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The SAMPL4 host–guest blind prediction challenge: an overview

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Abstract

Prospective validation of methods for computing binding affinities can help assess their predictive power and thus set reasonable expectations for their performance in drug design applications. Supramolecular host–guest systems are excellent model systems for testing such affinity prediction methods, because their small size and limited conformational flexibility, relative to proteins, allows higher throughput and better numerical convergence. The SAMPL4 prediction challenge therefore included a series of host–guest systems, based on two hosts, cucurbit[7]uril and octa-acid. Binding affinities in aqueous solution were measured experimentally for a total of 23 guest molecules. Participants submitted 35 sets of computational predictions for these host–guest systems, based on methods ranging from simple docking, to extensive free energy simulations, to quantum mechanical calculations. Over half of the predictions provided better correlations with experiment than two simple null models, but most methods underperformed the null models in terms of root mean squared error and linear regression slope. Interestingly, the overall performance across all SAMPL4 submissions was similar to that for the prior SAMPL3 host–guest challenge, although the experimentalists took steps to simplify the current challenge. While some methods performed fairly consistently across both hosts, no single approach emerged as consistent top performer, and the nonsystematic nature of the various submissions made it impossible to draw definitive conclusions regarding the best choices of energy models or sampling algorithms. Salt effects emerged as an issue in the calculation of absolute binding affinities of cucurbit[7]uril-guest systems, but were not expected to affect the relative affinities significantly. Useful directions for future rounds of the challenge might involve encouraging participants to carry out some calculations that replicate each others’ studies, and to systematically explore parameter options.

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Acknowledgments

We thank OpenEye software for providing logistical support in the form of web hosting, technical support, and some financial support for the SAMPL4 workshop. We also thank Jay Ponder (Washington University) and Teng Lin (Schrödinger) for helpful discussions about analyzing absolute versus relative free energy predictions, and Vijay Pande for hosting the workshop at Stanford University. DLM acknowledges the financial support of the National Institutes of Health (1R15GM096257-01A1) and appreciates the support of the GreenPlanet computing facility at UC Irvine, supported in part by NSF CHE-0840513. MKG acknowledges funding from National Institute of General Medical Sciences (GM61300). The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the NIGMS, NIH, or NSF.

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  1. Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, 9500 Gilman Drive, MC 0736, La Jolla, CA, 92093-0736, USA

    Hari S. Muddana, Andrew T. Fenley & Michael K. Gilson

  2. Departments of Pharmaceutical Sciences and Chemistry, University of California Irvine, 147 Bison Modular, Irvine, CA, 92697, USA

    David L. Mobley

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  1. Hari S. Muddana
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  2. Andrew T. Fenley
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Correspondence to David L. Mobley or Michael K. Gilson.

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Muddana, H.S., Fenley, A.T., Mobley, D.L. et al. The SAMPL4 host–guest blind prediction challenge: an overview. J Comput Aided Mol Des 28, 305–317 (2014). https://doi.org/10.1007/s10822-014-9735-1

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