Impact of domain knowledge on blinded predictions of binding energies by alchemical free energy calculations

The Drug Design Data Resource (D3R) consortium organises blinded challenges to address the latest advances in computational methods for ligand pose prediction, affinity ranking, and free energy calculations. Within the context of the second D3R Grand Challenge several blinded binding free energies predictions were made for two congeneric series of Farsenoid X Receptor (FXR) inhibitors with a semi-automated alchemical free energy calculation workflow featuring FESetup and SOMD software tools. Reasonable performance was observed in retrospective analyses of literature datasets. Nevertheless, blinded predictions on the full D3R datasets were poor due to difficulties encountered with the ranking of compounds that vary in their net-charge. Performance increased for predictions that were restricted to subsets of compounds carrying the same net-charge. Disclosure of X-ray crystallography derived binding modes maintained or improved the correlation with experiment in a subsequent rounds of predictions. The best performing protocols on D3R set1 and set2 were comparable or superior to predictions made on the basis of analysis of literature structure activity relationships (SAR)s only, and comparable or slightly inferior, to the best submissions from other groups.

[1]  David L Mobley,et al.  Multiple binding modes of ibuprofen in human serum albumin identified by absolute binding free energy calculations , 2016, bioRxiv.

[2]  Benoît Roux,et al.  Calculation of Standard Binding Free Energies:  Aromatic Molecules in the T4 Lysozyme L99A Mutant. , 2006, Journal of chemical theory and computation.

[3]  Richard D. Smith,et al.  CSAR Benchmark Exercise of 2010: Combined Evaluation Across All Submitted Scoring Functions , 2011, J. Chem. Inf. Model..

[4]  M. Karplus,et al.  Simulation of activation free energies in molecular systems , 1996 .

[5]  W. L. Jorgensen,et al.  Comparison of simple potential functions for simulating liquid water , 1983 .

[6]  Julien Michel,et al.  Blinded predictions of distribution coefficients in the SAMPL5 challenge , 2016, Journal of Computer-Aided Molecular Design.

[7]  Yuan Hu,et al.  The importance of protonation and tautomerization in relative binding affinity prediction: a comparison of AMBER TI and Schrödinger FEP , 2016, Journal of Computer-Aided Molecular Design.

[8]  M. Gilson,et al.  Free energy, entropy, and induced fit in host-guest recognition: calculations with the second-generation mining minima algorithm. , 2004, Journal of the American Chemical Society.

[9]  Michael R. Shirts,et al.  Statistically optimal analysis of samples from multiple equilibrium states. , 2008, The Journal of chemical physics.

[10]  Shoshana J Wodak,et al.  Prediction of protein-protein interactions: the CAPRI experiment, its evaluation and implications. , 2004, Current opinion in structural biology.

[11]  Hannes H. Loeffler,et al.  FESetup: Automating Setup for Alchemical Free Energy Simulations , 2015, J. Chem. Inf. Model..

[12]  Jian Yin,et al.  Overview of the SAMPL5 host–guest challenge: Are we doing better? , 2016, Journal of Computer-Aided Molecular Design.

[13]  Diwakar Shukla,et al.  OpenMM 4: A Reusable, Extensible, Hardware Independent Library for High Performance Molecular Simulation. , 2013, Journal of chemical theory and computation.

[14]  David L Mobley,et al.  Perspective: Alchemical free energy calculations for drug discovery. , 2012, The Journal of chemical physics.

[15]  William L. Jorgensen,et al.  Optimization of azoles as anti-human immunodeficiency virus agents guided by free-energy calculations. , 2008, Journal of the American Chemical Society.

[16]  Nicolas Foloppe,et al.  Rigorous Free Energy Calculations in Structure‐Based Drug Design , 2010, Molecular informatics.

[17]  Benoît Roux,et al.  Free Energy Perturbation Hamiltonian Replica-Exchange Molecular Dynamics (FEP/H-REMD) for Absolute Ligand Binding Free Energy Calculations. , 2010, Journal of chemical theory and computation.

[18]  Franz Schuler,et al.  Discovery of novel and orally active FXR agonists for the potential treatment of dyslipidemia & diabetes. , 2011, Bioorganic & medicinal chemistry letters.

[19]  Jian Yin,et al.  Lessons learned from comparing molecular dynamics engines on the SAMPL5 dataset , 2016, bioRxiv.

[20]  Julien Michel,et al.  Current and emerging opportunities for molecular simulations in structure-based drug design , 2014, Physical chemistry chemical physics : PCCP.

[21]  Julien Michel,et al.  Protein-Ligand Complexes:  Computation of the Relative Free Energy of Different Scaffolds and Binding Modes. , 2007, Journal of chemical theory and computation.

[22]  Krzysztof Fidelis,et al.  CASP10 results compared to those of previous CASP experiments , 2014, Proteins.

[23]  Herman van Vlijmen,et al.  Collaborating to improve the use of free-energy and other quantitative methods in drug discovery , 2016, Journal of Computer-Aided Molecular Design.

[24]  Julien Michel,et al.  Prediction of the water content in protein binding sites. , 2009, The journal of physical chemistry. B.

[25]  Scott P. Brown,et al.  Healthy skepticism: assessing realistic model performance. , 2009, Drug discovery today.

[26]  Kenneth M. Merz,et al.  Drug Design : Structure-and Ligand-Based Approaches , 2017 .

[27]  Thomas Steinbrecher,et al.  Bornyl (3,4,5-trihydroxy)-cinnamate--an optimized human neutrophil elastase inhibitor designed by free energy calculations. , 2008, Bioorganic & medicinal chemistry.

[28]  Julien Michel,et al.  Effects of Water Placement on Predictions of Binding Affinities for p38α MAP Kinase Inhibitors. , 2010, Journal of chemical theory and computation.

[29]  Hans Richter,et al.  Identification of an N-oxide pyridine GW4064 analog as a potent FXR agonist. , 2009, Bioorganic & medicinal chemistry letters.

[30]  David L Mobley,et al.  Alchemical free energy methods for drug discovery: progress and challenges. , 2011, Current opinion in structural biology.

[31]  B. Kuhn,et al.  Optimization of a novel class of benzimidazole-based farnesoid X receptor (FXR) agonists to improve physicochemical and ADME properties. , 2011, Bioorganic & medicinal chemistry letters.

[32]  Robert Abel,et al.  Sensitivity in binding free energies due to protein reorganization , 2016, bioRxiv.

[33]  Jonathan W. Essex,et al.  Prediction of protein–ligand binding affinity by free energy simulations: assumptions, pitfalls and expectations , 2010, J. Comput. Aided Mol. Des..

[34]  Julien Michel,et al.  Blinded predictions of binding modes and energies of HSP90-α ligands for the 2015 D3R grand challenge. , 2016, Bioorganic & medicinal chemistry.

[35]  S. Blanchard,et al.  Development of a scintillation proximity assay for peroxisome proliferator-activated receptor gamma ligand binding domain. , 1998, Analytical biochemistry.

[36]  Shuai Liu,et al.  D3R grand challenge 2015: Evaluation of protein–ligand pose and affinity predictions , 2016, Journal of Computer-Aided Molecular Design.

[37]  Julien Michel,et al.  Elucidation of Nonadditive Effects in Protein-Ligand Binding Energies: Thrombin as a Case Study. , 2016, The journal of physical chemistry. B.

[38]  W. Marsden.  I and J , 2012 .

[39]  Beat Ernst,et al.  Drug discovery today. , 2003, Current topics in medicinal chemistry.

[40]  Julien Michel,et al.  A simple QM/MM approach for capturing polarization effects in protein-ligand binding free energy calculations. , 2011, The journal of physical chemistry. B.

[41]  W. L. Jorgensen,et al.  Monte Carlo simulation of differences in free energies of hydration , 1985 .

[42]  Julien Michel,et al.  Blinded predictions of host-guest standard free energies of binding in the SAMPL5 challenge , 2016, Journal of Computer-Aided Molecular Design.

[43]  J. Essex,et al.  Hit identification and binding mode predictions by rigorous free energy simulations. , 2008, Journal of medicinal chemistry.

[44]  B. Roux,et al.  Absolute binding free energy calculations using molecular dynamics simulations with restraining potentials. , 2006, Biophysical journal.

[45]  W. L. Jorgensen,et al.  Energetics of displacing water molecules from protein binding sites: consequences for ligand optimization. , 2009, Journal of the American Chemical Society.

[46]  James Andrew McCammon,et al.  Ligand-receptor interactions , 1984, Comput. Chem..

[47]  Jennifer L. Knight,et al.  Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field. , 2015, Journal of the American Chemical Society.

[48]  Aric Hagberg,et al.  Exploring Network Structure, Dynamics, and Function using NetworkX , 2008, Proceedings of the Python in Science Conference.

[49]  Jaroslav Koča,et al.  Evaluation of Selected Classical Force Fields for Alchemical Binding Free Energy Calculations of Protein-Carbohydrate Complexes. , 2015, Journal of chemical theory and computation.

[50]  Julien Michel,et al.  Protein-ligand binding affinity predictions by implicit solvent simulations: a tool for lead optimization? , 2006, Journal of medicinal chemistry.