Large Scale FEP on Congeneric Ligand Series Have Practical Free Energy Calculations arrived at Last?

Transcription

1 Large Scale FEP on Congeneric Ligand Series Have Practical Free Energy Calculations arrived at Last? Thomas Steinbrecher, Teng Lin, Lingle Wang, Goran Krilov, Robert Abel, Woody Sherman, Richard Friesner

2 Large Scale FEP on Congeneric Ligand Series Have Practical Free Energy Calculations arrived at Last? Thomas Steinbrecher, Teng Lin, Lingle Wang, Goran Krilov, Robert Abel, Woody Sherman, Richard Friesner

3 Large Scale FEP on Congeneric Ligand Series Practical Free Energy Calculations at Last! Thomas Steinbrecher, Teng Lin, Lingle Wang, Goran Krilov, Robert Abel, Woody Sherman, Richard Friesner

4 Summary of FEP from Schrödinger We have a robust solution for predicting binding free energies Validation has been performed on over 1000 compounds Mix of retrospective and prospective applications Many target classes covered Kinase Proteases Bromodomains GPCRs PPIs Automated system provides solution for non-fep experts

5 Background: The Drug Design Problem

6 Approved Drugs The Role of Computation Molecular simulations can accelerate discovery Better potency with fewer synthesized compounds Can also account for other properties Selectivity ADME/Tox The Problem Source: FDA

7 Free Energy Simulations Advantages of Free Energy Simulations Based on solid statistical mechanics Takes into account key aspects of binding Dynamics Induced fit Water displacement and interactions Treatment of bound and unbound states Compute true free energies Entropy and enthalpy contributions Have been around for a long time Significant theoretical foundation Many advanced methods available Growing Interest in Protein-Ligand Binding

8 Computational Alchemy: The Abstract λ-coordinate Real starting state Alchemical intermediates Real final state V0 V ( ) V G 0 0 G 0 G 1 G 0 G 0 1 G 0 0 kt ln exp E kt

9 Free Energies from Thermodynamic Cycles Computing relative energies has advantages over absolute energies Smaller perturbations reduces errors Relative changes are of most interest Absolute energies can be trivially obtained once a single experimental binding energy is known To compute the difference in binding of ligand 1 and ligand 2, we can compute the difference between ligand 1 2 in solution and 1 2 in the binding site A 1 2 B G binding = G 1 G 2 = G A G B

10 New Technologies: FEP/REST, OPLS 2.1, Redundancy

11 Several New Technologies Needed for a Robust Solution Improved force field Enhanced sampling Hardware acceleration Automated setup Error estimates OPLS2 REST GPU FEP Mapper Cycle Closure

12 The OPLS2 Force Field Better charges: Semi-empirical charge assignment (CM1A-BCC) More torsional coverage: Parameterization covers > 90% of torsions found in drug-like compounds Automated parameter generation: FFBuilder allows for arbitrary extension of force field coverage Improved results: Excellent results for absolute solvation free energy and relative binding free energy Torsional energy RMSE for 10K molecules Absolute solvation free energies Force Field Slope R 2 OPLS OPLS AM1-BCC/GAFF ChelpG/CHARMM Shivakumar et al., JCTC, 2012, 8 (8),

13 Full Chemical Space Coverage with FFBuilder Coverage prob. per bond (per molecule) Ligand Alignments *MMFF coverage prob based on rel. tr. set size to OPLS2005 MMFF OPLS2005 OPLS2.1 *6% (0%) 25% (0%) 93% (67%) Ligand Alignments Ligand Alignments

14 Full Chemical Space Coverage with FFBuilder Coverage prob. per bond (per molecule) Ligand Alignments *MMFF coverage prob based on rel. tr. set size to OPLS2005 MMFF OPLS2005 OPLS2.1 *6% (0%) 25% (0%) 93% (67%) Ligand Alignments Fill in the Ligand Alignments coverage gap FFBuilder

15 Enhanced Sampling: REST and FEP Thermodynamic axis, alchemical transition λ=0 λ=λ 1 λ=λ m/2 λ=λ m-1 λ=1 T=T 0 T=T 1 T=T h T=T 1 T=T 0 Increasing the temperature of the hot region in intermediate λ Hot Region The ligand and the protein residues surrounding the binding pocket are in the hot region, the rest of the system stay cold. Wang L., Berne B. J., Friesner R.A., PNAS 2012,

16 Representative Configurations Sampled Using FEP/MD vs FEP/REST 1h1q FEP/MD 1h1q FEP/REST 1h1r FEP/MD 1h1r FEP/REST

17 CDK2: 1h1r Multiple Binding Modes Starting Conformation Alternative Conformation Electron Density Method ΔΔG 1H1Q 1H1R FEP FEP/REST 0.14 Expt. 0.51

18 Computation with Graphics Processing Units (GPU) Our standard FEP calculation (one pair of ligands) takes 200 CPU-days! GPUs can make this tractable: 2 perturbations/day on a 4-GPU machine 100 perturbations/day on a 200- GPU machine (what Schrödinger uses) GPU computing is essential! ~100x over CPU GPU ~1000x over CPU ~20,000x over CPU 8-GPU Box 200-GPU Cluster

19 FEP Mapper A human FEP expert will generally Connect molecules with similar R-groups Bias the network to favor high-confidence end states that will become hubs in the network This allows a human expert to run more reliable FEP calculations In FEP Mapper we have automated this highly domain specific expertise

20 Error Estimates from Cycle Closure Compute at least 2 paths for each molecule Closed thermodynamic cycles have ΔΔG=0, thus Any deviation from this provides an error estimate More cycles result in more accurate error estimates Co-crystalized Molecule ΔG 1 Closed Cycle ΔG 3 Assayed Molecule ΔG 2 Idea Molecule ΔG cycle = ΔG 1 +ΔG 2 +ΔG 3 = 0

21 Results and Applications Improved Scoring for Lead Optimization

22 Summary of Results: ~500 Perturbations Target # Ligands R 2 Slope G MUE (kcal/mol) A2A (GPCR) BACE BRD4 (1) BRD4 (2) CDK CHK1 (1) CHK1 (2) HSP JNK Lysozyme MCL MDM MUP-I P PTP1B Throm (1) Throm (2) Mean Unsigned Errors (MUE) 1.0 kcal.mol kcal/mol >1.4 kcal/mol Most errors below 1.0 kcal/mol Accurate for LO Ongoing work to understand outliers

23 Percentage FEP Prediction Accuracy >500 perturbations tested with single protocol RMSE = 1.2 kcal/mol R2 = 0.5 Slope = % 40% 30% 20% 10% 0% 46,2% 24,8% >70% of cases have error 1.2 kcal/mol Distribution of Errors 15,4% Prediction error (kcal/mol) 7,4% 6,2% < >2.4 Target Classes Bromodomains GPCRs Kinases Proteases PPI Others

24 Prediction (kcal/mol) Getting the Physics Right What does a slope of 1 mean? Can compare results between different targets Opens the door for in silico selectivity screening Entropy/enthalpy via in silico van t Hoff Solvation ITC for 220 small molecules Entropy and enthalpy from multiple temperature FEP R 2 = 0.82 ΔG/T DG T ( ) 1 T ( ) = DH 1/T Experiment (kcal/mol)

25 Example Applications

26 G pred kcal/mol Neither MM-GBSA nor WM/MM show strong signal Several species had highly flexible R-groups -7 R 2 = MUE = 0.7 kcal/mol Jnk1 Kinase G exp kcal/mol -8-7

27 MMGBSA Outliers R 2 value for MM-GB/SA scoring is 0.3 over the full set The R 2 value improves to 0.5 with these outliers removed

28 G pred kcal/mol P38 Kinase -10 R 2 =0.64 MUE = 0.9 kcal/mol G exp kcal/mol

29 G pred kcal/mol More P38 Ligands R 2 =0.64 MUE = 0.9 kcal/mol G exp kcal/mol

30 BACE J. N.Cumming et al, Bioorg. Med. Chem. Lett, 22(2012)

31 G pred kcal/mol BACE Correlation -6-7 R 2 =0.59 MUE = 0.9 kcal/mol G exp kcal/mol -8-7

32 Watch out for trapped Water Molecules Red: 4djx, ligand 17h Green: 4djw, ligand 4j

33 Prospective Applications

34 pic50 FEP/REST Prospective Applications for Projects C, D, and E at Schrödinger Most errors below 1.0 kcal/mol Consistent with retrospective results Currently in testing at ~5 pharma sites Results are consistent with our findings Prospective use is ramping up Several manuscripts in preparation Project C Project D pic50 Expt. Project E

35 FEP+ in DDAG 5 DDAG collaborations are heavily using FEP+ 95 prospective FEP+ predictions tested so far 65 predictions were accurate within a log-unit Only 6 were off by more than 2 log-units No dead compounds have been predicted to be tight binding

36 Collaboration B Pyrazole to Thiazole Core-Hop R 1 R 1 N N N R 2 R 3 R 2 R 3 pic 50 = 8.1 pic 50 = 8.3 (within 1 log unit of FEP/REST prediction) New molecule was not scheduled for synthesis prior to FEP calculation New back up series is now being actively developed

37 FEP results are encouraging >1000 perturbations ~20 targets MUE ~1.0 kcal/mol Summary Getting the physics right Correct slope, entropy/enthalpy balance, etc. Many potential applications beyond affinity prediction Selectivity Resistance Fragments Allostery PPIs

38 Development Robert Abel Ed Harder Byungchan Kim Jen Knight Goran Krilov Teng Lin Levi Pierce Lingle Wang Yujie Wu Acknowledgements Applications Thijs Beuming Daniel Cappel Roy Kimura Daniel Robinson Michelle Hall Devleena Shivakumar Thomas Steinbrecher Dora Warshaviak Friesner Lab at Columbia Woody Sherman Management Ramy Farid Rich Friesner Woody Sherman Scientific Advisors Bruce Berne John Chodera Bill Jorgensen David Mobley Mark Murcko Vijay Pande