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Blind prediction of host–guest binding affinities: a new SAMPL3 challenge

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Abstract

The computational prediction of protein–ligand binding affinities is of central interest in early-stage drug-discovery, and there is a widely recognized need for improved methods. Low molecular weight receptors and their ligands—i.e., host–guest systems—represent valuable test-beds for such affinity prediction methods, because their small size makes for fast calculations and relatively facile numerical convergence. The SAMPL3 community exercise included the first ever blind prediction challenge for host–guest binding affinities, through the incorporation of 11 new host–guest complexes. Ten participating research groups addressed this challenge with a variety of approaches. Statistical assessment indicates that, although most methods performed well at predicting some general trends in binding affinity, overall accuracy was not high, as all the methods suffered from either poor correlation or high RMS errors or both. There was no clear advantage in using explicit versus implicit solvent models, any particular force field, or any particular approach to conformational sampling. In a few cases, predictions using very similar energy models but different sampling and/or free-energy methods resulted in significantly different results. The protonation states of one host and some guest molecules emerged as key uncertainties beyond the choice of computational approach. The present results have implications for methods development and future blind prediction exercises.

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Acknowledgments

This work was supported in part by Grants GM61300 from the NIGMS to M.K.G, CHE-1110911 from the NSF to L.I., Robert A. Welch Foundation (F-1621) to C.W.B, and NSF grant CHE-0748483 to A.R.U. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIGMS or the National Institutes of Health. We thank Drs. Emilio Gallicchio, Enrico Purisima and Wei Yang for corrections and suggestions.

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Authors and Affiliations

  1. Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, 9500 Gilman Drive, Room# 3224, La Jolla, CA, 92093-0736, USA

    Hari S. Muddana & Michael K. Gilson

  2. Department of Chemistry and Biochemistry, The University of Texas at Austin, Austin, TX, 78212, USA

    C. Daniel Varnado & Christopher W. Bielawski

  3. Department of Chemistry, Trinity University, San Antonio, TX, 78212, USA

    Adam R. Urbach

  4. Department of Chemistry and Biochemistry, University of Maryland, College Park, MD, 20742, USA

    Lyle Isaacs

  5. OpenEye Scientific Software, Inc., Santa Fe, NM, 87508, USA

    Matthew T. Geballe

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  1. Hari S. Muddana
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Correspondence to Michael K. Gilson.

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Muddana, H.S., Daniel Varnado, C., Bielawski, C.W. et al. Blind prediction of host–guest binding affinities: a new SAMPL3 challenge. J Comput Aided Mol Des 26, 475–487 (2012). https://doi.org/10.1007/s10822-012-9554-1

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