Computational chemical biology to address non-traditional drug targets John Karanicolas
Our computational “toolbox” Structure-based Ligand-based approaches approaches Detailed MD simulations 2D fingerprints 3D matching Docking (to predict pose) (and pharmacophores) Virtual screening Fragment replacement
Ligand-based approaches for pose prediction and virtual screening
Ligand similarity metrics Neighborhood The activity of a Region compound is most likely to be shared by Active “similar” compounds… Compound but only IF we have an appropriate way to other describe similarity. compounds Glen and Bender, Org Biomol Chem , 2004
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
Comparing 2D fingerprints The Jaccard index is a statistic used for comparing the similarity and diversity of sample sets. Image detection example from Wikipedia
Comparing 2D fingerprints Given two fingerprints, their similarity is given by the Jaccard index (aka Tanimoto score). A = 01010111110001 B = 01100101111001 T = 0.6 Bender, Mussa, and Glen, J Chem Inf Comput Sci , 2004
What about 3D ligand comparisons for pose prediction and virtual screening?
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
Non-traditional drug targets • Protein-protein interactions • Protein-RNA interactions • Protein stabilizers
A detailed case study using a (traditional) kinase example…
Overview of an active kinase Jak1 + ADP “All active kinases are alike but an inactive kinase is inactive after its own fashion.” — Nobel et al, Science 2004
Type I kinase inhibitors Jak1+ADP Jak1+Tofatinib
Schematic of an active site Ghose et al., J Med Chem , 2008
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
Hinge-binding motifs Jak1 + ADP Jak1 + Tofacitinib
Hinge-binding motifs Three of the most common hinge-binding motifs in the PDB Ghose et al., J Med Chem , 2008
Hinge-binding motifs Ghose et al., J Med Chem , 2008
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
Introduction to today’s challenge MC180295 IC50 (nM) CDK1-Cyclin B 138 CDK2-Cyclin A 233 CDK2-Cyclin E 367 CDK3-Cyclin E 399 • How does this compound CDK4-Cyclin D 112 CDK5-P35 159 engage CDK9? CDK5-P25 186 CDK6-Cyclin D3 712 • Why is it selective for CDK9? CDK7-CycH/MAT1 555 Most CDK9 inhibitors also CDK9-Cyclin T1 5.1 inhibit CDK2…
Traditional docking Sialic acid binding to Hemagglutinin (generated using Autodock software)
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?!
Modeling the MC180295/CDK9 complex
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 MC180295 • Refine + re-score these 389 models
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)
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
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 MC180295 • Refine + re-score these 389 models
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?
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!
Csk21 example Inhibitor of Csk21 aka casein kinase II alpha aka CK2 α (over-expressed in CRC and modulates EMT)
Csk21 example Csk21 inhibitor aligned to diverse PDB templates
Csk21 example Top 10 models from diverse templates after minimization
Csk21 example Models closely match crystal structure of this complex
Csk21 templates CDK2 CK2 inhibitor inhibitor EphB4 B-Raf inhibitor inhibitor
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
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
Generalized synthesis R 1 R 1 S - O + N Step I N S Br H 2 N S O N 77 compounds S R 1 R 1 232k new compounds, O NH 2 126k are “drug-like” O R 2 Step II + H 2 N H 2 N 2872 compounds S S N N HN R 2 S
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…
Pharmacophore mapping • Cannabinoid receptor antagonist (example from wikipedia showing rimonabant) • Common software is CATALYST, MOE, others 39
Pharmacophore mapping
Pharmacophore mapping Cannabinoid receptor antagonists demonstrating shared pharmacophore
From pockets to exemplars Protein surface pocket “Exemplar” (aka pharmacophore)
Exemplar screening (using ROCS, in this case)
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
Our computational “toolbox” Structure-based Ligand-based approaches approaches Detailed MD simulations 2D fingerprints 3D matching Docking (to predict pose) (and pharmacophores) Virtual screening Fragment replacement
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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