D3RGC2: free energy scoring by alchemical free energy implementation in SOMD

Transcription

1 D3RGC2: free energy scoring by alchemical free energy implementation in SOMD Julien Michel EaStCHEM School of Chemistry, University of Edinburgh, United Kingdom D3R webinar 27/03/17

2 Acknowledgements: group Group members Stefano Bosisio Kanhaya Lal Alessio De Simone Cesar Mendoza Martinez Charis Georgiou Antonia Mey Arun Gupta Joan Clark Nicholas Harris Ioannidis Lisa Patrick Jordi Juarez-Jimenez Pattama Wapeesittipan Alumni Juan Bueren-Calabuig George Gerogiokas Gaetano Calabro Kevin Pinto-Gill Remi Cuchillo Sponsors

3 Our alchemical free energy tools FESetup: workflow for automated setup Sire AMBER Gromacs CHARMM SOMD: alchemical free energy calculation engine Loeffler, Michel & Woods JCIM 2015 Woods Eastman et al. Sire: MC & Free Energy OpenMM: MD & GPUs Sire/OpenMM (SOMD) Gaetano Calabro Antonia Mey

4 Semi-automated workflow for binding affinity predictions JulienMap JordiDock Babel Map network Generate 3D poses SMILES (ligands) PDBs (protein) Antonia Mey Setup Perts FESetup ( ) Simulate free Simulate bound SOMD ( ) Analyse Perts analyse_ freenrg Jordi Juarez-Jimenez Human Software Binding free energies Analyse network freenrgworkflows

5 Workflow validation: retrospective studies LitD1 8 compounds 17 perturbations binding modes cycle closures MUE 3.0±0.2 R 0.84±0.05 Feng et al. BMCL 2009, 19, 2595 LitD2 Richter et al. BMCL 2011, 21, (18) compounds 25 perturbations intermediates cycle closures MUE 1.7±0.1 R 0.56±0.03 MUE in kcal/mol ± = 68% CI

6 Polar interactions tend to be exaggerated DDG Computed Experiment +2.5± ± ± DDG in kcal/mol Experimental data from IC 50 s

7 Polarisation? We don t have an off the shelf polarisable force-field So we built a poor man s polarisable force-field

8 Effect of charge scaling on retrospective predictions d ++ DG scale d + d - - d - DDG(A->B) corr = DDG(A->B) + DDG(A->B) scale,bound - DDG(A->B) scale,free Clara Kelly LitD1 Unscaled Scale 0.7 * * 0.5 for net charge change LitD2 MUE 3.0±0.2 R 0.84±0.05 MUE 1.7±0.1 R 0.56± ± ± ± ±0.04 Better Same Worse (than unscaled)

9 D3R submissions summary Stage 1 Expert opinion on binding energies (from JM) Based on predicted binding poses and knowledge of literature SARs Before any calculations were made on D3R sets SOMD free energies from best guess for binding modes Full dataset and partial same net-charge dataset Manual (set1) or automated (set 1&2) way to analyse free energies Stage 2 SOMD free energies from stage 1 binding modes More l values and multiple repeats for poorly converged runs Full dataset and partial same net-charge dataset Default forcefield or charge scaling correction

10 Expert opinion results Set1 Set2 MUE R Full set 1.7± ±0.04 Same net charge 1.8± ±0.04 Full set 1.78± ±0.05 Same net charge N=14 N=13 N=17 N=13 1.7± ±0.07

11 Stage1 binding mode predictions Set1 Set2 RMSD = 0.8 Å RMSD = 2.7 Å Different conformation of K266 & A284 shifts the position of the carboxylic acid. Arylsulfonamide thiophene/benzyl ring oriented differently and pocket shape differs

12 Set1 Stage 1 results * Full set Same net charge MUE R N=14 N= ± ± ± ±0.03 Set2 MUE R N=17 N= ± ± ± ±0.10 * Automated analysis results shown only Better Same Worse (than expert)

13 Stage 2 results Set1 MUE R Full set 2.20± ±0.02 Same net charge 1.41± ±0.02 Full set scaled Same net charge scaled N=14 N=13 N=14 N= ± ± ± ±0.02 N=17 N=13 N=17 N=13 Set2 MUE R 3.79± ± ± ± ± ± ± ±0.05 Better Same Worse (than expert)

14 Overall performance Set1 Set2 Stage1 SOMD Alchemical QM MMPBSA Other Stage2

15 Lessons learned Electrostatics need attention We expected that from retrospective predictions Charge scaling fix not robust We automated analysis of free energies We need more automation Protein setup Mappings dataset Multiple binding modes Lambda schedule

16 Suggestions D3R GC1: datasets too small for robust conclusions D3R GC2: datasets large enough to rank methods by metrics precision of calculated free energies but too small to test robustness of methods to dataset composition bootstrapping on small populations Failures hard to diagnose because likely due to multiple sources of errors Case for complementing GCs with datasets of intermediate complexity (e.g. host-guests).

17 Protocol details Forcefield Protein: Amber 14SB, Ligand: GAFF2, hfe, Water: TIP3P Cutoffs: 10 Å, reaction-field, dielectric 82.0 NPT ensemble 2 fs timestep λ intermediates ranged between 9 and 26 evenly spaced λ windows, based on opinion about difficulty of the free energy perturbations. 4 ns/λ Two different starting conformations, multiple repeats

18 Expert opinion results (same net-charge) Set1 Set2

19 Stage 2 results Set1 MUE R Full set Same net charge Full set scaled Same net charge scaled N=14 N=13 N=14 N= ± ± ± ± ± ± ± ±0.02 Set2 MUE R N=17 N=13 N=17 N= ± ± ± ± ± ± ± ±0.05 Better Same Worse (than stage 1)