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Looking for the Best QSAR and Docking Methods

Guillermo RestrepoLaboratorio de Química Teórica, Universidad de

Pamplona, Pamplona, Colombia

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Outline

o Rankingo How we ranko Ranking problemso QSAR modelso Docking programso Conclusionso Acknowledgements

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“Good” “Bad”

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We love rankings!

La romería de San Isidro, Goya

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How do we rank?Beauty Intelligence Glamour

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Priorities

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Subjectivities

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q1 q2 q3

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x ≥ y if all qi(x) > qi (y) or at least one attribute (qj) is higher for x while all others are equal.

Comparable

IncomparableIf at least one qj fulfills qj(x) < qj(y) while the others are opposite (qi(x) ≥ qi (y)), x and y are incomparable.

Hasse diagram

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Total set of linear extensions

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Brüggemann, R.; Restrepo, G.; Voigt, K. J. Chem. Inf. Model. 2006, 46, 894-902.

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A B C D E F1 2 3 4 5

a 2 2 2 0 0b 0 0 0 3 3c 4 2 0 0 0d 0 0 0 3 3e 0 2 4 0 0

r1 r2 r3 r4 r5a 0.333 0.333 0.333 0 0b 0 0 0 0.5 0.5c 0.667 0.333 0 0 0d 0 0 0 0.5 0.5e 0 0.333 0.667 0 0

Ranking probability of having n at m

pmn = rmn / |LE|

rmn: ocurrence of object n at rank m

Average rank of nAv rkn = ∑m m∙pmn

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Min rk Av rk Max rk Vara 1 2 3 2b 4 4.5 5 1c 1 1.333 2 1d 4 4.5 5 1e 2 2.667 3 1

Restrepo, G.; Brüggemann, R.; Weckert, M.; Gerstmann, S.; Frank, H. MATCH Commun. Math. Comput. Chem. 2008, 59, 555-584.

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Best QSAR methods

Case study:

o Mutagenicity

o 95 aromatic & heteroaromatic amines

o 13 QSAR models

o Two statistics

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Model label Descriptors r2 s Method

Basak 1997 Topological and geometric 0.797 0.910 Linear

Basak 1998 Topological,geometric and quantum chemical 0.790 0.920 Linear

Maran 1999 #rings, γ-polarizability, HASA1 (SCF/AM1), HDSA (SCF/AM1), Etot(C-C), Etot(C-N)

0.834 0.811 Linear

Karelson 2000a

#rings, γ-polarizability, HASA1 (SCF/AM1), HDSA (SCF/AM1), Etot(C-C), Etot(C-N)

0.834 0.811 Linear

Karelson 2000b

Ic, 3κ, #H acceptor sites, max valence N, PNSA1, γ-polarizability

0.895 1.333 Non-linear

Basak 2001a Expanded set of topological, geometric and quantum chemical

0.794 0.912 Linear

Basak 2001b Expanded set of topological, geometric, quantum chemical and electrotopological

0.821 0.840 Linear

Cash 2001 Electrotopological 0.767 0.979 Linear

Toropov 2001 Graphs weighted with contributions of atomic orbitals 0.758 0.950 Linear

Vračko 2004a Topostructural, topochemical and geometric 0.793 0.840 Non-linear

Vračko 2004b Topostructural, topochemical, geometric and quantum chemical

0.793 0.840 Non-linear

Cash 2005a Electrotopological 0.760 0.950 Linear

Cash 2005b Electrotopological 0.750 0.890 Linear

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594 linear extensions

Maran 1999Karelson 2000a

Basak 2001b

Basak 1997 Vračko 2004a,b

Basak 2001a

Karelson 2000b

Basak 1998 Cash 2005b

Cash 2005aCash 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.

Min rk Av rk Max rk VarBasak 1997 6 8.2424 9 3Basak 1998 4 5.0909 6 2Maran 1999 10 10.909 11 1

Karelson 2000b 1 6 11 10Basak 2001a 5 6.6667 8 3Basak 2001b 9 9.8182 10 1

Cash 2001 1 2.5455 5 4Toropov 2001 1 1.697 4 3Vracko 2004a 6 7.7576 9 3Cash 2005a 2 3.3939 5 3Cash 2005b 1 3.8788 8 7

Maran 1999Karelson 2000a

Basak 2001b

Basak 1997

Vračko 2004a,b

Basak 2001aKarelson 2000b

Basak 1998

Cash 2005bCash 2005a

Cash 2001

Toropov 20011

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o Maran 1999 & Karelson 2000a and Basak 2001b are the less variable models.

o Karelson 2000b & Cash 2005b are the most variable models.

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Best Docking methodsCase 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 targetso virtual screening of compound databases to

identify leads for a protein targetWarren, 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.

Protein-ligand conformations

Percentage of compounds for which a docked pose was found within 2 Å of the crystal structure

136 protein/ligand conformations

chk1 pdfs mrs ppard fxa gyrb hcvpDock4 7 25 19 2 10 29 0DockIt 47 25 3 7 10 0 8FlexX 73 75 39 37 40 43 0

Flo 60 88 45 80 50 0 38Fred 73 50 58 0 10 0 0Glide 67 50 74 33 40 29 8Gold 53 88 94 78 40 43 31LigFit 40 63 0 15 0 0 0MOE 0 0 0 0 0 0 0MVP 87 38 42 41 0 43 0

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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

DockIt LigFit

MVP FlexX Glide Flo+

Protein-ligand conformations

12,960 linear extensions

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Min rk Av rk Max rk VarDock4 2 3.5833 7 5DockIt 2 3.5833 7 5FlexX 4 7.75 10 6Flo+ 4 7.75 10 6Fred 2 6 10 8Glide 4 7.75 10 6Gold 5 8 10 5LigFit 2 3.5833 7 5MOE 1 1 1 0MVP 2 6 10 8

Gold

FlexX, Flo+, Glide

Fred, MVP

Dock4, DockIt, LigFit

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o All programs have variable positions in the ranking, except MOE.

o The most suitable docking program to estimate protein-ligand conformations is Gold.

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Enrichment factor for actives (≤1 μM) found at 10% of the docking-score-ordered list

chk1 fxa gyrb hcvp mrs Ecoli-pdf Strep-pdf ppardDock4 1.4 4.1 1.7 1.8 4.2 0.9 0.8 1.7DockIt 4.2 2 2 1 1 0.2 0 3.2FlexX 7 2.2 5.8 0.9 3.9 0.8 0.8 5.2Flo+ 5.6 2.7 2.3 3.4 1.7 1.5 0.8 3.6Fred 2.9 4.1 1.9 2 0.6 3.2 1.2 1.1Glide 6.3 3.4 1 1 5.3 0.6 0.4 4.8Gold 0.1 4.1 4 0 0.8 1 0.1 5.5

LigandFit 3.3 1.9 2.8 1.8 2.9 2.9 1.7 1.2MOEDock 3.9 0.6 0 0 1 2.1 0.6 0

MVP 7.2 5.8 5.3 3.6 6.4 6.7 6.9 3.9

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Docking as a virtual screening tool

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Gold

FredDock4 MOE

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

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Min rk Av rk Max Rk VarDock4 1 4.8125 9 8DockIt 1 3.2083 8 7FlexX 1 5.5 10 9Flo+ 2 6.4167 9 7Fred 1 4.8125 9 8Glide 1 5.5 10 9Gold 1 5.5 10 9LigFit 1 4.8125 9 8MOE 1 4.8125 9 8MVP 7 9.625 10 3

MVP

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Dock4, Fred, LigFit, MOE

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DockIt

o All programs have quite variable positions in the ranking

o The most suitable docking program to identify active chemotypes is MVP

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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.

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Outlook

o Why not using more statistics for QSAR models?o Instead of ordering Alice and Bob’s 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.

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Acknowledgements

Rainer Brüggemann

Subhash C. Basak

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Thank you!