Limits...
CSAR benchmark exercise 2011-2012: evaluation of results from docking and relative ranking of blinded congeneric series.

Damm-Ganamet KL, Smith RD, Dunbar JB, Stuckey JA, Carlson HA - J Chem Inf Model (2013)

Bottom Line: Lastly, we found that minimizing the protein and correcting histidine tautomeric states positively trended with low RMSD for pose prediction but minimizing the ligand negatively trended.Optimizing docking parameters and pretraining with the native ligand had a positive effect on the docking performance as did using restraints, substructure fitting, and shape fitting.Here, by combining the results of many methods, we hope to provide a statistically relevant evaluation and elucidate specific shortcomings of docking methodology for the community.

View Article: PubMed Central - PubMed

Affiliation: Department of Medicinal Chemistry, University of Michigan, Ann Arbor, Michigan 48109-1065, USA.

ABSTRACT
The Community Structure-Activity Resource (CSAR) recently held its first blinded exercise based on data provided by Abbott, Vertex, and colleagues at the University of Michigan, Ann Arbor. A total of 20 research groups submitted results for the benchmark exercise where the goal was to compare different improvements for pose prediction, enrichment, and relative ranking of congeneric series of compounds. The exercise was built around blinded high-quality experimental data from four protein targets: LpxC, Urokinase, Chk1, and Erk2. Pose prediction proved to be the most straightforward task, and most methods were able to successfully reproduce binding poses when the crystal structure employed was co-crystallized with a ligand from the same chemical series. Multiple evaluation metrics were examined, and we found that RMSD and native contact metrics together provide a robust evaluation of the predicted poses. It was notable that most scoring functions underpredicted contacts between the hetero atoms (i.e., N, O, S, etc.) of the protein and ligand. Relative ranking was found to be the most difficult area for the methods, but many of the scoring functions were able to properly identify Urokinase actives from the inactives in the series. Lastly, we found that minimizing the protein and correcting histidine tautomeric states positively trended with low RMSD for pose prediction but minimizing the ligand negatively trended. Pregenerated ligand conformations performed better than those that were generated on the fly. Optimizing docking parameters and pretraining with the native ligand had a positive effect on the docking performance as did using restraints, substructure fitting, and shape fitting. Lastly, for both sampling and ranking scoring functions, the use of the empirical scoring function appeared to trend positively with the RMSD. Here, by combining the results of many methods, we hope to provide a statistically relevant evaluation and elucidate specific shortcomings of docking methodology for the community.

Show MeSH
Number of raw packing contacts in co-crystal versus numberof rawpacking contacts in prediction. The solid line illustrates a perfectmatch, while the dotted lines show a ±10% range. (A) RMSD <1 Å bin. (B) RMSD = 1–2 Å bin.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3753884&req=5

fig8: Number of raw packing contacts in co-crystal versus numberof rawpacking contacts in prediction. The solid line illustrates a perfectmatch, while the dotted lines show a ±10% range. (A) RMSD <1 Å bin. (B) RMSD = 1–2 Å bin.

Mentions: InFigure 6A and B, the RMSD < 1 Åbin and RMSD = 1–2 Å bin for Het–Het contacts areprovided, respectively; the population of each value is given by thesize of the point. As one would expect, when the RMSD is quite small,the majority of the points are close to the identity line. It is alsoobvious from these graphs that when the RMSD is small, the trend thatscoring functions are underpredicting contacts holds true. As theRMSD becomes larger, the data becomes more spread and moves away fromthe identity line (data for bins 2–4, 4–10, and >10Å is provided in the Supporting Information). Furthermore, once the RMSD is greater than 10 Å, almost allof the contacts are off the identity line and being underpredicted.Figure 7A and B show the RMSD < 1 Åbin and RMSD = 1–2 Å bin for C–C contacts and Figure 8A and B for Packing contacts (again data for bins2–4, 4–10, and >10 Å is provided in the Supporting Information). For C–C contacts,the points are spread almost evenly between underprediction and overprediction.The packing contacts agree with what was shown for Het–Hetcontacts, and at small RMSD values, the trend that the scoring functionis underpredicting contacts remains. Again, this is a very interestingfinding as most scoring functions use an additive term for van derWaals packing, and hence, more contacts should result in a betterscore.


CSAR benchmark exercise 2011-2012: evaluation of results from docking and relative ranking of blinded congeneric series.

Damm-Ganamet KL, Smith RD, Dunbar JB, Stuckey JA, Carlson HA - J Chem Inf Model (2013)

Number of raw packing contacts in co-crystal versus numberof rawpacking contacts in prediction. The solid line illustrates a perfectmatch, while the dotted lines show a ±10% range. (A) RMSD <1 Å bin. (B) RMSD = 1–2 Å bin.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3753884&req=5

fig8: Number of raw packing contacts in co-crystal versus numberof rawpacking contacts in prediction. The solid line illustrates a perfectmatch, while the dotted lines show a ±10% range. (A) RMSD <1 Å bin. (B) RMSD = 1–2 Å bin.
Mentions: InFigure 6A and B, the RMSD < 1 Åbin and RMSD = 1–2 Å bin for Het–Het contacts areprovided, respectively; the population of each value is given by thesize of the point. As one would expect, when the RMSD is quite small,the majority of the points are close to the identity line. It is alsoobvious from these graphs that when the RMSD is small, the trend thatscoring functions are underpredicting contacts holds true. As theRMSD becomes larger, the data becomes more spread and moves away fromthe identity line (data for bins 2–4, 4–10, and >10Å is provided in the Supporting Information). Furthermore, once the RMSD is greater than 10 Å, almost allof the contacts are off the identity line and being underpredicted.Figure 7A and B show the RMSD < 1 Åbin and RMSD = 1–2 Å bin for C–C contacts and Figure 8A and B for Packing contacts (again data for bins2–4, 4–10, and >10 Å is provided in the Supporting Information). For C–C contacts,the points are spread almost evenly between underprediction and overprediction.The packing contacts agree with what was shown for Het–Hetcontacts, and at small RMSD values, the trend that the scoring functionis underpredicting contacts remains. Again, this is a very interestingfinding as most scoring functions use an additive term for van derWaals packing, and hence, more contacts should result in a betterscore.

Bottom Line: Lastly, we found that minimizing the protein and correcting histidine tautomeric states positively trended with low RMSD for pose prediction but minimizing the ligand negatively trended.Optimizing docking parameters and pretraining with the native ligand had a positive effect on the docking performance as did using restraints, substructure fitting, and shape fitting.Here, by combining the results of many methods, we hope to provide a statistically relevant evaluation and elucidate specific shortcomings of docking methodology for the community.

View Article: PubMed Central - PubMed

Affiliation: Department of Medicinal Chemistry, University of Michigan, Ann Arbor, Michigan 48109-1065, USA.

ABSTRACT
The Community Structure-Activity Resource (CSAR) recently held its first blinded exercise based on data provided by Abbott, Vertex, and colleagues at the University of Michigan, Ann Arbor. A total of 20 research groups submitted results for the benchmark exercise where the goal was to compare different improvements for pose prediction, enrichment, and relative ranking of congeneric series of compounds. The exercise was built around blinded high-quality experimental data from four protein targets: LpxC, Urokinase, Chk1, and Erk2. Pose prediction proved to be the most straightforward task, and most methods were able to successfully reproduce binding poses when the crystal structure employed was co-crystallized with a ligand from the same chemical series. Multiple evaluation metrics were examined, and we found that RMSD and native contact metrics together provide a robust evaluation of the predicted poses. It was notable that most scoring functions underpredicted contacts between the hetero atoms (i.e., N, O, S, etc.) of the protein and ligand. Relative ranking was found to be the most difficult area for the methods, but many of the scoring functions were able to properly identify Urokinase actives from the inactives in the series. Lastly, we found that minimizing the protein and correcting histidine tautomeric states positively trended with low RMSD for pose prediction but minimizing the ligand negatively trended. Pregenerated ligand conformations performed better than those that were generated on the fly. Optimizing docking parameters and pretraining with the native ligand had a positive effect on the docking performance as did using restraints, substructure fitting, and shape fitting. Lastly, for both sampling and ranking scoring functions, the use of the empirical scoring function appeared to trend positively with the RMSD. Here, by combining the results of many methods, we hope to provide a statistically relevant evaluation and elucidate specific shortcomings of docking methodology for the community.

Show MeSH