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Overview of the SAMPL6 host-guest binding affinity prediction challenge

View ORCID ProfileAndrea Rizzi, View ORCID ProfileSteven Murkli, View ORCID ProfileJohn N. McNeill, View ORCID ProfileWei Yao, View ORCID ProfileMatthew Sullivan, View ORCID ProfileMichael K. Gilson, Michael W. Chiu, View ORCID ProfileLyle Isaacs, View ORCID ProfileBruce C. Gibb, View ORCID ProfileDavid L. Mobley, View ORCID ProfileJohn D. Chodera
doi: https://doi.org/10.1101/371724
Andrea Rizzi
1Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
2Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY 10065, USA
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Steven Murkli
3Department of Chemistry and Biochemistry, University of Maryland, College Park, MD 20742, USA
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John N. McNeill
3Department of Chemistry and Biochemistry, University of Maryland, College Park, MD 20742, USA
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Wei Yao
4Department of Chemistry, Tulane University, Louisiana, LA 70118, USA
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Matthew Sullivan
4Department of Chemistry, Tulane University, Louisiana, LA 70118, USA
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Michael K. Gilson
5Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA 92093, USA
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Michael W. Chiu
6Qualcomm Institute, University of California, San Diego, La Jolla, CA 92093, USA
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Lyle Isaacs
3Department of Chemistry and Biochemistry, University of Maryland, College Park, MD 20742, USA
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Bruce C. Gibb
4Department of Chemistry, Tulane University, Louisiana, LA 70118, USA
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David L. Mobley
7Department of Pharmaceutical Sciences and Department of Chemistry, University of California, Irvine, California 92697, USA
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  • For correspondence: dmobley@uci.edu john.chodera@choderalab.org
John D. Chodera
1Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
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  • For correspondence: dmobley@uci.edu john.chodera@choderalab.org
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Abstract

The ability to accurately predict the binding affinities of small organic molecules to biological macromolecules would greatly accelerate drug discovery by reducing the number of compounds that must be synthesized to realize desired potency and selectivity goals. Unfortunately, the process of assessing the accuracy of current quantitative physical and empirical modeling approaches to affinity prediction against binding data to biological macromolecules is frustrated by several challenges, such as slow conformational dynamics, multiple titratable groups, and the lack of high-quality blinded datasets. Over the last several SAMPL blind challenge exercises, host-guest systems have emerged as a practical and effective way to circumvent these challenges in assessing the predictive performance of current-generation quantitative modeling tools, while still providing systems capable of possessing tight binding affinities. Here, we present an overview of the SAMPL6 host-guest binding affinity prediction challenge, which featured three supramolecular hosts: octa-acid (OA), the closely related tetra-endo-methyl-octa-acid (TEMOA), and cucurbit[8]uril (CB8), along with 21 small organic guest molecules. A total of 119 entries were received from 10 participating groups employing a variety of methods that spanned electronic structure and movable type calculations in implicit solvent to alchemical and potential of mean force strategies using empirical force fields and explicit solvent models. While empirical models tended to obtain better performance, it was not possible to identify a single approach consistently providing superior predictions across all host-guest systems and statistical metrics, and the accuracy of the methodologies generally displayed a substantial dependence on the systems considered, arguing for the importance of considering a diverse set of hosts in blind evaluations. Several entries exploited previous experimental measurements of similar host-guest systems in an effort to improve their physical-based predictions via some manner of rudimentary machine learning; while this strategy succeeded in reducing systematic errors, it was not able to generated a corresponding improvement of correlation statistics. Comparison to previous rounds of the host-guest binding free energy challenge highlights an overall improvement in the correlation obtained by the affinity predictions for OA and TEMOA systems, but a surprising lack of improvement in root mean square error over the past several challenge rounds. The data suggests that further refinement of force field parameters and improved treatment of chemical effects (e.g., buffer salt conditions, protonation states) may be required to continue to enhance predictive accuracy.

  • List of abbreviations

    AM1-BCC
    Austin model 1 bond charge correction [64, 65]
    AMOEBA
    atomic multipole optimized energetics for biomolecular simulation [87]
    B3LYP
    Becke 3-parameter Lee-Yang-Parr exchange-correlation functional [96]
    B3PW91
    Becke 3-parameter Perdew-Wang 91 exchange-correlation functional [96]
    CGenFF
    CHARMM generalized force field [91]
    COSMO-RS
    conductor-like screening model for real solvents [72]
    DDM
    double decoupling method [73]
    DFT-D3
    density functional theory with the D3 dispersion corrections [98]
    FM
    Force Matching [74]
    FSDAM
    Fast switching double annihilation method [75, 76]
    GAFF
    generalized AMBER force field [89]
    HREX
    Hamiltonian replica exchange [99]
    KECSA
    knowledge-based and empirical combined scoring algorithm [92]
    KMTISM
    KECSA-Movable Type Implicit Solvation Model [77]
    MD
    molecular dynamics
    MMPBSA
    molecular mechanics Poisson Boltzmann/solvent accessible surface area [103]
    MovTyp
    Movable Type method [78]
    OPLS3
    optimized potential for liquid simulations [71]
    PBSA
    Poisson-Boltzmann surface area [79]
    PM6-DH+
    PM6 semiempirical method with dispersion and hydrogen bonding corrections [94, 95]
    RESP
    restrained electrostatic potential [90]
    REST
    replica exchange with solute torsional tempering [80, 81]
    RFEC
    relative free energy calculation
    QM/MM
    mixed quantum mechanics and molecular mechanics
    SOMD
    double annihilation or decoupling method performed with Sire/OpenMM6.3 software [82, 83]
    SQM
    semi-empirical quantum mechanics
    TIP3P
    transferable interaction potential three-point [86]
    TPSS
    Tao, Perdew, Staroverov, and Scuseria exchange functional [97]
    US
    umbrella sampling [84]
    VSGB2.1
    VSGB2.0 solvation model refit to OPLS2.1/3/3e [85]
  • Copyright 
    The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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    Posted July 19, 2018.
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    Overview of the SAMPL6 host-guest binding affinity prediction challenge
    Andrea Rizzi, Steven Murkli, John N. McNeill, Wei Yao, Matthew Sullivan, Michael K. Gilson, Michael W. Chiu, Lyle Isaacs, Bruce C. Gibb, David L. Mobley, John D. Chodera
    bioRxiv 371724; doi: https://doi.org/10.1101/371724
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    Overview of the SAMPL6 host-guest binding affinity prediction challenge
    Andrea Rizzi, Steven Murkli, John N. McNeill, Wei Yao, Matthew Sullivan, Michael K. Gilson, Michael W. Chiu, Lyle Isaacs, Bruce C. Gibb, David L. Mobley, John D. Chodera
    bioRxiv 371724; doi: https://doi.org/10.1101/371724

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