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Cited by 194

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Modeling Local Structural Rearrangements Using FEP/REST: Application to Relative Binding Affinity Predictions of CDK2 Inhibitors

Wang, Lingle, Deng, Yuqing, Knight, Jennifer L., Wu, Yujie, Kim, Byungchan, Sherman, Woody, Shelley, John C., Lin, Teng, Abel, Robert·2013·Journal of Chemical Theory and Computation
DOI 10.1021/ct300911a
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Abstract

Accurate and reliable calculation of protein-ligand binding affinities remains a hotbed of computer-aided drug design research. Despite the potentially large impact FEP (free energy perturbation) may have in drug design projects, practical applications of FEP in industrial contexts have been limited. In this work, we use a recently developed method, FEP/REST (free energy perturbation/replica exchange with solute tempering), to calculate the relative binding affinities for a set of congeneric ligands binding to the CDK2 receptor. We compare the FEP/REST results with traditional FEP/MD (molecular dynamics) results and MM/GBSA (molecular mechanics/Generalized Born Surface Area model) results and examine why FEP/REST performed notably better than these other methods, as well as why certain ligand mutations lead to large increases of the binding affinity while others do not. We also introduce a mathematical framework for assessing the consistency and reliability of the calculations using cycle closures in FEP mutation paths.

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Citations

Cites

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Cited by

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Metadata

Authors
Wang, L., Deng, Y., Knight, J. L., Wu, Y., Kim, B., Sherman, W., Shelley, J. C., Lin, T., & Abel, R.
Venue
Journal of Chemical Theory and Computation
Publisher
American Chemical Society (ACS)
Published
2013-01-30
Volume
9
Issue
2
Pages
1282-1293
ISSN
  • 1549-9618 (ISSN-L)
  • 1549-9626
DOI
10.1021/ct300911a
OpenAlex
W2323942455
Cited by
194
References
1
FWCI
14.87
Type
journal article
Language
en
Open access
closed

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.