows for a better understanding of the physical principles behind 
protein-protein association, and provides a fast tool for the evaluation of 
large sets of rigid-body docking poses in search of the near-native orientation.