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Predicting Absolute Ligand Binding Free Energies to a Simple Model Site
Open accessAbstract
A central challenge in structure-based ligand design is the accurate prediction of binding free energies. Here we apply alchemical free energy calculations in explicit solvent to predict ligand binding in a model cavity in T4 lysozyme. Even in this simple site, there are challenges. We made systematic improvements, beginning with single poses from docking, then including multiple poses, additional protein conformational changes, and using an improved charge model. Computed absolute binding free energies had an RMS error of 1.9 kcal/mol relative to previously determined experimental values. In blind prospective tests, the methods correctly discriminated between several true ligands and decoys in a set of putative binders identified by docking. In these prospective tests, the RMS error in predicted binding free energies relative to those subsequently determined experimentally was only 0.6 kcal/mol. X-ray crystal structures of the new ligands bound in the cavity corresponded closely to predictions from the free energy calculations, but sometimes differed from those predicted by docking. Finally, we examined the impact of holding the protein rigid, as in docking, with a view to learning how approximations made in docking affect accuracy and how they may be improved.
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“More rigorous free energy methods such as alchemical binding free energy calculations have become established for the computation of binding free energies of small molecules to proteins with MD.55−60”
From Assessment of the Topology and Oligomerisation States of Coiled Coils Using Metadynamics with Conformational Restraints (2025) · cites this paper as [55]
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“Similar observations on the impact of the rigidreceptor approximation have also been made in the studies of the binding free enthalpies of small aromatic ligands to a binding site in the L99A mutant of T4 lysozyme where it has been shown that keeping the protein rigid while estimating free enthalpies of binding result…”
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BackgroundOptimizing Drug Design by Merging Generative AI With Active Learning Frameworks
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“The methodology was first applied in identifying new binders to a hydrophobic binding site in T4 lysozyme as model system, with good success in ranking binding affinities and in predicting the geometry of binding [135].”
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“The docking simulations were conducted with a maximum of 5 conformations per ligand and a root mean square deviation (RMSD) tolerance of 2.0 Å [52,53].”
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“The protein was parameterized with the AMBER parm96 forcefield [22] using LEaP from the AmberTools 11 [40] (with the force field chosen to be consistent with previous studies of this system) [1].”
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“∆∆GGbbbbbbbbbb = ∆GGvvbbvv(1) + ∆GGeeeeeeee(2) + ∆GGvvbbvv(3) (2)”
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“, 2005) or molecular dynamics (Mobley et al., 2007) also have been tested for MeBS prediction in holoproteins with missing ion coordinates.”
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“…system (13 ligands targeting the L99A mutant of T4 lysozyme) that performing energy minimization of ligand-bound proteins significantly improved the correlation between calculated absolute binding free energies and experimental values, increasing the correlation coefficient from -0.05 to 0.82 [52].”
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“set of diverse factors can affect receptor–ligand binding [2]: rotational, translational, conformational, and vibrational entropy of the ligand and receptor, solvation effects around hydrophobic or charged groups, conformational strain energies, changes in”
Background
Metadata
- Authors
- Mobley, D. L., Graves, A. P., Chodera, J. D., McReynolds, A. C., Shoichet, B. K., & Dill, K. A.
- Affiliations
- University of California, San Francisco (US)
- Corresponding author
- Shoichet, Brian K.
- Dill, Ken A.
- Venue
- Journal of Molecular Biology
- Publisher
- Elsevier BV
- Published
- 2007-08
- Volume
- 371
- Issue
- 4
- Pages
- 1118-1134
- ISSN
- 0022-2836 (ISSN-L)
- PMC
- PMC2104542
- OpenAlex
- W2128494768
- Cited by
- 310
- References
- 0
- Citations by year
- 2026: 7 · 2025: 9 · 2024: 19 · 2023: 8 · 2022: 21 · 2021: 11 · 2020: 14 · 2019: 10 · 2018: 20 · 2017: 12 · 2016: 21 · 2015: 12 · 2014: 12 · 2013: 19 · 2012: 24
- FWCI
- 23.38
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Molecular Biology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- Howard Hughes Medical Institute
- National Institutes of Health (GM63592)
- U.S. Air Force
- University of California, San Francisco
- National Institute of General Medical Sciences (R01GM063592, R56GM063592, R01GM034993, GM59957, R01GM059957, GM63592)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Keywords
- absolute binding free energy, alchemical free energy calculations, T4 lysozyme, structure-based ligand design, explicit solvent, protein conformational change, charge model, docking, decoy discrimination, protein-ligand binding
- MeSH terms
- Binding Sites, Computer Simulation, Ligands, Models, Molecular, Protein Binding, Proteins, Temperature, Titrimetry, Protein Structure, Tertiary, Crystallography, X-Ray, Hydrophobic and Hydrophilic Interactions
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