Journal of Computer-Aided Molecular Design

, Volume 28, Issue 4, pp 463–474 | Cite as

Blind prediction of SAMPL4 cucurbit[7]uril binding affinities with the mining minima method

  • Hari S. Muddana
  • Jian Yin
  • Neil V. Sapra
  • Andrew T. Fenley
  • Michael K. Gilson
Article
First Online:
Received:
Accepted:

Abstract

Accurate methods for predicting protein–ligand binding affinities are of central interest to computer-aided drug design for hit identification and lead optimization. Here, we used the mining minima (M2) method to predict cucurbit[7]uril binding affinities from the SAMPL4 blind prediction challenge. We tested two different energy models, an empirical classical force field, CHARMm with VCharge charges, and the Poisson–Boltzmann surface area solvation model; and a semiempirical quantum mechanical (QM) Hamiltonian, PM6-DH+, coupled with the COSMO solvation model and a surface area term for nonpolar solvation free energy. Binding affinities based on the classical force field correlated strongly with the experiments with a correlation coefficient (R2) of 0.74. On the other hand, binding affinities based on the QM energy model correlated poorly with experiments (R2 = 0.24), due largely to two major outliers. As we used extensive conformational search methods, these results point to possible inaccuracies in the PM6-DH+ energy model or the COSMO solvation model. Furthermore, the different binding free energy components, solute energy, solvation free energy, and configurational entropy showed significant deviations between the classical M2 and quantum M2 calculations. Comparison of different classical M2 free energy components to experiments show that the change in the total energy, i.e. the solute energy plus the solvation free energy, is the key driving force for binding, with a reasonable correlation to experiment (R2 = 0.56); however, accounting for configurational entropy further improves the correlation.

Keywords

SAMPL4 Supramolecular Binding affinity Host–guest Force field Semiempirical quantum 
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Notes

Acknowledgments

This study was made possible in part by grant GM61300 from the NIGMS. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH. Principal Investigator M.K.G. is a founder of and has an equity interest in VeraChem LLC. Although grant GM61300 has been identified for conflict of interest management based on the overall scope of the project and its potential benefit to VeraChem LLC, the research findings included in this particular publication may not necessarily relate to the interests of VeraChem LLC. The terms of this arrangement have been reviewed and approved by the University of California, San Diego in accordance with its conflict of interest policies.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Hari S. Muddana
    • 1
  • Jian Yin
    • 1
  • Neil V. Sapra
    • 1
  • Andrew T. Fenley
    • 1
  • Michael K. Gilson
    • 1
  1. 1.Skaggs School of Pharmacy and Pharmaceutical SciencesUniversity of California San DiegoLa JollaUSA

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