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Absolute binding free energies for octa-acids and guests in SAMPL5 Evaluating binding free energies for octa-acid and guest complexes in the SAMPL5 blind challenge

Cited 13 time in Web of Science Cited 14 time in Scopus
Authors

Tofoleanu, Florentina; Lee, Ju Yong; Pickard, Frank C.; Koenig, Gerhard; Huang, Jing; Baek, Minkyung; Seok, Chaok; Brooks, Bernard R.

Issue Date
2017-01
Publisher
Kluwer Academic Publishers
Citation
Journal of Computer-Aided Molecular Design, Vol.31 No.1, pp.107-118
Abstract
As part of the SAMPL5 blind prediction challenge, we calculate the absolute binding free energies of six guest molecules to an octa-acid (OAH) and to a methylated octa-acid (OAMe). We use the double decoupling method via thermodynamic integration (TI) or Hamiltonian replica exchange in connection with the Bennett acceptance ratio (HREM-BAR). We produce the binding poses either through manual docking or by using GalaxyDock-HG, a docking software developed specifically for this study. The root mean square deviations for our most accurate predictions are 1.4 kcal mol(-1) for OAH with TI and 1.9 kcal mol(-1) for OAMe with HREM-BAR. Our best results for OAMe were obtained for systems with ionic concentrations corresponding to the ionic strength of the experimental solution. The most problematic system contains a halogenated guest. Our attempt to model the sigma-hole of the bromine using a constrained off-site point charge, does not improve results. We use results from molecular dynamics simulations to argue that the distinct binding affinities of this guest to OAH and OAMe are due to a difference in the flexibility of the host. We believe that the results of this extensive analysis of host-guest complexes will help improve the protocol used in predicting binding affinities for larger systems, such as protein-substrate compounds.
ISSN
0920-654X
URI
https://hdl.handle.net/10371/201527
DOI
https://doi.org/10.1007/s10822-016-9965-5
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  • Graduate School of Convergence Science & Technology
  • Dept. of Molecular and Biopharmaceutical Sciences
Research Area AI models for drug discovery, Free energy calculation, Molecular dynamics, 분자동역학, 신약개발을 위한 AI 모델, 자유에너지 계산

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