Journal of Computer-Aided Molecular Design

, Volume 26, Issue 5, pp 475–487

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

  • Hari S. Muddana
  • C. Daniel Varnado
  • Christopher W. Bielawski
  • Adam R. Urbach
  • Lyle Isaacs
  • Matthew T. Geballe
  • Michael K. Gilson
Article

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.

Keywords

SAMPL3 Host–guest Binding Blind prediction Free energy 

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Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • Hari S. Muddana
    • 1
  • C. Daniel Varnado
    • 2
  • Christopher W. Bielawski
    • 2
  • Adam R. Urbach
    • 3
  • Lyle Isaacs
    • 4
  • Matthew T. Geballe
    • 5
  • Michael K. Gilson
    • 1
  1. 1.Skaggs School of Pharmacy and Pharmaceutical SciencesUniversity of California San DiegoLa JollaUSA
  2. 2.Department of Chemistry and BiochemistryThe University of Texas at AustinAustinUSA
  3. 3.Department of ChemistryTrinity UniversitySan AntonioUSA
  4. 4.Department of Chemistry and BiochemistryUniversity of MarylandCollege ParkUSA
  5. 5.OpenEye Scientific Software, Inc.Santa FeUSA

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