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Cited by 2,246Computational Drug Discovery MethodsAdvanced Chemical Physics StudiesProtein Structure and Dynamics

A semiempirical free energy force field with charge‐based desolvation

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Huey, Ruth, Morris, Garrett M., Olson, Arthur J., Goodsell, David S.·2007·Journal of Computational Chemistry
DOI 10.1002/jcc.20634
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Abstract

The authors describe the development and testing of a semiempirical free energy force field for use in AutoDock4 and similar grid‐based docking methods. The force field is based on a comprehensive thermodynamic model that allows incorporation of intramolecular energies into the predicted free energy of binding. It also incorporates a charge‐based method for evaluation of desolvation designed to use a typical set of atom types. The method has been calibrated on a set of 188 diverse protein–ligand complexes of known structure and binding energy, and tested on a set of 100 complexes of ligands with retroviral proteases. The force field shows improvement in redocking simulations over the previous AutoDock3 force field. © 2007 Wiley Periodicals, Inc. J Comput Chem, 2007

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  • The standard AutoDock force field computes free energy of binding as a weighted sum of van der Waals, hydrogen bond, Coulomb electrostatic, desolvation, and ligand torsional entropy terms, with weights estimated by linear regression.

    “The standard force field includes the following terms (eq 1):371where the free energy of binding (FEB) is calculated as a sum of van der Waals (vdW), hydrogen bond (Hbond), Coulomb electrostatic (Elec), desolvation (desolv) and ligand torsional entropy (torsDoF); each term is weighted by a specific value (Wterm) estimated using a linear regression model.37”

    From AutoDock4<sub>Zn</sub>: An Improved AutoDock Force Field for Small-Molecule Docking to Zinc Metalloproteins (2014) · cites this paper as [37]

    The Overview (page unavailable) describes van der Waals-like dispersion/repulsion, hydrogen-bond, electrostatic and desolvation terms, plus a weighted torsional-entropy term. It says the weights are optimized against experimentally characterized complexes, but our copy does not specify linear regression as the estimation method. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

  • In the Gasteiger charge model used by AutoDock, oxygen atoms receive more negative charges than nitrogen and sulfur, making oxygen the preferred chelator of positively charged metals.

    “Also, in the Gasteiger38 charge model used in AutoDock,37 oxygen atoms are systematically assigned a more negative charge than nitrogen and sulfur, thus resulting in the preferred candidates for chelating positively charged metal.”

    From AutoDock4<sub>Zn</sub>: An Improved AutoDock Force Field for Small-Molecule Docking to Zinc Metalloproteins (2014) · cites this paper as [37]

    The Coordinate Sets section says the authors assigned atomic charges using the Gasteiger PEOE method and processed ligands with AutoDock tools. The Magnitude of Terms section says metal-ion electrostatic interactions can be strongly favorable. Neither section says Gasteiger systematically assigns oxygen a more negative charge than nitrogen or sulfur, or that this makes oxygen the preferred metal chelator; page numbers are unavailable in our copy. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • The standard AutoDock force field describes ligand-metal interactions mainly through van der Waals and Coulomb electrostatic terms, and to a smaller extent through a desolvation term.

    “Similar to all other pairwise interactions in the force field, the interaction between ligand atoms and metals contained in the receptor is described mainly by van der Waals (ΔHvdW) and Coulomb electrostatic (ΔHelec) terms and, to a smaller degree, by the desolvation term (ΔGdesolv).”

    From AutoDock4<sub>Zn</sub>: An Improved AutoDock Force Field for Small-Molecule Docking to Zinc Metalloproteins (2014) · cites this paper as [37]

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Research works printed but not linked: 21

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Authors
Huey, R., Morris, G. M., Olson, A. J., & Goodsell, D. S.
Affiliations
Scripps Research Institute (US)
Corresponding author
Goodsell, David S.
ORCID
Venue
Journal of Computational Chemistry
Publisher
Wiley
Published
2007-02
Volume
28
Issue
6
Pages
1145-1152
ISSN
  • 0192-8651 (ISSN-L)
  • 1096-987X
DOI
10.1002/jcc.20634
OpenAlex
W2165526004
Cited by
2,246
References
21
Citations by year
2026: 80 · 2025: 105 · 2024: 115 · 2023: 110 · 2022: 116 · 2021: 114 · 2020: 127 · 2019: 132 · 2018: 112 · 2017: 126 · 2016: 121 · 2015: 108 · 2014: 116 · 2013: 135 · 2012: 151
FWCI
130.12
Type
journal article
Language
en
Open access
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations
  • Journal of Computational Chemistry (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • National Institutes of Health
  • National Institute of General Medical Sciences (P01GM048870, P01 GM48870, R01GM069832)
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
Keywords
protein-ligand docking, desolvation, binding free energy, retroviral protease, redocking, force field calibration
MeSH terms
Algorithms, Hydrogen Bonding, Ligands, Mathematical Computing, Models, Chemical, Models, Molecular, Molecular Conformation, Protein Binding, Proteins, Software, Thermodynamics, Water, Drug Design, HIV Protease, HIV Protease Inhibitors, Databases, Protein, Static Electricity

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