Cited by 1,308Computational Drug Discovery MethodsProtein Structure and Dynamicsvaccines and immunoinformatics approaches

Improved Docking of Polypeptides with Glide

Tubert-Brohman, Ivan, Sherman, Woody, Repasky, Matt, Beuming, Thijs·2013·Journal of Chemical Information and Modeling

Abstract

Predicting the binding mode of flexible polypeptides to proteins is an important task that falls outside the domain of applicability of most small molecule and protein-protein docking tools. Here, we test the small molecule flexible ligand docking program Glide on a set of 19 non-α-helical peptides and systematically improve pose prediction accuracy by enhancing Glide sampling for flexible polypeptides. In addition, scoring of the poses was improved by post-processing with physics-based implicit solvent MM-GBSA calculations. Using the best RMSD among the top 10 scoring poses as a metric, the success rate (RMSD ≤ 2.0 Å for the interface backbone atoms) increased from 21% with default Glide SP settings to 58% with the enhanced peptide sampling and scoring protocol in the case of redocking to the native protein structure. This approaches the accuracy of the recently developed Rosetta FlexPepDock method (63% success for these 19 peptides) while being over 100 times faster. Cross-docking was performed for a subset of cases where an unbound receptor structure was available, and in that case, 40% of peptides were docked successfully. We analyze the results and find that the optimized polypeptide protocol is most accurate for extended peptides of limited size and number of formal charges, defining a domain of applicability for this approach.

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Crossref · 10.1021/ci400128m

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  • Software programs like AutoDock, GOLD, and Glide use docking simulations to predict binding of algal peptides to target protein receptors, providing a three-dimensional model of peptide-ligand interactions, scoring binding affinity, and identifying the most thermodynamically favorable conformation.

    “Software programs like AutoDock, GOLD, and Glide use docking simulations to predict the binding of algal peptides to target protein receptors. They provide a three-dimensional model of peptide-ligand interactions, scoring the binding affinity and identifying the most thermodynamically favorable conformation [212,213].”

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Authors
Tubert-Brohman, I., Sherman, W., Repasky, M., & Beuming, T.
Affiliations
  • Schrödinger, Inc., 120 West 45th street, New York, New York 10036, United States
  • Schrodinger (United States) (US)
Corresponding author
Beuming, Thijs
Venue
Journal of Chemical Information and Modeling
Published
2013-07-10
Volume
53
Issue
7
Pages
1689-1699
ISSN
  • 1549-9596 (ISSN-L)
  • 1549-960X
OpenAlex
W1972940559
Cited by
1,308
References
33
Citations by year
2026: 21 · 2025: 25 · 2024: 56 · 2023: 79 · 2022: 81 · 2021: 372 · 2020: 222 · 2019: 123 · 2018: 144 · 2017: 125 · 2016: 46 · 2015: 13 · 2014: 1
FWCI
22.23
Type
journal article
Language
en
Open access
closed
Locations
  • Journal of Chemical Information and Modeling (publishedVersion)
  • PubMed (publishedVersion)
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
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
Keywords
glides, protein-peptide interactions, flexible ligand docking, cross-docking, domain of applicability, formal charge
MeSH terms
Algorithms, Amino Acid Sequence, Binding Sites, Peptides, Protein Conformation, Software, Surface Properties, Time Factors, Molecular Docking Simulation, Databases, Pharmaceutical

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