Presentation is loading. Please wait.

Presentation is loading. Please wait.

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

Similar presentations


Presentation on theme: "Looking for the Best QSAR and Docking Methods Guillermo Restrepo Laboratorio de Química Teórica, Universidad de Pamplona, Pamplona, Colombia 1."— Presentation transcript:

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

2 2

3 Outline o Ranking o How we rank o Ranking problems o QSAR models o Docking programs o Conclusions o Acknowledgements 3

4 GoodBad 4

5 We love rankings! La romería de San Isidro, Goya 5

6 How do we rank? BeautyIntelligenceGlamour a082 b91312 c215 d e437 Priorities Subjectivities 6

7 q1q1 q2q2 q3q3 a082 b91312 c215 d e437 x y if all q i (x) > q i (y) or at least one attribute (q j ) is higher for x while all others are equal. Comparable Incomparable If at least one q j fulfills q j (x) < q j (y) while the others are opposite (q i (x) q i (y)), x and y are incomparable. Hasse diagram Total set of linear extensions ABCDEF Brüggemann, R.; Restrepo, G.; Voigt, K. J. Chem. Inf. Model. 2006, 46,

8 ABCDEF a22200 b00033 c42000 d00033 e02400 r1r2r3r4r5 a b c d e Ranking probability of having n at m p mn = r mn / |LE| r mn : ocurrence of object n at rank m Average rank of n Av rkn = m mp mn bdbd e a c Min rkAv rkMax rkVar a1232 b c d e Restrepo, G.; Brüggemann, R.; Weckert, M.; Gerstmann, S.; Frank, H. MATCH Commun. Math. Comput. Chem. 2008, 59,

9 Best QSAR methods Case study: o Mutagenicity o 95 aromatic & heteroaromatic amines o 13 QSAR models o Two statistics 9

10 Model labelDescriptorsr2r2 sMethod Basak 1997 Topological and geometric Linear Basak 1998 Topological,geometric and quantum chemical Linear Maran 1999 #rings, γ-polarizability, HASA1 (SCF/AM1), HDSA (SCF/AM1), Etot(C-C), Etot(C-N) Linear Karelson 2000a #rings, γ-polarizability, HASA1 (SCF/AM1), HDSA (SCF/AM1), Etot(C-C), Etot(C-N) Linear Karelson 2000b Ic, 3 κ, #H acceptor sites, max valence N, PNSA 1, γ- polarizability Non-linear Basak 2001a Expanded set of topological, geometric and quantum chemical Linear Basak 2001b Expanded set of topological, geometric, quantum chemical and electrotopological Linear Cash 2001 Electrotopological Linear Toropov 2001 Graphs weighted with contributions of atomic orbitals Linear Vračko 2004a Topostructural, topochemical and geometric Non-linear Vračko 2004b Topostructural, topochemical, geometric and quantum chemical Non-linear Cash 2005a Electrotopological Linear Cash 2005b Electrotopological Linear 10

11 594 linear extensions Maran 1999 Karelson 2000a Basak 2001b Basak 1997 Vračko 2004a,b Basak 2001a Karelson 2000b Basak 1998 Cash 2005b Cash 2005a Cash 2001 Toropov 2001 o Maran 1999 & Karelson 2000a are better than 10 other models. o It is not possible to state whether Karelson 2000b is better or worse than other models. o There are better models than Cash 2001 & Toropov Restrepo, G.; Basak, S. C.; Mills, D. Curr. Comput-Aid Drug. 2011, 7,

12 Min rkAv rkMax rkVar Basak Basak Maran Karelson 2000b Basak 2001a Basak 2001b Cash Toropov Vracko 2004a Cash 2005a Cash 2005b Maran 1999 Karelson 2000a Basak 2001b Basak 1997 Vračko 2004a,b Basak 2001a Karelson 2000b Basak 1998 Cash 2005b Cash 2005a Cash 2001 Toropov o Maran 1999 & Karelson 2000a and Basak 2001b are the less variable models. o Karelson 2000b & Cash 2005b are the most variable models. 12

13 Best Docking methods Case study: o 10 docking programs: Dock4, DockIt, FlexX, Flo, Fred, Glide, Gold, LigFit, MOE, MVP o 8 protein targets o Two main characteristics: o prediction of conformations of small molecules bound to protein targets o 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,

14 Protein-ligand conformations Percentage of compounds for which a docked pose was found within 2 Å of the crystal structure 136 protein/ligand conformations chk1pdfsmrsppardfxagyrbhcvp Dock DockIt FlexX Flo Fred Glide Gold LigFit MOE MVP Kinase Polypeptide deformilase Synthetase Nuclear hormone receptor Serine protease Isomerase Polymerase 14

15 o There are better programs than MOE o There is no program behaving better than the others o Gold performs better than 4 other programs GoldFred Dock4 MOE DockItLigFit MVPFlexXGlideFlo+ Protein-ligand conformations 12,960 linear extensions 15

16 Min rkAv rkMax rkVar Dock DockIt FlexX Flo Fred26108 Glide Gold58105 LigFit MOE1110 MVP26108 Gold FlexX, Flo+, Glide Fred, MVP Dock4, DockIt, LigFit MOE o All programs have variable positions in the ranking, except MOE. o The most suitable docking program to estimate protein- ligand conformations is Gold. 16

17 Enrichment factor for actives (1 μM) found at 10% of the docking- score-ordered list chk1fxagyrbhcvpmrsEcoli-pdfStrep-pdfppard Dock DockIt FlexX Flo Fred Glide Gold LigandFit MOEDock MVP kinase Serine protease IsomerasePolymerase Synthetase Metalloprotease Nuclear hormone receptor Docking as a virtual screening tool 17

18 Gold FredDock4MOE DockIt LigFit MVPFlexX Glide Flo+ Ability to correctly identify all active chemotypes from a population of decoy molecules o MVP works better than 6 of the other programs o DockIt behaves worse than Flo+ and MVP o There is no program behaving better than all the others o Flex, Glide and Gold are the programs for which it is not possible to find a better or worse program Docking as a virtual screening tool 259,200 linear extensions 18

19 Min rkAv rkMax RkVar Dock DockIt FlexX Flo Fred Glide Gold LigFit MOE MVP MVP Flo+ FlexX, Glide, Gold Dock4, Fred, LigFit, MOE DockIt o All programs have quite variable positions in the ranking o The most suitable docking program to identify active chemotypes is MVP 19

20 Conclusions o With 2 statistics characterising QSAR models, we found 2 best models. o … and 2 worse models. o The docking program for protein-ligand conformations with the highest probability of being the best one (21%) is Gold. o MVP has 70% probability of being the best docking program for virtual screening searches. 20

21 Outlook o Why not using more statistics for QSAR models? o Instead of ordering Alice and Bobs models, a work to do is to order QSAR models, e.g. linear & non-linear ones. o Some other attributes of QSAR methods need to be introduced, e.g. related to the applicability domain. o Computational costs and other docking programs features may be included in the study. 21

22 Acknowledgements Rainer Brüggemann Subhash C. Basak 22

23 Thank you! 23


Download ppt "Looking for the Best QSAR and Docking Methods Guillermo Restrepo Laboratorio de Química Teórica, Universidad de Pamplona, Pamplona, Colombia 1."

Similar presentations


Ads by Google