proteins STRUCTURE O FUNCTION O BIOINFORMATICS A simple reference state makes a significant improvement in near-native selections from structurally refined docking decoys Shide Liang,1,2,3 Song Liu,1 Chi Zhang,1,3 and Yaoqi Zhou1,3* 1 Howard Hughes Medical Institute Center for Single Molecule Biophysics, Department of Physiology and Biophysics, State University of New York at Buffalo, Buffalo, NY 14214 2 Department of Marine Biology, Ocean University of China, Qingdao, 266003 3 Indiana University School of Informatics, Indiana University-Purdue University at Indianapolis and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana 46202 ABSTRACT Near-native selections from docking decoys have proved challenging espe- cially when unbound proteins are used in the molecular docking. One reason is that significant atomic clashes in docking decoys lead to poor predictions of binding affinities of near native decoys. Atomic clashes can be removed by structural refinement through energy minimization. Such an energy minimization, however, will lead to an unrealistic bias toward docked struc- tures with large interfaces. Here, we extend an empirical energy function developed for protein design to protein– protein docking selection by introduc- ing a simple reference state that removes the unrealistic dependence of binding affinity of docking decoys on the buried solvent accessible surface area of interface. The energy function called EMPIRE (EMpirical Protein- InteRaction Energy), when coupled with a refinement strategy, is found to provide a significantly improved suc- cess rate in near native selections when applied to RosettaDock and refined ZDOCK docking decoys. Our work underlines the importance of removing nonspecific interactions from specific ones in near native selections from docking decoys. Proteins 2007; 69:244–253. VC 2007 Wiley-Liss, Inc. Key words: knowledge-based potential; energy score functions; reference state; binding affinity; docking decoys. 244 PROTEINS INTRODUCTION Docking prediction refers to the prediction of the structure of a protein– protein complex from the structures of individual subunits. This is a challenging task because an unbound subunit often changes its conformation upon binding with its partner (induced fit). Docking prediction involves decoy generation and the selection of the near-native structure from decoys using a filter and/or energy function. Thus, the success of docking prediction requires an efficient method that samples complex conformations and an accurate energy function that ranks the near-native conformations as low energy conformations. Advan- ces in sampling methods and energy functions for docking have been high- lighted in several recent reviews.1–13 Various energy functions have been used in docking prediction to separate near-native structures from other structures. They are classified into two groups: ‘‘integrated’’ and ‘‘edge’’ functions based on whether or not they were used directly in sampling procedures or applied at the end of sampling procedures.4 Energy functions are also classified based on the methods used to obtain them. Physical-based energy functions,14–17 derived based on the laws of physics, have been applied to docking [e.g., DARWIN,18 DOT,19 Hex,20 Guided Docking,21 TSCF,22 SmoothDock23]. Some docking algorithms use semi-empirical energy functions that combine various physical terms such as surface complementarity, van der Waals interaction, generalized Born-surface area (GB/SA), and hydrogen bonding with optimized weight factors. Examples are Dock,24–26 ICM- DISCO,27 PPD,28,29 GRAMM,30 FTDOCK,31 3D-DOCK,32 AutoDock,33 Surfdock,34 GAPDOCK,35 MolFit,36,37 BIGGER,38 Northwestern DOCK,39 ZDOCK,40 and RosettaDock.41 Still others use statistical energy functions derived from known protein structures.42–49 The use of energy functions is of- ten accompanied with clustering to incorporate entropic contribution as demon- strated in recent CAPRI (Critical Assessment of PRedicted Interaction) (for example, Refs. 50–54). Moreover, predicted protein–protein conformations of- ten contain steric clashes and wrong sidechain conformations. Thus, sidechain Grant sponsor: NIH; Grant numbers: R01 GM 966049 and R01 GM 068530; Grant sponsors: HHMI, Center for computation Research, Keck Center for Computational Biology; Grant sponsor: Natural Science Foundation of China; Grant number: 30400096. *Correspondence to: Dr. Yaoqi Zhou, Indiana University School of Informatics, Indiana University-Purdue University at Indianapolis and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, 719 Indiana Ave. Rm 319, Indianapolis, IN 46202. E-mail: yqzhou@iupui.edu Received 29 November 2006; Revised 11 February 2007; Accepted 7 March 2007 Published online 10 July 2007 in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/prot.21498 VC 2007 WILEY-LISS, INC.