Computational chemical biology to address non-traditional drug targets. John Karanicolas

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1 Computational chemical biology to address non-traditional drug targets John Karanicolas

2 Our computational toolbox Structure-based approaches Ligand-based approaches Detailed MD simulations 2D fingerprints Docking (to predict pose) 3D matching (and pharmacophores) Virtual screening Fragment replacement

3 Ligand-based approaches for pose prediction and virtual screening

4 Ligand similarity metrics Neighborhood Region The activity of a compound is most likely to be shared by Active Compound similar compounds but only IF we have an appropriate way to other compounds describe similarity. Glen and Bender, Org Biomol Chem, 2004

5 2D Fingerprints Define a string of bits based on the presence/absence of particular substructure features (from a dictionary ). Bender, Mussa, and Glen, J Chem Inf Comput Sci, 2004

6 Comparing 2D fingerprints The Jaccard index is a statistic used for comparing the similarity and diversity of sample sets. Image detection example from Wikipedia

7 Comparing 2D fingerprints A = B = Given two fingerprints, their similarity is given by the Jaccard index (aka Tanimoto score). T = 0.6 Bender, Mussa, and Glen, J Chem Inf Comput Sci, 2004

8 What about 3D ligand comparisons for pose prediction and virtual screening?

9 Targets of marketed drugs Enzymes (including kinases) (47%) GPCR s (30%) Ion channels (7%) Nuclear hormone receptors, transporters, other receptors (12%) All are proteins evolved to bind small-molecules Hopkins and Groom, Nat. Rev. Drug Discov. 2002

10 Non-traditional drug targets Protein-protein interactions Protein-RNA interactions Protein stabilizers

11 A detailed case study using a (traditional) kinase example

12 Overview of an active kinase All active kinases are alike but an inactive kinase is inactive after its own fashion. Nobel et al, Science 2004 Jak1 + ADP

13 Type I kinase inhibitors Jak1+ADP Jak1+Tofatinib

14 Schematic of an active site Ghose et al., J Med Chem, 2008

15 Overview of kinase inhibitors Type I inhibitors bind the active conformation: selectivity is a challenge! Type II inhibitors bind inactive conformations; however, this hasn t led to selectivity Type III and beyond: boutique inhibitors

16 Hinge-binding motifs Jak1 + ADP Jak1 + Tofacitinib

17 Hinge-binding motifs Three of the most common hinge-binding motifs in the PDB Ghose et al., J Med Chem, 2008

18 Hinge-binding motifs Ghose et al., J Med Chem, 2008

19 Introduction to today s challenge A lab has screened for compounds that provide a certain phenotype After optimizing activity they ultimately arrive at a compound with an aminothiazole core, and they find it to be a CDK9 inhibitor

20 Introduction to today s challenge How does this compound engage CDK9? Why is it selective for CDK9? Most CDK9 inhibitors also inhibit CDK2 MC IC50 (nm) CDK1-Cyclin B 138 CDK2-Cyclin A 233 CDK2-Cyclin E 367 CDK3-Cyclin E 399 CDK4-Cyclin D 112 CDK5-P CDK5-P CDK6-Cyclin D3 712 CDK7-CycH/MAT1 555 CDK9-Cyclin T1 5.1

21 Traditional docking Sialic acid binding to Hemagglutinin (generated using Autodock software)

22 Modeling the MC180295/CDK9 complex We know what active kinases look like We know how Type I inhibitors bind Why would we possibly want to use docking?!

23 Modeling the MC180295/CDK9 complex

24 Comparative modeling for kinase inhibitors Start from an ATP-bound structure of CDK9 Align all other inhibitor-bound CDK-kinases to this (there are 389 in the PDB) Use each of these inhibitors to position MC Refine + re-score these 389 models

25 Comparing molecules in 3D Matching of features in 3D is more predictive of shared activity than 2D shared features Template compound is represented by a series of Gaussians ( cloud ) Clouds from query (library) compounds are individually aligned with template cloud to check their overlap (via Jaccard index)

26 Shape comparison matching H2 (white) (orange) ROCS: Rapid Overlay of Chemical Structures (very fast method for virtual screening!) Rush et al., J Med Chem, 2005

27 Comparative modeling for kinase inhibitors Start from an ATP-bound structure of CDK9 Align all other inhibitor-bound CDK-kinases to this (there are 389 in the PDB) Use each of these inhibitors to position MC Refine + re-score these 389 models

28 Extending this case study Can we successfully model other inhibitors / kinases, and ultimately use this to predict selectivity? Can we design new inhibitors that are more potent / selective?

29 Modeling beyond CDK9 Benchmark experiment: choose crystal structures of various kinases, each bound to a different inhibitor Modeling pipeline: 1) start from ATP-bound kinase of interest, 2) align inhibitor-bound structure of a different kinase, 3) transfer template inhibitor into kinase of interest, 4) align designed inhibitor onto template inhibitor, 5) minimize and re-rank Remember: we don t know active conformation of inhibitor Whole PDB will serve as templates - all active kinases look the same, so do all Type I complexes!

30 Csk21 example Inhibitor of Csk21 aka casein kinase II alpha aka CK2α (over-expressed in CRC and modulates EMT)

31 Csk21 example Csk21 inhibitor aligned to diverse PDB templates

32 Csk21 example Top 10 models from diverse templates after minimization

33 Csk21 example Models closely match crystal structure of this complex

34 Csk21 templates CDK2 inhibitor CK2 inhibitor EphB4 inhibitor B-Raf inhibitor

35 Summary of benchmark expt Using diverse templates in the PDB helps model many (Type I) kinase inhibitors Minimization is helpful for achieving refinement better than the templates Pipeline is VERY fast, ~100 CPU minutes per complex (with more degrees of freedom than typical docking) This opens the door to more virtual screening, and especially to predicting selectivity

36 Designing better inhibitors Build kinase-focused libraries of inhibitors Given a synthetic route to arrive a known inhibitor, enumerate analogs that can be built using commercially available building blocks Computationally screen these for potency and selectivity

37 Generalized synthesis R 1 Step I R 1 O Br + N N S - S H 2 N N O S 77 compounds S R 1 Step II R 1 H 2 N N O S + NH 2 O R 2 H 2 N 2872 compounds S N 232k new compounds, 126k are drug-like S HN R 2

38 Designing new inhibitors This reaction scheme gives 126,000 drug-like compounds that could be made using the Sigma catalog (including MC180295!) The vast majority of these are novel chemical matter Modeling this library against CDK9 gives many that score better than MC180295, and are also predicted to be more selective Other members of this library are predicted to be potent and selective for other kinases

39 Pharmacophore mapping Cannabinoid receptor antagonist (example from wikipedia showing rimonabant) Common software is CATALYST, MOE, others 39

40 Pharmacophore mapping

41 Pharmacophore mapping Cannabinoid receptor antagonists demonstrating shared pharmacophore

42 From pockets to exemplars Protein surface pocket Exemplar (aka pharmacophore)

43 Exemplar screening (using ROCS, in this case)

44 From traditional to non-traditional drug targets Well-validated (traditional) drug targets have the advantage of ample extant knowledge, enabling new studies Biology doesn t work this way though! The ability to access non-traditional drug targets may open new avenues in drug discovery In both regimes, it s important to think carefully about how to best apply the tools at hand

45 Our computational toolbox Structure-based approaches Ligand-based approaches Detailed MD simulations 2D fingerprints Docking (to predict pose) 3D matching (and pharmacophores) Virtual screening Fragment replacement

46 Emerging themes in the field There is still lots of room for creative approaches, figuring out new ways to use these tools Ligand-based methods (and hybrid methods) offer intriguing advantages over purely structure-based methods At present, virtual screening often only identifies micromolar hits: carrying out med chem optimization in silico may enable design of more potent compounds at the outset

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