Holger Claußen Recent developments in the FlexX family

Transcription

1 olger Claußen Recent developments in the Flex family

2 The Problem # Satisfy known key interactions Utilize successful templates vary linker groups exploit isosteric space Linkers and R-groups = combinatorial libraries Key interactions = pharmacophores * * *? * R3 * # 2-2 # 2 # 2 R Leach et. al, JMGM 18, , 2000 Krier et al, JMC, 48, , 2005 * 2006 BioSolve IT Gmb olger ACS Fall 2006, San Francisco 1

3 Requirements Docking of virtual combinatorial libraries under pharmacophore constraints Very fast docking algorithm to cope with millions of virtual molecules Flex c Consideration of pharmacophore constraints during docking Flex-Pharm ew module: Flex c -Pharm 2006 BioSolve IT Gmb olger ACS Fall 2006, San Francisco 2

4 Combinatorial Docking with Flex c Key feature: Flex C reuses already placed fragments C a R3 a R3 a higher speed than serial docking results depend on build-up order ab 2006 BioSolve IT Gmb olger ACS Fall 2006, San Francisco 3

5 In-situ docking filter I: Flex-Pharm Flex-Pharm constraints interaction constraints: based on Flex interaction geometries Check at every build-up step existence of counter groups distance compatibility if fails, reject solution speed-up by look-ahead strategy spatial constraints: spheres in active site SMARTS pattern to define (un-)allowed functional groups indle et al, JCAMD, 16: , BioSolve IT Gmb olger ACS Fall 2006, San Francisco 4

6 In-situ docking filter II: Chemistry Lipinski Rule-of-Five like filter (logp not yet implemented for combi libs) molecular weight < 500 number of or 5 (mimicking donors) number of or 10 (mimicking acceptors) 2 constraints must be fulfilled Avoid aza-compounds (smarts( - == 0)) and ( ( (mass < 500) and (smarts( [#7;!0,#8;!0] ) <= 5) ) or ( (mass < 500) and (smarts( [#7,#8] ) <= 10) ) or ( (smarts( [#7;!0,#8;!0] ) <= 5) and (smarts( [#7,#8] ) <= 10) ) ) 2006 BioSolve IT Gmb olger ACS Fall 2006, San Francisco 5

7 Example I: Methotrexate/DFR one of these 94 one of these Flex-Pharm A _D2 ASP A 27 h_acc 1 B _D1 ASP A 27 h_acc 1 C _ D _ ILE A 94 h_acc ILE A 5 h_acc one of these E _1 ARG A 57 h_don 12 F _2 ARG A 57 h_don (A or B) and (C or D) and (E or F) 2006 BioSolve IT Gmb olger ACS Fall 2006, San Francisco 6

8 Generation of Methotrexate Library : 775 primary and secondary amines scaffold : 1750 primary and secondary amines including amino acids products in combinatorial library Known Actives includes: includes: 2 2 [C,] [C,] [,C] variations x 2 - variations = 16 known actives R. L. Kisliuk, Cur. Pharm. Des., 2003, 9, BioSolve IT Gmb olger ACS Fall 2006, San Francisco 7

9 Virtual Screening Results Enrichment um its optimum top 16 are active BioSolve IT Gmb 4 actives in top actives found overall ~ fulfill constraints ( 2.5 %) first hit rank 14 9 actives in top 1000 olger ACS Fall 2006, San Francisco Enrichment of ~750-fold in top 0.1% random Mio um Docked 8

10 Combinatorial vs. Serial Docking Runtimes Total CPU Real CPU (60 nodes) Average per cmpd. Sequential 3600 d 60 d sec Combinatorial 120 d 2 d 7.2 sec 30-fold speed-up 2006 BioSolve IT Gmb olger ACS Fall 2006, San Francisco 9

11 Combinatorial vs. Serial Docking Score Correlation Methotrexate library in 4dfr combinatorial score R 2 = 0.87 score of sequential docking 2006 BioSolve IT Gmb olger ACS Fall 2006, San Francisco 10

12 Example II: Gleevec/c-Abl kinase Glu 286 Flex-Pharm constraints Met 318 Asp 381 Ile A _ ASP A 381 h_don B _E2 GLU A 286 h_acc 0 C _E2 GLU A 286 h_acc 1 + D _ ILE A 360 h_acc E _ MET A 318 A and (B or C) and D and E 2006 BioSolve IT Gmb olger ACS Fall 2006, San Francisco 11

13 The Leach-Approach Synergy between Combinatorial Chemistry and De ovo Design Leach et al., J. Mol. Graphics Mod. 2000, 18, Pharmacophores # * * # Scaffold # * * # Generic Linkers 2006 BioSolve IT Gmb olger ACS Fall 2006, San Francisco 12

14 The Leach-Approach First Phase Path length = 5 Path length = 4 Path length = 3 Path length = 2 Scaffold Decorate Scaffold by Generic Linkers Save in 3D database 3D search/docking to link pharmacophores Generic its 2006 BioSolve IT Gmb olger ACS Fall 2006, San Francisco 13

15 The Leach-Approach First Phase R4 R BioSolve IT Gmb olger ACS Fall 2006, San Francisco 14

16 The Leach-Approach 2nd Phase Decompose Cl + 2 Generic hit Generalise queries Search Reagent Database A A A A A A Cl 2 A A A 2006 BioSolve IT Gmb olger ACS Fall 2006, San Francisco 15

17 Decomposition of Gleevec Pharmacophore 1 Pharmacophore 2 Scaffold + Generic Linkers Generic Linkers 2006 BioSolve IT Gmb olger ACS Fall 2006, San Francisco 16

18 Generic Gleevec Library Scaffold Pharmacophore 2 R R + R R + R R R R Pharmacophore 1 + R Generic Linkers Size of combinatorial library: 4 x 20 x 20 x 7 x 20 x 20 x 5 = 22,400,000 cmpds 2006 BioSolve IT Gmb olger ACS Fall 2006, San Francisco 17

19 Filtering in Gleevec Library Lipinski Rule-of-Five like filter (logp not yet implemented for combi libs) molecular weight < 500 number of or 5 (mimicking donors) number of or 10 (mimicking acceptors) 2 constraints must be fulfilled Avoid aza-compounds (smarts( - == 0)) and ( ( (mass < 500) and (smarts( [#7;!0,#8;!0] ) <= 5) ) or ( (mass < 500) and (smarts( [#7,#8] ) <= 10) ) or ( (smarts( [#7;!0,#8;!0] ) <= 5) and (smarts( [#7,#8] ) <= 10) ) ) 2006 BioSolve IT Gmb olger ACS Fall 2006, San Francisco 18

20 Flex c -Pharm Docking Results # cmpds in virtual library: 22,400,000 # cmpds. docked: ~70,000 (~0.3% of all) Rank of closest Gleevec analog: 141 (within Top % of all) Total CPU time: 60h (3h on 20 nodes) Average run time per cmpd.: 0.01s Docking pose of closest Gleevec analog corresponds well with ray structure 2006 BioSolve IT Gmb olger ACS Fall 2006, San Francisco 19

21 Similarity of Top Ranking Solutions earest Gleevec analogue, rank 141 Ftrees Similarity to Gleevec single point and average Flex Rank 2006 BioSolve IT Gmb olger ACS Fall 2006, San Francisco 20

22 Monomer Frequency Analysis Based on top-1000 docking solutions, monomer frequencies were determined and ranked + P 1 L 1 L 2 S L 3 L 4 P 2 Rank of Gleevec fragment Topranked fragment 1 R R BioSolve IT Gmb olger ACS Fall 2006, San Francisco 21

23 Summary and Conclusion ew Flex module Flex c -Pharm provides extremely efficient docking of large virtual libraries increased enrichment rates and further speed-up by pharmacophore constraints Pharmacophore Linker Scaffold concept in combination with Monomer Frequency Analysis exploits isosteric space identifies most suitable linkers helps to select most appropriate chemistry ( scaffold-hopping ) serves as idea generator Combinatorial de novo design 2006 BioSolve IT Gmb olger ACS Fall 2006, San Francisco 22

24 Acknowledgements Markus Lilienthal Marcus Gastreich Sally indle Christian Lemmen ans Briem ans-peter Wrona-Metzinger Judith Günther ikolaus einrich For more details see: M. Gastreich, M. Lilienthal,. Briem,. Claussen Ultrafast De ovo Docking Combining Pharmacophores and Combinatorics, Journal of Computer-Aided Molecular Design, accepted BioSolve IT Gmb olger ACS Fall 2006, San Francisco 23