A combination of rescoring and refinement significantly improves protein docking performance

Abstract

To determine the structures of protein-protein interactions, protein docking is a valuable tool that complements experimental methods to characterize protein complexes. Although protein docking can often produce a near-native solution within a set of global docking predictions, there are sometimes predictions that require refinement to elucidate correct contacts and conformation. Previously, we developed the ZRANK algorithm to rerank initial docking predictions from ZDOCK, a docking program developed by our lab. In this study, we have applied the ZRANK algorithm toward refinement of protein docking models in conjunction with the protein docking program RosettaDock. This was performed by reranking global docking predictions from ZDOCK, performing local side chain and rigid-body refinement using RosettaDock, and selecting the refined model based on ZRANK score. For comparison, we examined using RosettaDock score instead of ZRANK score, and a larger perturbation size for the RosettaDock search, and determined that the larger RosettaDock perturbation size with ZRANK scoring was optimal. This method was validated on a protein-protein docking benchmark. For refining docking benchmark predictions from the newest ZDOCK version, this led to improved structures of top-ranked hits in 20 of 27 cases, and an increase from 23 to 27 cases with hits in the top 20 predictions. Finally, we optimized the ZRANK energy function using refined models, which provides a significant improvement over the original ZRANK energy function. Using this optimized function and the refinement protocol, the numbers of cases with hits ranked at number one increased from 12 to 19 and from 7 to 15 for two different ZDOCK versions. This shows the effective combination of independently developed docking protocols (ZDOCK/ZRANK, and RosettaDock), indicating that using diverse search and scoring functions can improve protein docking results.

Figure 1

Hit success rate (top) and hit and near-hit success rate (bottom) for ZDOCK 2.3 and ZDOCK 3.0 with and without ZRANK for the rigid-body cases of Benchmark 2.0, versus number of predictions allowed (N_p). Hits are defined as having interface RMSD less than or equal to 2.5 Å from the complex structure determined by x-ray crystallography, and for near-hits the RMSD is between 2.5 Å and 4.0 Å.

Figure 2

Protocol employed for docking and refinement (alternative protocols employed in this study are indicated with dashed lines). The initial stage, which produces 20 rigid-body models, includes ZDOCK followed by ZRANK (alternatively the top 20 ZDOCK models are used). The model refinement, which is the focus of this study, employs RosettaDock to refine each model to generate 300 structures per rigid body prediction. These structures are rescored by ZRANK and the top scoring model is selected from each set of 300. The resultant 20 predictions are reranked using ZRANK score (alternatively RosettaDock score is used to select and rerank the structures).

Figure 3

Histogram of interface RMSD change for all hit and near-hit models after refinement using several search/scoring strategies. Each bin represents the interface RMSD after refinement minus the interface RMSD of the model before refinement. Default Pert = RosettaDock refinement with default perturbation size, Large Pert = RosettaDock refinement with large perturbation size, Rosetta = RosettaDock score used to select the predictions, ZRANK = ZRANK score used to select the predictions.

Figure 4

Percent of models with RMSD improvement for several search/scoring strategies, binned by initial interface RMSD of the models. The dotted line represents 50% success rate. Protocols and abbreviations are the same as Figure 3.

Figure 5

Percent of models with hits after refinement for several search/scoring strategies. binned by initial interface RMSD of the models. Protocols and abbreviations are the same as Figure 3.

Figure 6

Refinement of three test cases, 1MLC, 1RLB, and 1CGI, with Rosetta scores (top) and ZRANK scores (bottom) versus interface RMSD of the predictions. For each case, 300 refinement models were generated for each of 10 input structures from ZD2.3ZR (1MLC), ZD3.0ZR (1RLB), and 1CGI (ZD3.0), using the large perturbation size for RosettaDock refinement. Each point represents the score for one refinement model, and each point type represents refinement models for one input prediction. For each input model, the top scoring refined model was retained for evaluation.

Figure 7

Success rates of refinement for ZD3.0ZR predictions for hit and near-hit cases for various numbers of RosettaDock refinement models. Success is defined as the number of cases (out of 27 hit and near-hit cases from this set) that have a hit in a given number of top-ranked predictions (N_p). The large perturbation size was used for RosettaDock, and ZRANK scoring was used to select and rerank the refined model. Random subsets of RosettaDock refined models were selected from a total of 300 for the smaller numbers of models.

Figure 8

Refinement of ZD3.0ZR prediction #12 for test case 1IQD (Factor VIII/Fab), with input ligand (red), refined ligand (green) and bound ligand (blue). The bound receptor is colored gray. The refined structure was chosen by ZRANK score of the 300 refined models from Rosetta. Figure generated with Pymol.