Prediction of SAMPL4 host-guest binding affinities using funnel metadynamics

Ya-Wen Hsiao, Pär Söderhjelm

Research output: Contribution to journalArticlepeer-review

14 Citations (SciVal)

Abstract

Accurately predicting binding affinities between ligands and macromolecules has been a much sought-after goal. A tremendous amount of resources can be saved in the pharmaceutical industry through accurate binding-affinity prediction and hence correct decision-making for the drug discovery processes. Owing to the structural complexity of macromolecules, one of the issues in binding affinity prediction using molecular dynamics is the adequate sampling of the conformational space. Recently, the funnel metadynamics method (Limongelli et al. in Proc Natl Acad Sci USA 110: 6358, 2013) was developed to enhance the sampling of the ligand at the binding site as well as in the solvated state, and offer the possibility to predict the absolute binding free energy. We apply funnel metadynamics to predict host-guest binding affinities for the cucurbit[7]uril host as part of the SAMPL4 blind challenge. Using total simulation times of 300-400 ns per ligand, we show that the errors due to inadequate sampling are below 1 kcal/mol. However, despite the large investment in terms of computational time, the results compared to experiment are not better than a random guess. As we obtain differences of up to 11 kcal/mol when switching between two commonly used force fields (with automatically generated parameters), we strongly believe that in the pursuit of accurate binding free energies a more careful force-field parametrization is needed to address this type of system.
Original languageEnglish
Pages (from-to)443-454
JournalJournal of Computer-Aided Molecular Design
Volume28
Issue number4
DOIs
Publication statusPublished - 2014

Bibliographical note

The information about affiliations in this record was updated in December 2015.
The record was previously connected to the following departments: Theoretical Chemistry (S) (011001039)

Subject classification (UKÄ)

  • Theoretical Chemistry

Keywords

  • Funnel metadynamics
  • Binding affinity
  • CGENFF
  • GAFF
  • NAMD

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