4.7 Article

Prediction of Protein-Ligand Binding Pose and Affinity Using the gREST plus FEP Method

Journal

JOURNAL OF CHEMICAL INFORMATION AND MODELING
Volume 60, Issue 11, Pages 5382-5394

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/acs.jcim.0c00338

Keywords

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Funding

  1. HPCI System Research Project [hp150270, hp160207, hp170254, hp180201, hp180274, hp190181, hp170115]
  2. MEXT [26119006]
  3. MEXT/JSPS KAKENHI [26220807, 19H05645, 19K12229]
  4. RIKEN pioneering project in Dynamic Structural Biology
  5. MEXT
  6. RIKEN pioneering project in Glycolipid logue
  7. Grants-in-Aid for Scientific Research [19H05645, 19K12229, 26220807] Funding Source: KAKEN

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The accurate prediction of protein-ligand binding affinity is a central challenge in computational chemistry and in-silico drug discovery. The free energy perturbation (FEP) method based on molecular dynamics (MD) simulation provides reasonably accurate results only if a reliable structure is available via high-resolution X-ray crystallography. To overcome the limitation, we propose a sequential prediction protocol using generalized replica exchange with solute tempering (gREST) and FEP. At first, ligand binding poses are predicted using gREST, which weakens protein-ligand interactions at high temperatures to sample multiple binding poses. To avoid ligand dissociation at high temperatures, a flat-bottom restraint potential centered on the binding site is applied in the simulation. The binding affinity of the most reliable pose is then calculated using FEP. The protocol is applied to the bindings of ten ligands to FK506 binding proteins (FKBP), showing the excellent agreement between the calculated and experimental binding affinities. The present protocol, which is referred to as the gREST+FEP method, would help to predict the binding affinities without high-resolution structural information on the ligand-bound state.

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