BEDAM binding free energy predictions for the SAMPL4 octa-acid host challenge

The binding energy distribution analysis method (BEDAM) protocol has been employed as part of the SAMPL4 blind challenge to predict the binding free energies of a set of octa-acid host–guest complexes. The resulting predictions were consistently judged as some of the most accurate predictions in this category of the SAMPL4 challenge in terms of quantitative accuracy and statistical correlation relative to the experimental values, which were not known at the time the predictions were made. The work has been conducted as part of a hands-on graduate class laboratory session. Collectively the students, aided by automated setup and analysis tools, performed the bulk of the calculations and the numerical and structural analysis. The success of the experiment confirms the reliability of the BEDAM methodology and it shows that physics-based atomistic binding free energy estimation models, when properly streamlined and automated, can be successfully employed by non-specialists.

[1]  Lingle Wang,et al.  On achieving high accuracy and reliability in the calculation of relative protein–ligand binding affinities , 2012, Proceedings of the National Academy of Sciences.

[2]  Jonathan C. Horton,et al.  What you see ... , 2001, Nature.

[3]  Richard A. Friesner,et al.  Docking performance of the glide program as evaluated on the Astex and DUD datasets: a complete set of glide SP results and selected results for a new scoring function integrating WaterMap and glide , 2012, Journal of Computer-Aided Molecular Design.

[4]  Ronald M. Levy,et al.  AGBNP: An analytic implicit solvent model suitable for molecular dynamics simulations and high‐resolution modeling , 2004, J. Comput. Chem..

[5]  Emilio Gallicchio,et al.  The Binding Energy Distribution Analysis Method (BEDAM) for the Estimation of Protein-Ligand Binding Affinities. , 2010, Journal of chemical theory and computation.

[6]  David L. Mobley,et al.  The SAMPL4 host–guest blind prediction challenge: an overview , 2014, Journal of Computer-Aided Molecular Design.

[7]  Michael K Gilson,et al.  Grid inhomogeneous solvation theory: hydration structure and thermodynamics of the miniature receptor cucurbit[7]uril. , 2012, The Journal of chemical physics.

[8]  K. Dill,et al.  Binding of small-molecule ligands to proteins: "what you see" is not always "what you get". , 2009, Structure.

[9]  David S. Goodsell,et al.  AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility , 2009, J. Comput. Chem..

[10]  R. Levy,et al.  Large scale affinity calculations of cyclodextrin host-guest complexes: Understanding the role of reorganization in the molecular recognition process. , 2013, Journal of chemical theory and computation.

[11]  Richard D. Smith,et al.  CSAR Data Set Release 2012: Ligands, Affinities, Complexes, and Docking Decoys , 2013, J. Chem. Inf. Model..

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

[13]  Hege S. Beard,et al.  Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. , 2004, Journal of medicinal chemistry.

[14]  Traian Sulea,et al.  Exhaustive docking and solvated interaction energy scoring: lessons learned from the SAMPL4 challenge , 2014, Journal of Computer-Aided Molecular Design.

[15]  J. Irwin,et al.  Lead discovery using molecular docking. , 2002, Current opinion in chemical biology.

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

[17]  A. Spek,et al.  Self-assembled biomimetic [2Fe2S]-hydrogenase-based photocatalyst for molecular hydrogen evolution , 2009, Proceedings of the National Academy of Sciences.

[18]  David L. Mobley,et al.  Drug Design: Free-energy calculations in structure-based drug design , 2010 .

[19]  Anthony Nicholls,et al.  The SAMPL2 blind prediction challenge: introduction and overview , 2010, J. Comput. Aided Mol. Des..

[20]  Z. Tan,et al.  Theory of binless multi-state free energy estimation with applications to protein-ligand binding. , 2012, The Journal of chemical physics.

[21]  Arthur J. Olson,et al.  Virtual screening of integrase inhibitors by large scale binding free energy calculations: the SAMPL4 challenge , 2014, Journal of Computer-Aided Molecular Design.

[22]  David S. Goodsell,et al.  Automated Docking of Flexible Ligands to Flexible Receptors , 2010 .

[23]  Arthur J. Olson,et al.  Virtual screening with AutoDock Vina and the common pharmacophore engine of a low diversity library of fragments and hits against the three allosteric sites of HIV integrase: participation in the SAMPL4 protein–ligand binding challenge , 2014, Journal of Computer-Aided Molecular Design.

[24]  Richard A. Friesner,et al.  Integrated Modeling Program, Applied Chemical Theory (IMPACT) , 2005, J. Comput. Chem..

[25]  Ronald M. Levy,et al.  Prediction of SAMPL3 host-guest affinities with the binding energy distribution analysis method (BEDAM) , 2012, Journal of Computer-Aided Molecular Design.

[26]  Emilio Gallicchio,et al.  The AGBNP2 Implicit Solvation Model. , 2009, Journal of chemical theory and computation.

[27]  Matthew P. Repasky,et al.  Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. , 2004, Journal of medicinal chemistry.

[28]  Pablo G Debenedetti,et al.  Effect of pressure on the phase behavior and structure of water confined between nanoscale hydrophobic and hydrophilic plates. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[29]  W. Sherman,et al.  Thermodynamic analysis of water molecules at the surface of proteins and applications to binding site prediction and characterization , 2011, Proteins.

[30]  Thomas S. Peat,et al.  Interrogating HIV integrase for compounds that bind- a SAMPL challenge , 2013, Journal of Computer-Aided Molecular Design.

[31]  Ricardo A. Mata,et al.  Free-energy perturbation and quantum mechanical study of SAMPL4 octa-acid host–guest binding energies , 2014, Journal of Computer-Aided Molecular Design.

[32]  S. Rick,et al.  Water inside a hydrophobic cavitand molecule. , 2008, The journal of physical chemistry. B.

[33]  Hao Sun,et al.  Calorimetric Analysis of the 1:1 Complexes Formed between a Water-soluble Deep-cavity Cavitand, and Cyclic and Acyclic Carboxylic Acids , 2008 .

[34]  Emilio Gallicchio,et al.  Role of Ligand Reorganization and Conformational Restraints on the Binding Free Energies of DAPY Non-Nucleoside Inhibitors to HIV Reverse Transcriptase. , 2012, Computational molecular bioscience.

[35]  David L. Mobley,et al.  Let’s get honest about sampling , 2011, Journal of Computer-Aided Molecular Design.

[36]  Richard A. Friesner,et al.  Comparative Performance of Several Flexible Docking Programs and Scoring Functions: Enrichment Studies for a Diverse Set of Pharmaceutically Relevant Targets. , 2007 .

[37]  Emilio Gallicchio,et al.  Recent theoretical and computational advances for modeling protein-ligand binding affinities. , 2011, Advances in protein chemistry and structural biology.

[38]  Ryan G. Coleman,et al.  SAMPL4 & DOCK3.7: lessons for automated docking procedures , 2014, Journal of Computer-Aided Molecular Design.

[39]  D S Goodsell,et al.  Automated docking of flexible ligands: Applications of autodock , 1996, Journal of molecular recognition : JMR.

[40]  Emilio Gallicchio,et al.  Advances in all atom sampling methods for modeling protein-ligand binding affinities. , 2011, Current opinion in structural biology.

[41]  Emilio Gallicchio,et al.  The non-polar solvent potential of mean force for the dimerization of alanine dipeptide: the role of solute-solvent van der Waals interactions. , 2004, Biophysical chemistry.

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

[43]  Jian Yin,et al.  Blind prediction of SAMPL4 cucurbit[7]uril binding affinities with the mining minima method , 2014, Journal of Computer-Aided Molecular Design.

[44]  Scott P. Brown,et al.  Large-scale application of high-throughput molecular mechanics with Poisson-Boltzmann surface area for routine physics-based scoring of protein-ligand complexes. , 2009, Journal of medicinal chemistry.

[45]  R. Friesner,et al.  Evaluation and Reparametrization of the OPLS-AA Force Field for Proteins via Comparison with Accurate Quantum Chemical Calculations on Peptides† , 2001 .

[46]  Corinne L. D. Gibb,et al.  Binding of cyclic carboxylates to octa-acid deep-cavity cavitand , 2014, Journal of Computer-Aided Molecular Design.

[47]  Michael K. Gilson,et al.  Overcoming dissipation in the calculation of standard binding free energies by ligand extraction , 2013, J. Comput. Chem..

[48]  Emilio Gallicchio,et al.  On the nonpolar hydration free energy of proteins: surface area and continuum solvent models for the solute-solvent interaction energy. , 2003, Journal of the American Chemical Society.

[49]  Shuai Liu,et al.  Blind prediction of HIV integrase binding from the SAMPL4 challenge , 2014, Journal of Computer-Aided Molecular Design.

[50]  W. L. Jorgensen,et al.  Development and Testing of the OPLS All-Atom Force Field on Conformational Energetics and Properties of Organic Liquids , 1996 .

[51]  Wendy A. Warr.  Data sharing matters , 2014, Journal of Computer-Aided Molecular Design.

[52]  W. L. Jorgensen.  The Many Roles of Computation in Drug Discovery , 2004, Science.

[53]  W. C. Still,et al.  The GB/SA Continuum Model for Solvation. A Fast Analytical Method for the Calculation of Approximate Born Radii , 1997 .

[54]  Shuai Liu,et al.  Lead optimization mapper: automating free energy calculations for lead optimization , 2013, Journal of Computer-Aided Molecular Design.

[55]  Lingle Wang,et al.  Hydrophobic interactions in model enclosures from small to large length scales: non-additivity in explicit and implicit solvent models. , 2010, Faraday discussions.

[56]  M. Gilson,et al.  The statistical-thermodynamic basis for computation of binding affinities: a critical review. , 1997, Biophysical journal.

[57]  Robert Abel,et al.  Motifs for molecular recognition exploiting hydrophobic enclosure in protein–ligand binding , 2007, Proceedings of the National Academy of Sciences.

[58]  Emilio Gallicchio,et al.  Conformational Transitions and Convergence of Absolute Binding Free Energy Calculations. , 2012, Journal of chemical theory and computation.

[59]  Michael K. Gilson,et al.  Blind prediction of host–guest binding affinities: a new SAMPL3 challenge , 2012, Journal of Computer-Aided Molecular Design.

[60]  Stefan Boresch,et al.  Absolute Binding Free Energies: A Quantitative Approach for Their Calculation , 2003 .