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pyDock: Electrostatics and desolvation for effective scoring of rigid‐body protein–protein docking
Author(s) -
Cheng Tammy ManKuang,
Blundell Tom L.,
FernandezRecio Juan
Publication year - 2007
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.21419
Subject(s) - docking (animal) , searching the conformational space for docking , protein–ligand docking , macromolecular docking , rigid body , computer science , electrostatics , virtual screening , molecular dynamics , protein structure , computational chemistry , chemistry , physics , classical mechanics , medicine , biochemistry , nursing
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. Proteins 2007. © 2007 Wiley‐Liss, Inc.