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Looking for the Best QSAR and Docking Methods

Looking for the Best QSAR and Docking Methods. Guillermo Restrepo Laboratorio de Química Teórica , Universidad de Pamplona, Pamplona, Colombia. Outline. Ranking How we rank Ranking problems QSAR models Docking programs Conclusions Acknowledgements. “Good”. “Bad”. 1. 2. 3. 4. 5.

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Looking for the Best QSAR and Docking Methods

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  1. Looking for the Best QSAR and Docking Methods Guillermo Restrepo Laboratorio de QuímicaTeórica, Universidad de Pamplona, Pamplona, Colombia

  2. Outline • Ranking • How we rank • Ranking problems • QSAR models • Docking programs • Conclusions • Acknowledgements

  3. “Good” “Bad”

  4. 1 2 3 4 5 6 We love rankings! La romería de San Isidro, Goya

  5. How do we rank? •  Priorities •  •  Subjectivities

  6. Comparable x≥ y if all qi(x) > qi(y) or at least one attribute (qj) is higher for xwhile all others are equal. Incomparable If at least one qjfulfills qj(x) < qj(y) while the others are opposite (qi(x) ≥ qi (y)), x and y are incomparable. A B C D E F Hasse diagram Total set of linear extensions Brüggemann, R.; Restrepo, G.; Voigt, K. J. Chem. Inf. Model.2006, 46, 894-902.

  7. rmn: ocurrence of object n at rank m A B C D E F 5 b d 4 3 e a 2 Average rank of n Av rkn= ∑mm∙pmn Ranking probability of having n at m c 1 pmn= rmn/ |LE| Restrepo, G.; Brüggemann, R.; Weckert, M.; Gerstmann, S.; Frank, H. MATCH Commun. Math. Comput. Chem.2008, 59, 555-584.

  8. Best QSAR methods • Case study: • Mutagenicity • 95 aromatic & heteroaromatic amines • 13 QSAR models • Two statistics

  9. Maran 1999 & Karelson 2000a are better than 10 other models. • It is not possible to state whether Karelson 2000b is better or worse than other models. • There are better models than Cash 2001 & Toropov 2001. Maran1999 Karelson2000a Karelson 2000b Basak 2001b Basak 1997 Vračko2004a,b Basak 2001a Cash 2005b Basak 1998 Cash 2001 Cash 2005a 594 linear extensions Toropov 2001 Restrepo, G.; Basak, S. C.; Mills, D. Curr. Comput-Aid Drug. 2011, 7, 109-121.

  10. 11 Maran1999 Karelson2000a 10 Basak 2001b 9 Basak 1997 8 Vračko2004a,b 7 Basak 2001a 6 Karelson 2000b Basak 1998 5 • Maran1999 & Karelson2000a and Basak 2001b are the less variable models. • Karelson 2000b & Cash 2005b are the most variable models. 4 Cash 2005b Cash 2005a 3 Cash 2001 2 Toropov 2001 1

  11. Best Docking methods • Case study: • 10 docking programs: Dock4, DockIt, FlexX, Flo, Fred, Glide, Gold, LigFit, MOE, MVP • 8 protein targets • Two main characteristics: • prediction of conformations of small molecules bound to protein targets • virtual screening of compound databases to identify leads for a protein target Warren, G. L.; Andrews, C. W.; Capelli, A-M.; Clarke, B.; LaLonde, J.; Lambert, M. H.; Lindvall, M.; Nevins, N.; Semus, S. F.; Senger, S.; Tedesco, G.; Wall, I. D.; Woolven, J. M.; Peishoff, C. E.; Head, M. S. J. Med. Chem. 2006, 49, 5912-5931.

  12. Protein-ligand conformations Percentage of compounds for which a docked pose was found within 2 Å of the crystal structure Nuclear hormone receptor Polypeptide deformilase Serine protease Polymerase Synthetase Isomerase Kinase 136 protein/ligand conformations

  13. Protein-ligand conformations • There are better programs than MOE • There is no program behaving better than the others • Gold performs better than 4 other programs MVP Fred FlexX Glide Flo+ Gold Dock4 DockIt LigFit MOE 12,960 linear extensions

  14. 10 9 Gold 8 FlexX, Flo+, Glide 7 6 Fred, MVP 5 • All programs have variable positions in the ranking, except MOE. • The most suitable docking program to estimate protein-ligand conformations is Gold. 4 Dock4, DockIt, LigFit 3 2 MOE 1

  15. Docking as a virtual screening tool Enrichment factor for actives (≤1 μM) found at 10% of the docking-score-ordered list Nuclear hormone receptor Metalloprotease Serine protease Metalloprotease Polymerase Synthetase Isomerase kinase

  16. Docking as a virtual screening tool Ability to correctly identify all active chemotypes from a population of decoy molecules • MVP works better than 6 of the other programs • DockIt behaves worse than Flo+ and MVP • There is no program behaving better than all the others • Flex, Glide and Gold are the programs for which it is not possible to find a better or worse program Glide Gold FlexX MVP Dock4 Flo+ Fred LigFit MOE DockIt 259,200 linear extensions

  17. 10 MVP 9 8 7 Flo+ 6 FlexX, Glide, Gold 5 Dock4, Fred, LigFit, MOE 4 • All programs have quite variable positions in the ranking • The most suitable docking program to identify active chemotypes is MVP DockIt 3 2 1

  18. Conclusions • With 2 statistics characterising QSAR models, we found 2 best models. • … and 2 “worse” models. • The docking program for protein-ligand conformations with the highest probability of being the best one (21%) is Gold. • MVP has 70% probability of being the best docking program for virtual screening searches.

  19. Outlook • Why not using more statistics for QSAR models? • Instead of ordering Alice and Bob’s models, a work to do is to order QSAR models, e.g. linear & non-linear ones. • Some other attributes of QSAR methods need to be introduced, e.g. related to the applicability domain. • Computational costs and other docking programs features may be included in the study.

  20. Acknowledgements Rainer Brüggemann Subhash C. Basak

  21. Thank you!

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