Iagonal. Only one test case has a hit for the standard approach and not for the hybridresolution approach. Thus for this case the near-native region of the 6D space is not sampled in the top 400 predictions of the first stage, which may be due to the score being sensitive to small perturbations. For the top 1000 predictions, we see that when the IRMSD’s are different, they are generally lower using the standard approach. However, we see that only three hits (out of 176 test cases) are not shared by the two 25033180 approaches, with two hits generated by the standard approach only and one hit generated by the hybrid-resolution approach only. These results show that the two approaches generally make highly similar predictions.Consequently, we used the top 10 success rate (ranging from zero to one) to assess the performance of the density-based clustering. Using the ClusPro approach, we found best SRs of 0.222 and 0.227 using RMSD and angular distance, get 125-65-5 respectively, at ?thresholds in the range of 5? A and at 20u, respectively. Using hierarchical clustering, we found best SRs of 0.222 and 0.233 using RMSD and angular distance, respectively, with branch ?cutoff at heights in the ranges of 14?8 A and 57?9u, respectively. Thus the RMSD and angular distance again yield similar performance, with the angular distance slightly outperforming RMSD. When we use the score-based pruning algorithm, we find top ten SRs of 0.233 using both RMSD and angular distance.Funnel AnalysisA collection of predictions that are similar (typically defined as low RMSD’s) can contain more purchase Hypericin information than a single prediction. In its simplest form, one can average the scores of grouped predictions to alleviate random errors. More complex is to analyze the relationship between the predictions, specifically whether they form a `funnel’ where the best prediction is assumed to be at the center. Here we analyze funnels in a simple way by calculating the slope and intersect (using linear regression) of a collection of nearest neighbors of a given prediction. A larger slope means a better-defined funnel, and the intersect is an estimate of the score at the center of the funnel. In Table 1 we show the ISR’s obtained with angular distance and RMSD while varying the total number of predictions used to characterize the funnel. RMSD and angular distance show similar behaviors, but angular distance shows the best performance. The ZDOCK score on its own with 6u and 15u sampling gives ISR = 0.241 and ISR = 0.287, respectively. Thus by taking into account either RMSD or angular distance funnel, we can obtain an improvement over the raw scores. In the Figures 8 and 9 we show the performance of the funnels using angular distance, 6u sampling, and 10 neighbors. We also constructed a weighted linear combination of the ZDOCK score and the intercept and slope funnel properties, which gives ISR = 0.300.Pruning and ClusteringProtein-protein docking algorithms typically make a large number of predictions, many of which are very similar. Therefore, before further refining the predictions, the set is usually pruned or clustered to remove the redundant predictions. Using our benchmark, we determined the threshold that maximizes the ISR for pruning using similarity based on RMSD and using similarity based on angular distance. For RMSD based pruning the optimal ?threshold was 6 A and for angular distance pruning the optimal threshold was 19u. For these cutoffs, the angular distance and RMSD based pruning reta.Iagonal. Only one test case has a hit for the standard approach and not for the hybridresolution approach. Thus for this case the near-native region of the 6D space is not sampled in the top 400 predictions of the first stage, which may be due to the score being sensitive to small perturbations. For the top 1000 predictions, we see that when the IRMSD’s are different, they are generally lower using the standard approach. However, we see that only three hits (out of 176 test cases) are not shared by the two 25033180 approaches, with two hits generated by the standard approach only and one hit generated by the hybrid-resolution approach only. These results show that the two approaches generally make highly similar predictions.Consequently, we used the top 10 success rate (ranging from zero to one) to assess the performance of the density-based clustering. Using the ClusPro approach, we found best SRs of 0.222 and 0.227 using RMSD and angular distance, respectively, at ?thresholds in the range of 5? A and at 20u, respectively. Using hierarchical clustering, we found best SRs of 0.222 and 0.233 using RMSD and angular distance, respectively, with branch ?cutoff at heights in the ranges of 14?8 A and 57?9u, respectively. Thus the RMSD and angular distance again yield similar performance, with the angular distance slightly outperforming RMSD. When we use the score-based pruning algorithm, we find top ten SRs of 0.233 using both RMSD and angular distance.Funnel AnalysisA collection of predictions that are similar (typically defined as low RMSD’s) can contain more information than a single prediction. In its simplest form, one can average the scores of grouped predictions to alleviate random errors. More complex is to analyze the relationship between the predictions, specifically whether they form a `funnel’ where the best prediction is assumed to be at the center. Here we analyze funnels in a simple way by calculating the slope and intersect (using linear regression) of a collection of nearest neighbors of a given prediction. A larger slope means a better-defined funnel, and the intersect is an estimate of the score at the center of the funnel. In Table 1 we show the ISR’s obtained with angular distance and RMSD while varying the total number of predictions used to characterize the funnel. RMSD and angular distance show similar behaviors, but angular distance shows the best performance. The ZDOCK score on its own with 6u and 15u sampling gives ISR = 0.241 and ISR = 0.287, respectively. Thus by taking into account either RMSD or angular distance funnel, we can obtain an improvement over the raw scores. In the Figures 8 and 9 we show the performance of the funnels using angular distance, 6u sampling, and 10 neighbors. We also constructed a weighted linear combination of the ZDOCK score and the intercept and slope funnel properties, which gives ISR = 0.300.Pruning and ClusteringProtein-protein docking algorithms typically make a large number of predictions, many of which are very similar. Therefore, before further refining the predictions, the set is usually pruned or clustered to remove the redundant predictions. Using our benchmark, we determined the threshold that maximizes the ISR for pruning using similarity based on RMSD and using similarity based on angular distance. For RMSD based pruning the optimal ?threshold was 6 A and for angular distance pruning the optimal threshold was 19u. For these cutoffs, the angular distance and RMSD based pruning reta.
