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The SAMPL6 SAMPLing challenge: assessing the reliability and efficiency of binding free energy calculations

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Abstract

Approaches for computing small molecule binding free energies based on molecular simulations are now regularly being employed by academic and industry practitioners to study receptor-ligand systems and prioritize the synthesis of small molecules for ligand design. Given the variety of methods and implementations available, it is natural to ask how the convergence rates and final predictions of these methods compare. In this study, we describe the concept and results for the SAMPL6 SAMPLing challenge, the first challenge from the SAMPL series focusing on the assessment of convergence properties and reproducibility of binding free energy methodologies. We provided parameter files, partial charges, and multiple initial geometries for two octa-acid (OA) and one cucurbit[8]uril (CB8) host–guest systems. Participants submitted binding free energy predictions as a function of the number of force and energy evaluations for seven different alchemical and physical-pathway (i.e., potential of mean force and weighted ensemble of trajectories) methodologies implemented with the GROMACS, AMBER, NAMD, or OpenMM simulation engines. To rank the methods, we developed an efficiency statistic based on bias and variance of the free energy estimates. For the two small OA binders, the free energy estimates computed with alchemical and potential of mean force approaches show relatively similar variance and bias as a function of the number of energy/force evaluations, with the attach-pull-release (APR), GROMACS expanded ensemble, and NAMD double decoupling submissions obtaining the greatest efficiency. The differences between the methods increase when analyzing the CB8-quinine system, where both the guest size and correlation times for system dynamics are greater. For this system, nonequilibrium switching (GROMACS/NS-DS/SB) obtained the overall highest efficiency. Surprisingly, the results suggest that specifying force field parameters and partial charges is insufficient to generally ensure reproducibility, and we observe differences between seemingly converged predictions ranging approximately from 0.3 to 1.0 kcal/mol, even with almost identical simulations parameters and system setup (e.g., Lennard-Jones cutoff, ionic composition). Further work will be required to completely identify the exact source of these discrepancies. Among the conclusions emerging from the data, we found that Hamiltonian replica exchange—while displaying very small variance—can be affected by a slowly-decaying bias that depends on the initial population of the replicas, that bidirectional estimators are significantly more efficient than unidirectional estimators for nonequilibrium free energy calculations for systems considered, and that the Berendsen barostat introduces non-negligible artifacts in expanded ensemble simulations.

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Code availability

Input files and setup scripts: https://github.com/samplchallenges/SAMPL6/tree/master/host_guest/SAMPLing/. Analysis scripts: https://github.com/samplchallenges/SAMPL6/tree/master/host_guest/Analysis/Scripts/. Analysis results: https://github.com/samplchallenges/SAMPL6/tree/master/host_guest/Analysis/SAMPLing/. Participant submissions: https://github.com/samplchallenges/SAMPL6/tree/master/host_guest/Analysis/Submissions/SAMPLing/

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Acknowledgements

AR and JDC acknowledge support from the Sloan Kettering Institute. JDC acknowledges support from NIH grant P30 CA008748. AR acknowledges partial support from the Tri-Institutional Program in Computational Biology and Medicine. DLM appreciates financial support from the National Institutes of Health (1R01GM108889-01 and 1R01GM124270-01A1) and the National Science Foundation (CHE 1352608). AR and JDC are grateful to OpenEye Scientific for providing a free academic software license for use in this work. MA was supported by a Postdoctoral Research Fellowship of the Alexander von Humboldt Foundation. VG and BLdG were supported by BioExcel CoE, a project funded by the European Union contract H2020-INFRAEDI-02-2018-823830. MKG thanks NIGMS (NIH) for partial support of this project (GM061300). The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the NIH. AD acknowledges support from the National Institutes of Health (R01GM130794) and the National Science Foundation (DMS 1761320). ZC and DN would like to thank Stamatia Zavitsanou, Michail Papadourakis and Chris Chipot for useful discussions. This work was further supported by computational time granted from the Greek Research & Technology Network (GRNET) in the National HPC facility—ARIS—under Project ID vspr001005/arp2/3. ZC acknowledges support of this work by the project “An Open-Access Research Infrastructure of Chemical Biology and Target-Based Screening Technologies for Human and Animal Health, Agriculture and the Environment (OPENSCREEN-GR)” (MIS 5002691), which is implemented under the Action “Reinforcement of the Research and Innovation Infrastructure”, funded by the Operational Programme “Competitiveness, Entrepreneurship and Innovation” (NSRF 2014-2020) and co-financed by Greece and the European Union (European Regional Development Fund). TJ and MRS acknowledge a CU Boulder Research and Innovation Seed Grant.

Disclosures

JDC was a member of the Scientific Advisory Board for Schrödinger, LLC during part of this study. JDC and DLM are current members of the Scientific Advisory Board of OpenEye Scientific Software. The Chodera laboratory receives or has received funding from multiple sources, including the National Institutes of Health, the National Science Foundation, the Parker Institute for Cancer Immunotherapy, Relay Therapeutics, Entasis Therapeutics, Silicon Therapeutics, EMD Serono (Merck KGaA), AstraZeneca, Vir Biotechnology, XtalPi, the Molecular Sciences Software Institute, the Starr Cancer Consortium, the Open Force Field Consortium, Cycle for Survival, a Louis V. Gerstner Young Investigator Award, and the Sloan Kettering Institute. A complete funding history for the Chodera lab can be found at http://choderalab.org/funding. MKG has an equity interest in, and is a cofounder and scientific advisor of VeraChem LLC.

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Authors and Affiliations

  1. Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA

    Andrea Rizzi & John D. Chodera

  2. Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY, 10065, USA

    Andrea Rizzi

  3. Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, 80309, USA

    Travis Jensen & Michael R. Shirts

  4. Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, 92093, USA

    David R. Slochower, Niel M. Henriksen & Michael K. Gilson

  5. Max Planck Institute for Biophysical Chemistry, Computational Biomolecular Dynamics Group, Göttingen, Germany

    Matteo Aldeghi, Vytautas Gapsys & Bert L. de Groot

  6. Biomedical Research Foundation, Academy of Athens, 4 Soranou Ephessiou, 11527, Athens, Greece

    Dimitris Ntekoumes & Zoe Cournia

  7. EaStCHEM School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh, EH9 3FJ, UK

    Stefano Bosisio, Michail Papadourakis & Julien Michel

  8. Atomwise, 717 Market St Suite 800, San Francisco, CA, 94103, USA

    Niel M. Henriksen

  9. Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, USA

    Alex Dickson

  10. Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI, USA

    Alex Dickson

  11. Department of Pharmaceutical Sciences and Department of Chemistry, University of California, Irvine, California, 92697, USA

    David L. Mobley

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  1. Andrea Rizzi
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  2. Travis Jensen
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Contributions

Conceptualization: DLM, AR, JDC, MS, JM; Data Curation: AR; Formal Analysis: AR; Funding Acquisition: JDC, DLM, MRS, MKG, AD, BLdG, JM, ZC; Investigation: AR, TJ, DRS, MA, VG, AD, DN, SB, NMH, MP; Methodology: AR, DLM, MKG, JM, JDC; Project Administration: AR, DLM, JDC; Resources: JDC, MRS, MKG, ZC, JM, AD, BLdG; Software: AR; Supervision: JDC, MRS, MKG, DLM, JM, ZC, AD, BLdG; Visualization: AR, VG, TJ; Writing—Original Draft: AR; Writing—Review & Editing: AR, MKG, JDC, DLM, DRS, MRS, MA, VG, JM, DN, AD, ZC, BLdG.

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Correspondence to Andrea Rizzi, David L. Mobley or John D. Chodera.

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Rizzi, A., Jensen, T., Slochower, D.R. et al. The SAMPL6 SAMPLing challenge: assessing the reliability and efficiency of binding free energy calculations. J Comput Aided Mol Des 34, 601–633 (2020). https://doi.org/10.1007/s10822-020-00290-5

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