S.Liang et al. Table II The Ranks and rmsd Values of Refined Structures in ZDOCK2.3 Decoy Sets No. of hitsb Rank(rmsd)c Sidechaine Minimizationf Complexa PDB ID# 1CGI 1CHO 2PTC 1TGS 2SNI 2SIC 1CSE 2KAI 1BRC 1ACB 1BRS 1MAH 1UGH 1DFJ 1FSS 1AVW 1PPEg 1TABg 1UDIg 1STFg 2TECg 4HTCg 1MLC 1WEJ 1AHW 1DQJ 1BVK 1FBIg 2JELg 1BQLg 1JHLg 1NCAg 1NMBg 1MELg 2VIRg 1EO8g 1QFUg 1IAIg 2PCC 1WQ1 1AVZ 1MDA 1IGCg 1ATNg 1GLAg 1SPBg 2BTFg 1A0Og Top 1 (Top 10) 77 99 48 109 1 52 29 16 54 93 21 28 20 51 15 52 393 50 35 83 185 57 17 22 67 0 2 5 35 70 12 67 9 71 3 2 18 3 0 54 0 0 3 24 0 112 35 4 Originald 107 (1.54) 1 (1.26) 8 (1.03) 10 (2.46) 425 (2.22) 2 (2.06) 1 (0.50) 151 (2.30) 21 (1.21) 2 (1.44) 20 (1.30) 238 (1.78) 1069 (1.60) 517 (2.38) 54 (1.04) 1 (1.89) 1 (0.52) 1 (0.51) 12 (1.06) 1 (0.80) 1 (0.68) 45 (1.40) 46 (2.46) 5 (0.91) 25 (1.41) ( ) 672 (2.34) 1593 (2.18) 598 (1.90) 14 (0.68) 121 (1.16) 8 (1.51) 1 (0.99) 2 (1.36) 79 (1.03) 55 (0.94) 21 (0.75) 52 (1.47) ( ) 121 (2.23) ( ) ( ) 141 (1.18) 1 (0.56) ( ) 2 (0.61) 1 (0.65) 21 (2.45) 10 (18) 48 (2.02) 1 (1.01) 1 (0.44) 4 (1.55) 617 (2.22) 3 (2.06) 5 (1.10) 3 (1.69) 1 (1.73) 14 (1.44) 26 (1.97) 104 (0.84) 66 (1.13) 1 (1.70) 1 (1.07) 12 (1.48) 1 (1.46) 1 (1.56) 1 (0.94) 1 (1.42) 1 (1.25) 1 (0.69) 395 (2.46) 12 (0.57) 7 (1.75) ( ) 450 (2.34) 534 (2.18) 20 (1.16) 11 (0.84) 9 (1.16) 56 (0.83) 427 (0.99) 3 (1.01) 527 (1.03) 607 (0.94) 92 (0.78) 106 (1.47) ( ) 10 (1.88) ( ) ( ) 785 (1.20) 1 (0.52) ( ) 1 (0.61) 1 (1.02) 13 (2.25) 14 (22) 1 (2.18) 1 (1.57) 1 (0.44) 3 (1.85) 92 (2.22) 3 (1.04) 4 (1.24) 28 (1.69) 1 (2.30) 4 (0.93) 15 (2.29) 1 (0.89) 1 (1.60) 1 (1.70) 2 (1.05) 1 (1.53) 1 (0.87) 1 (1.56) 1 (0.79) 1 (1.01) 1 (0.92) 1 (0.69) 338 (2.46) 62 (0.57) 4 (1.23) ( ) 419 (2.34) 447 (2.18) 1 (1.09) 9 (0.84) 50 (1.85) 2 (1.93) 337 (1.13) 1 (1.07) 521 (1.19) 72 (0.94) 1 (0.78) 429 (1.70) ( ) 9 (1.20) ( ) ( ) 227 (1.18) 1 (0.80) ( ) 1 (0.95) 1 (0.83) 427 (2.45) 20 (29) aEnzyme/Inhibitor: the first 22 protein complexes (1CGI-4HTC), antibody-antigen: the next 16 protein complexes (1MLC-1IAI). The rest are 10 other complexes. bThe number of hits (near-native structures with interface rmsd < 2.5A˚ ). cThe highest rank of hits (and its interface rmsd). dOriginal decoys without any refinement. eResults after sidechain optimization. fResults after sidechain optimization and energy minimization. gDocking decoys from unbound and bound structures. chain optimization and decoys with further energy minimization. Near-native structures of 10 proteins (out of 43 proteins with near-native structures, 23%) are ranked as top 1 in 248 PROTEINS the direct application of the scoring function to the ZDOCK 2.3 decoy set. The number of correctly ranked near-native proteins increases to 14 (33%) after sidechain optimization and 20 (47%) after further energy minimiza- tion. The number of near-native structures that are ranked within top 10 also increases from 18 (42%) for the origi- nal, 22 (51%) for sidechain optimizied, to 29 (67%) for energy-minimized decoys. This highlights the importance of sidechain optimization and energy minimization. The success rates for enzyme/inhibitor group are more impressive. In a total of 22 targets, there are 11 (50%) and 14 (64%) complexes whose near-native structures are ranked number 1 after sidechain optimization and after energy minimization, respectively. For the 16 antigen-anti- body complexes, however, none of their near-native struc- tures are successfully ranked as number 1 after sidechain modeling and only three after energy minimization. Among the 10 other targets, 4 do not contain any near- native structures. The success rates are 50% before and after energy minimization for the remaining six targets. The main reason behind different success rates for dif- ferent types of complexes is that the enzyme/inhibitor group has significantly more near-native structures per target in 2000 decoys than those of antigen-antibody com- plexes. In fact, for all targets with 24 or more near-native structures in their decoys (1.2%), there is at least one near-native structure ranked within top 10 after energy minimization. This is true regardless of actual type of complex structures (enzyme/inhibitor, antigen-antibody, or others). Thus, the success rate is largely determined by the quality of docking conformations (i.e., the population of near-native structures). To further illustrate the importance of refinement, one example for the ribonuclease A/ribonuclease inhibitor complex (1dfj) is shown in Figure 1. Before any structural refinement, strong atomic clashes make the binding free energies of all docking decoys positive [Fig. 1(a)]. Side- chain optimization removes most steric clashes and major- ity of docking decoys now have negative (attractive) bind- ing free energies [Fig. 1(b)]. Most near-native structures, however, do not yet distinguish significantly from other decoys. Only further energy minimization [Fig. 1(c)] makes a clear identification of near-natives as a cluster of structures that are separated from the rest. The reference state used in EMPIRE plays an essential role in the accuracy of EMPIRE. Figure 2 compares the performance made by EMPIRE with or without the refer- ence state for the original ZDOCK decoys, decoys after sidechain optimizations, and decoys after further 50, 100, and 200 steps of energy minimization. It is clear that the use of the reference state leads to a significant increase in the number of successful predictions for the refined com- plexes. Another interesting result is that 50 steps of mini- mization lead to the highest success rate for including near-native structures within the top 10, in particular. We found that 50 steps of minimization yield the binding DOI 10.1002/prot