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Cited by 135Computational Drug Discovery MethodsProtein Structure and DynamicsEnzyme Structure and Function

Molecular Docking Screens Using Comparative Models of Proteins

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Fan, Hao, Irwin, John J., Webb, Benjamin M., Klebe, Gerhard, Shoichet, Brian K., Sali, Andrej·2009·Journal of Chemical Information and Modeling
DOI 10.1021/ci9003706PMC PMC2790034
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Abstract

Two orders of magnitude more protein sequences can be modeled by comparative modeling than have been determined by X-ray crystallography and NMR spectroscopy. Investigators have nevertheless been cautious about using comparative models for ligand discovery because of concerns about model errors. We suggest how to exploit comparative models for molecular screens, based on docking against a wide range of crystallographic structures and comparative models with known ligands. To account for the variation in the ligand-binding pocket as it binds different ligands, we calculate "consensus" enrichment by ranking each library compound by its best docking score against all available comparative models and/or modeling templates. For the majority of the targets, the consensus enrichment for multiple models was better than or comparable to that of the holo and apo X-ray structures. Even for single models, the models are significantly more enriching than the template structure if the template is paralogous and shares more than 25% sequence identity with the target.

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  • Repeating ligand docking to multiple homology models based on different template structures greatly improves docking predictions.

    “It has been demonstrated that repeating ligand docking to multiple homology models based on different template structures greatly improves docking predictions40.”

    From Modeling the binding of diverse ligands within the Ah receptor ligand binding domain (2019) · cites this paper as [40]

    The Results section ('Ligand enrichment for multiple models'; page unavailable) reports that combining docking results from models based on different templates significantly improved ligand enrichment over the best-assessed single model. But the Conclusions section reports only modest gains in the proportion of targets performing at least as well as X-ray structures, so 'greatly improves' overstates the general result. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • The accuracy of binding geometries predicted by molecular docking to homology models strongly depends on the quality of the model, particularly in the binding site.

    “The accuracy of the binding geometries predicted by molecular docking to homology models strongly depends on the quality of the model, particularly in the binding site22,40,41.”

    From Modeling the binding of diverse ligands within the Ah receptor ligand binding domain (2019) · cites this paper as [40]

    The Results section, under “Accuracy of the docking geometry in modeled binding sites” (page unavailable), reports several relatively accurate predicted ligand poses. It also says the study did not focus on predicting complex conformations. The paper therefore supports the point that docking to comparative models can reproduce binding geometries, but does not establish that geometry accuracy strongly depends on model quality, particularly binding-site quality. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

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Authors
Fan, H., Irwin, J. J., Webb, B. M., Klebe, G., Shoichet, B. K., & Sali, A.
Affiliations
  • QB3 (US)
  • Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences, University of California at San Francisco, San Francisco, California 94158, Department of Pharmaceutical Chemistry and California Institute for Quantitative Biosciences, University of California at San Francisco, San Francisco, California 94158, and Institute of Pharmaceutical Chemistry, University of Marburg, Marburg, Germany
Corresponding author
Shoichet, Brian K.
ORCID
Venue
Journal of Chemical Information and Modeling
Publisher
American Chemical Society (ACS)
Published
2009-10-21
Volume
49
Issue
11
Pages
2512-2527
ISSN
  • 1549-9596 (ISSN-L)
  • 1549-960X
DOI
10.1021/ci9003706
PMC
PMC2790034
OpenAlex
W2061865181
Cited by
135
References
140
Citations by year
2026: 3 · 2025: 4 · 2024: 5 · 2023: 4 · 2022: 6 · 2021: 2 · 2020: 3 · 2019: 10 · 2018: 11 · 2017: 9 · 2016: 6 · 2015: 8 · 2014: 14 · 2013: 9 · 2012: 16
FWCI
12.90
Type
journal article
Language
en
Open access
Locations
  • Journal of Chemical Information and Modeling (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • Foundation for the National Institutes of Health
  • National Institute of General Medical Sciences (R01GM054762, P01GM071790, R01GM059957)
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
Keywords
comparative protein modeling, ligand discovery, model error, binding pocket, template structure, paralogous proteins, sequence identity, virtual screening
MeSH terms
Hydrogen Bonding, Ligands, Models, Molecular, Protein Conformation, Proteins, Crystallography, X-Ray, Nuclear Magnetic Resonance, Biomolecular

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