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

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

6 How do we rank? BeautyIntelligenceGlamour a082 b91312 c215 d101411 e437 Priorities Subjectivities 6

7 q1q1 q2q2 q3q3 a082 b91312 c215 d101411 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, 894-902. 7

8 ABCDEF 12345 a22200 b00033 c42000 d00033 e02400 r1r2r3r4r5 a0.333 00 b0000.5 c0.6670.333000 d0000.5 e00.3330.66700 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 1 2 3 4 5 Min rkAv rkMax rkVar a1232 b44.551 c11.33321 d44.551 e22.66731 Restrepo, G.; Brüggemann, R.; Weckert, M.; Gerstmann, S.; Frank, H. MATCH Commun. Math. Comput. Chem. 2008, 59, 555-584. 8

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 geometric0.7970.910Linear Basak 1998 Topological,geometric and quantum chemical0.7900.920Linear Maran 1999 #rings, γ-polarizability, HASA1 (SCF/AM1), HDSA (SCF/AM1), Etot(C-C), Etot(C-N) 0.8340.811Linear Karelson 2000a #rings, γ-polarizability, HASA1 (SCF/AM1), HDSA (SCF/AM1), Etot(C-C), Etot(C-N) 0.8340.811Linear Karelson 2000b Ic, 3 κ, #H acceptor sites, max valence N, PNSA 1, γ- polarizability 0.8951.333Non-linear Basak 2001a Expanded set of topological, geometric and quantum chemical 0.7940.912Linear Basak 2001b Expanded set of topological, geometric, quantum chemical and electrotopological 0.8210.840Linear Cash 2001 Electrotopological0.7670.979Linear Toropov 2001 Graphs weighted with contributions of atomic orbitals0.7580.950Linear Vračko 2004a Topostructural, topochemical and geometric0.7930.840Non-linear Vračko 2004b Topostructural, topochemical, geometric and quantum chemical 0.7930.840Non-linear Cash 2005a Electrotopological0.7600.950Linear Cash 2005b Electrotopological0.7500.890Linear 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 2001. Restrepo, G.; Basak, S. C.; Mills, D. Curr. Comput-Aid Drug. 2011, 7, 109-121. 11

12 Min rkAv rkMax rkVar Basak 199768.242493 Basak 199845.090962 Maran 19991010.909111 Karelson 2000b161110 Basak 2001a56.666783 Basak 2001b99.8182101 Cash 200112.545554 Toropov 200111.69743 Vracko 2004a67.757693 Cash 2005a23.393953 Cash 2005b13.878887 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 1 11 2 3 4 5 6 7 8 9 10 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, 5912-5931. 13

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 Dock472519210290 DockIt4725371008 FlexX7375393740430 Flo6088458050038 Fred73505801000 Glide6750743340298 Gold53889478404331 LigFit4063015000 MOE0000000 MVP873842410430 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 Dock423.583375 DockIt23.583375 FlexX47.75106 Flo+47.75106 Fred26108 Glide47.75106 Gold58105 LigFit23.583375 MOE1110 MVP26108 Gold FlexX, Flo+, Glide Fred, MVP Dock4, DockIt, LigFit MOE 1 2 3 4 5 6 7 8 9 10 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 Dock41.44.11.71.84.20.90.81.7 DockIt4.222110.203.2 FlexX72.25.80.93.90.8 5.2 Flo+5.62.72.33.41.71.50.83.6 Fred2.94.11.920.63.21.21.1 Glide6.33.4115.30.60.44.8 Gold0.14.1400.810.15.5 LigandFit3.31.92.81.82.9 1.71.2 MOEDock3.90.60012.10.60 MVP7.25.85.33.66.46.76.93.9 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 Dock414.812598 DockIt13.208387 FlexX15.5109 Flo+26.416797 Fred14.812598 Glide15.5109 Gold15.5109 LigFit14.812598 MOE14.812598 MVP79.625103 MVP Flo+ FlexX, Glide, Gold Dock4, Fred, LigFit, MOE 1 2 3 4 5 6 7 8 9 10 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


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