Evotec AG, 5 th Joint Sheffield Conference on Chemoinformatics , July 2010

1. Investigation of CDK2 Inhibitor Potency using Electrostatic Potential Complementarity and the Fragment Molecular Orbital MethodCreating high-value drug discovery innovation alliances Evotec AG, 5th Joint Sheffield Conference on Chemoinformatics, July 2010

2. Overview • Molecular Shape and Electrostatic Considerations in Ligand Binding • Case Study: Cyclin-Dependent Kinase 2 (CDK2) • Understanding Complex Interactions During H2L/F2L/LO • The Fragment Molecular Orbital (FMO) Method • Application of FMO Calculations

3. Classical Lock and Key Problem “Everything should be made as simple as possible, but not simpler.” - Einstein • What is required to effectively describe protein::ligand interactions? • Ligand and receptor features to consider: • Shape • Charge and electrostatic potential • Dynamics Ligand “Keys” Receptor “Lock”

4. OAB IB IA Scoring Ligand Shape and Electrostatic Potential TanimotoCoefficient How similar are these? B A • Tanimoto coefficient is widely used to compare chemical similarities • Gaussian Tanimoto compares ligand shapes in 3D • Electrostatic Tanimoto (TES) is calculated in the similar manner as for Gaussian Tanimoto but an electrostatic field overlap is used instead of volume overlap1 • Implemented in MOE2 and is high throughput (10,000s cmpds) Tanimoto = 1 = A and B are identical Jennings and Tennant, J. Chem. Inf. Model. 47, 1829-1838, 2007 MOE (The Molecular Operating Environment) http://www.chemcomp.com

5. Ligand-Based Shape and Electrostatic Potential Calculations • Gaussian Tanimoto is a fast shape comparison application, based on the idea that molecules have similar shape if their volumes overlap well and any volume mismatch is a measure of dissimilarity • Used as a virtual screening tool which can rapidly identify potentially active compounds with a similar shape to a known hit or lead compound • TES score is sensitive to subtle changes in ligand electrostatics • Semi-empirical atomic charges using AM1-BCC is recommended 1,2 AM1-BCC is parameterized for good correlation with HF 6-31G* charges 3 Gauss = 0.97 TES = 0.54 Gauss = 0.97 TES = 0.40 Gauss = 0.99 TES = 0.64 Gauss = 1.00 TES = 1.00 Tsai et al., Bioorg. Med. Chem. Lett., 18, 3509-3512, 2008 Jennings and Tennant, J. Chem. Inf. Model. 47, 1829-1838, 2007 Balyl, et al., J. Comput. Chem., 132-146, 132, 2000

6. Case Study: CDK2 Astex AT7519 (CDK2 inhibitor) as an example • Can ligands be effectively represented and compared using measures of shape and electrostatics? • Case example taken from the literature.1,2 CDK2 fragment-based screened identified a number of hits. 28 ligands taken from this work were examined. • Pharmacological inhibitors of cyclin-dependent kinases (CDKs) are currently being evaluated for therapeutic use against cancer and neurodegenerative disorders amongst many other diseases. 1) P. G. Wyatt et al., J. Med. Chem. 51, 4986-4999, 2008 2) M. Congreve et al.,J. Med. Chem.51, 3661-3680, 2008

7. Gaussian Tanimoto Based on CDK2-Bound Alignment AT7519 3nM Gaussian Tanimoto Coefficient Hit Clinical Candidate Fragment Hit

8. Absolute Difference in pIC50 AT7519 0.0 1.5 4.0 3nM Absolute Difference In pIC50 Hit Clinical Candidate Fragment Hit

9. Electrostatic Tanimoto - TES Based on CDK2-Bound Alignment AT7519 3nM Electrostatic Tanimoto Coefficient Hit Clinical Candidate Fragment Hit

10. Case Study: CDK2 Optimisation of Shape and Electrostatics 2VU3 AT7519, 47nM Which interactions are the most important? What happens when you have a complicated interaction that requires better understanding? Chau, P-L., and Dean, P.M., J. Comput.-Aided Mol. Design., 8, 513-525, 1994

11. Understanding Complex Interactions during H2L/F2L/LO Which interactions are the most important? What happens when you have a complicated interaction that requires better understanding? Multiple equivalent binding modes Interactions not represented in docking/MM forcefields “Defragmentation” of large ligands to determine group efficiency More complex methods required – e.g., free energy and/or quantum mechanical calculations

12. Fragment Molecular Orbital (FMO) Method Method and throughput • Full quantum computation of protein::ligand complexes has been practically impossible until recently due to extremely large resources required for computing • The fragment molecular orbital method1 (FMO) was proposed by K. Kitaura and co-workers • Highly suitable for calculation of large (biological) systems in parallel computing environment2,3 • Implemented in GAMESS QM suite • PIEDA4,5(Pair interaction energy decomposition analysis) provides detailed ligand/protein interaction information Fragmentation of peptide PIE (Pair Interaction Energy) Calculations for systems with 200-300 atoms are routinely ran at Evotec (~10/day) using MP2 / 6-31G* , 6-31G(3df,3pd) for Cl and S • Fedorov, D. G., and Kitaura, K., J. Comput. Chem., 28, 222-237, 2007 • Nakano et al., Chem. Phys. Lett., 351, 475-480, 2002 1) Kitara et al., Chem. Phys. Lett., 313, 701-706, 1999 2) Komeiji et al., Comput. Biol. Chem., 28, 155-161, 2004 3) Fedorov et al., J. Comput. Chem., 25, 872-880, 2004 PAGE 12

13. 3 Energy (kcal/mol) 0 -3 4.0 3.0 5.0 Distance (Å) The Cl-p Interaction in a Protein::Ligand Complex HF/6-311G++(3df,2pd) • Cl- interaction is an attractive interaction, where the major source of attraction is the dispersion force • Calculated interaction energy is 2-3 kcal/mol depending on the chloro species • Optimal distance is ca. 3.6 Å • HF interaction is repulsive • Electron correlation method, such as MP2, needed to probe the interaction accurately • For example – serine protease inhibitor series1 MP-2/6-311G++(3df,2pd) MP-2/cc-PVTZ Shi, Y., et al., J. Med. Chem. 51, 7541-7551, 2008 Imai et al., Protein Science, 16, 1229, 2008

14. Application of FMO Calculations Glu81 Phe80 Phe82 Exchange Electrostatic CT & Mixed Dispersion His84 Leu134 PIE and PIEDA (Facio)1,2 and PIO (Pair Interacting Orbitals)3,4 PIEDA diagram PDB: 1WCC IC50 = 350mM -48.40kcal/mol PIO analysis Fujimoto, H.; Koga, N.; Fukui, K. J. Am. Chem. Soc. 1981, 103, 7452. Fujimoto, H.; Yamasaki, T.; Mizutani, H.; Koga, N. J. Am. Chem. Soc. 1985, 107, 6157. Suenaga, M., J. Comput. Chem. Jpn., 4 (1), 25-32, 2005 Suenaga, M., J. Comput. Chem. Jpn., 7 (1), 33-53, 2008 PAGE 14

15. Application of FMO to FBDD Astex AT7519 (CDK2 inhibitor) as an example PDB: 2VTA IC50 = 185mM LE = 0.57 PDB: 2VTN IC50 = 0.85mM LE = 0.44 PDB: 2VTO IC50 = 0.14mM LE = 0.39 PDB: 2VTP IC50 = 0.003mM LE = 0.45 Development discontinued AT7519 IC50 = 0.047mM LE = 0.40 PDB: 1WCC IC50 = 350mM LE < 0.51 LE = -RTln(IC50)/heavy atom cout P. G. Wyatt et al., J. Med. Chem. 2008, 51, 4986-4999 M. Congreve et al.,J. Med. Chem.2008, 51, 3661-3680

16. Application of FMO to FBDD Phe80 Phe82 Exchange Electrostatic CT & Mixed Dispersion His84 = Direction of CT Leu134 PIEDA and PIO (Pair Interacting Orbitals) PIEDA diagram PIO analysis PDB: 1WCC IC50 = 350mM -48.40 kcal/mol • PIEDA identifies the nature of ligand/protein interactions • H-bond, VDW, p-p etc • PIO analysis used to visualize and provide 3D information on the interactions • Interacting orbitals, direction of charge transfer (vacant-occupied MO interaction) PAGE 16

17. Application of FMO to FBDD 1 2 3 4 5 6 7 8 9 10 11 12 13 1WCC core modifications: FMO virtual SAR Removal of the chlorine detrimental to the fragment binding • Medium throughput (up to few 100s input) FMO analysis can be rapidly carried out to answer SAR questions • The technique is highly effective for prioritizing the initial fragment expansion directions or optimization for larger ligands DE IC50 = 350mM IC50 = 7mM DE = Sum PIE – Sum PIE (1WCC fragment) PAGE 17

18. Application of FMO to FBDD repulsive attractive Glu81 Lys33-Asp145 Salt bridge Phe80 Phe82 His84 Leu134 Tracking the PDB: 2VTA development path by FMO analysis PDB: 2VTA IC50 = 185mM -41.71kcal/mol PDB: 2VTN IC50 = 0.85mM -61.24 kcal/mol PDB: 2VTO IC50 = 0.14mM -64.81kcal/mol Leu83 Val18 Val18 PAGE 18

19. FMO Heatmap Analysis Sum of the PIE R1 R2 Energy Kcal/mol

20. Binding Enthalpy Comparison to MM Methods Known Binding Modes from X-ray Structures London dG FMO GB IV Affinity dG Alpha HB ASE

21. Summary • Gaussian Tanimoto can be used to assess similarly shaped compounds to actives • TES can be used to assess which docking pose is the best during VS • TES used to identify suboptimal interactions for further development • FMO can be used to identify which binding pose from a VS has the optimal interactions with a receptor • FMO can be used to indentify subtle changes required to improve binding enthalpy • Molecular interactions reflected in the binding enthalpy are critical variables in lead optimisation

22. Current and Future Work • Currently assessing protein::ligandcomplementaritymethods

23. Current and Future Work • PBSA treatment of free energy of solvation can be used to rationalize overestimated enthalpic terms in FMO • Free energy of binding QSAR models are highly predictive • Need for improved treatment of • Solvation • Entropy • Salts and Metals

24. Acknowledgements Evotec CADD Group Richard Law Osamu Ichihara Alex Heifetz Chemical Computing Group MOE svl Scripts Andrew Henry Simon Grimshaw Guido Kirsten Kristina Grabowski FMO Developers Dmitri Fedorov

25. Your contact: Dr. Mike Mazanetz Senior Scientist, Computational Chemistry +44 (0) 1235 44 1342michael.mazanetz@evotec.com

26. Appendices

27. Estimation of Binding Free Energies Entropy – Enthalpy Compensation Aim of free energy calculation in a VS campaign is to rank-order molecules such that if a selection of high-ranking compounds is obtained and analysed, it is likely that some will show activity. However, compound activity is likely to span about 5 log orders in magnitude, which equates to free energy range of around 5.5 kcal/mol at 37°C. R: universal gas constant ≈ 1.986 cal/Kmol T: temperature 310 K • Williams, D., et al., Angew. Chem. Int. Ed., 43, 6596-6916, 2004

28. Estimation of Binding Free Energies Basic equations and two thermodynamic terms • Relationship between to Ki (IC50) and the free enegy of binding DG = -RT lnKD • Free energy of ligand binding consists of two thermodynamic terms DG = DH – TDS • Binding enthalpy • Notoriously difficult to optimize due to strict three dimensional requirements • Enthalpic improvement is often not reflected in better affinity, because of the associated entropy-loss (desolvation) • Binding entropy • Dependent primarily on the hydrophobic effect and conformational entropy • Easier to optimize and less affected by compensating enthalpy changes

29. Key SBDD Concepts • Entropy-enthalpy compensation phenomenon • Desolvation penalty (4-8 kcal/mol per polar group) • Origin of hydrophobic interaction (entropy-driven effect, re-organization of surface water network) • Two terms contribute to the entropy of binding Desolvationentropy (always favourable, about 25 cal/mol Å2 for a carbon atom) Conformational entropy • Overcoming enthalpy/entropy compensation • Well placed H-bond can make a favourable enthalpic contribution of the order of -4 to -5 kcal/mol (1000 – 5000 fold increase in affinity) • Hydrogen bonds should be aimed at already structured regions of the protein • Try achieving multiple H-bonds for flexible residues – positive cooperativity • Be aware of the forced solvent exposure of hydrophobic groups

30. Free Energy of Binding Thermodynamic Cycle ΔGBind, Solv + ΔGSolv, Ligand ΔGSolv, Complex ΔGSolv, Receptor ΔGBind, Vac + ΔGBind, Solv = ΔGBind, Vac + ΔGSolv,Complex ( ΔGSolv,Ligand + ΔGSolv,Receptor ) ΔGSolv = ΔGElec,ε=80ΔGElec,ε=1 + ΔGHydro ΔGVac = ΔEMM T·ΔS

31. Estimation of Binding Free Energies All 28 CDK2 ligands Actual pIC50 r2 = 0.81 q2 = 0.76 PBSA using a single energy-minmized structure Number of rotatable bonds Predicted pIC50 FMO sum PIE ΔGBind, Solv = ΔGBind, Vac + ΔGSolv,Complex ( ΔGSolv,Ligand + ΔGSolv,Receptor ) ΔGSolv = ΔGElec,ε=80ΔGElec,ε=1 + ΔGHydro ΔGVac = ΔEMM T·ΔS • Rastelli G., et al., J. Comput. Chem., 31(4), 797-810, 2009