Improved Docking of Polypeptides with Glide
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.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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].”
From Navigating the potential of algal peptides: health effects, market applications, and scientific challenges (2026) · cites this paper as [213]
Cites
Jorgensen, W. L., Maxwell, D. S., & Tirado-Rives, J. (1996) · Journal of the American Chemical Society · doi:10.1021/ja9621760 · Cited by 14,078 · see citing works
Friesner, R. A., Banks, J. L., Murphy, R. B., Halgren, T. A., Klicic, J. J., Mainz, D. T., Repasky, M. P., Knoll, E. H., Shelley, M., Perry, J. K., et al. (2004) · Journal of Medicinal Chemistry · doi:10.1021/jm0306430 · Cited by 10,057 · see citing works
Self-citationHalgren, T. A., Murphy, R. B., Friesner, R. A., Beard, H. S., Frye, L. L., Pollard, W. T., & Banks, J. L. (2004) · Journal of Medicinal Chemistry · doi:10.1021/jm030644s · Cited by 5,280 · see citing works
Jorgensen, W. L., & Tirado‐Rives, J. (1988) · Journal of the American Chemical Society · doi:10.1021/ja00214a001 · Cited by 5,018 · see citing works
Announcing the worldwide Protein Data Bank
Berman, H., Henrick, K., & Nakamura, H. (2003) · Nature Structural & Molecular Biology · doi:10.1038/nsb1203-980 · Cited by 3,093 · see citing works
Novel procedure for modeling ligand/receptor induced fit effects.
Sherman, W., Day, T., Jacobson, M., Friesner, R., & Farid, R. (2006) · Journal of Medicinal Chemistry · doi:10.1021/jm050540c · Cited by 1,998 · see citing works
Self-citationReaching for high-hanging fruit in drug discovery at protein–protein interfaces
Wells, J. A., & McClendon, C. L. (2007) · Nature · doi:10.1038/nature06526 · Cited by 1,963 · see citing works
Shivakumar, D., Williams, J., Wu, Y., Damm, W., Shelley, J. C., & Sherman, W. (2010) · Journal of Chemical Theory and Computation · doi:10.1021/ct900587b · Cited by 1,945 · see citing works
Self-citationIdentifying and Characterizing Binding Sites and Assessing Druggability
Halgren, T. A. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci800324m · Cited by 1,771 · see citing works
Synthetic therapeutic peptides: science and market
Vlieghe, P., Lisowski, V., Martínez, J., & Khrestchatisky, M. (2009) · Drug Discovery Today · doi:10.1016/j.drudis.2009.10.009 · Cited by 1,487 · see citing works
The VSGB 2.0 model: A next generation energy model for high resolution protein structure modeling
Li, J., Abel, R., Zhu, K., Cao, Y., Zhao, S., & Friesner, R. A. (2011) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.23106 · Cited by 1,181 · see citing works
New Method for Fast and Accurate Binding‐site Identification and Analysis
Halgren, T. (2007) · Chemical Biology & Drug Design · doi:10.1111/j.1747-0285.2007.00483.x · Cited by 880 · see citing works
Thévenet, P., Shen, Y., Maupetit, J., Guyon, F., Derreumaux, P., & Tufféry, P. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks419 · Cited by 639 · see citing works
A detailed comparison of current docking and scoring methods on systems of pharmaceutical relevance
Perola, E., Walters, W. P., & Charifson, P. S. (2004) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.20088 · Cited by 446 · see citing works
ConfGen: A Conformational Search Method for Efficient Generation of Bioactive Conformers
Watts, K. S., Dalal, P., Murphy, R. B., et al. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci100015j · Cited by 442 · see citing works
Sub-angstrom modeling of complexes between flexible peptides and globular proteins
Raveh, B., London, N., & Schueler‐Furman, O. (2010) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.22716 · Cited by 423 · see citing works
The Structural Basis of Peptide-Protein Binding Strategies
London, N., Movshovitz‐Attias, D., & Schueler‐Furman, O. (2010) · Structure · doi:10.1016/j.str.2009.11.012 · Cited by 417 · see citing works
Discovering and improving novel peptide therapeutics
MCGREGOR, D. (2008) · Current Opinion in Pharmacology · doi:10.1016/j.coph.2008.06.002 · Cited by 332 · see citing works
Labute, P. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci900508k · Cited by 328 · see citing works
Raveh, B., London, N., Zimmerman, L., & Schueler‐Furman, O. (2011) · PLoS ONE · doi:10.1371/journal.pone.0018934 · Cited by 323 · see citing works
Chen, I., & Foloppe, N. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci100026x · Cited by 208 · see citing works
PEPstr: A de novo Method for Tertiary Structure Prediction of Small Bioactive Peptides
Kaur, H., Garg, A., & Raghava, G. P. S. (2007) · Protein and Peptide Letters · doi:10.2174/092986607781483859 · Cited by 206 · see citing works
A Unified Conformational Selection and Induced Fit Approach to Protein-Peptide Docking
Trellet, M. E., Melquiond, A. S. J., & Bonvin, A. M. J. J. (2013) · PLoS ONE · doi:10.1371/journal.pone.0058769 · Cited by 200 · see citing works
Andersen, J. T., Pehrson, R., Максимилианович, Т. В., Daba, M. B., Abrahmsén, L., & Ekblad, C. (2010) · Journal of Biological Chemistry · doi:10.1074/jbc.m110.164848 · Cited by 177 · see citing works
Receptor-Fc fusion therapeutics, traps, and MIMETIBODY™ technology
Huang, C. (2009) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2009.10.010 · Cited by 167 · see citing works
Antes, I. (2009) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.22629 · Cited by 166 · see citing works
Zhou, Z., Felts, A. K., Friesner, R. A., & Levy, R. M. (2007) · Journal of Chemical Information and Modeling · doi:10.1021/ci7000346 · Cited by 165 · see citing works
Donsky, E., & Wolfson, H. J. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr498 · Cited by 102 · see citing works
Ab initio prediction of peptide‐MHC binding geometry for diverse class I MHC allotypes
Bordner, A. J., & Abagyan, R. A. (2006) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.20831 · Cited by 76 · see citing works
Niv, M. Y., & Weinstein, H. (2005) · Journal of the American Chemical Society · doi:10.1021/ja054195s · Cited by 70 · see citing works
Numerical Errors and Chaotic Behavior in Docking Simulations
Fehér, M., & Williams, C. I. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci200598m · Cited by 47 · see citing works
Peptide-Based Anticancer Vaccines: Recent Advances and Future Perspectives
Mocellin, S., Pilati, P., & Nitti, D. (2009) · Current Medicinal Chemistry · doi:10.2174/092986709789909648 · Cited by 43 · see citing works
All-Atom Monte Carlo Approach to Protein–Peptide Binding
Staneva, I., & Wallin, S. (2009) · Journal of Molecular Biology · doi:10.1016/j.jmb.2009.08.063 · Cited by 20 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Macromolecular modeling and design in Rosetta: recent methods and frameworks
Leman, J. K., Weitzner, B. D., Lewis, S. M., Adolf-Bryfogle, J., Alam, N., Alford, R. F., Aprahamian, M., Baker, D., Barlow, K. A., Barth, P., et al. (2020) · Nature Methods · doi:10.1038/s41592-020-0848-2 · Cited by 888 · see citing works
Ganesh, K. S., Sridhar, A., & Vishali, S. (2022) · Chemosphere · doi:10.1016/j.chemosphere.2021.132221 · Cited by 278 · see citing works
Hammerl, D., Martens, J. W. M., Timmermans, M., Smid, M., Trapman-Jansen, A. M., Foekens, R., Isaeva, O. I., Voorwerk, L., Balcioglu, H. E., Wijers, R., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-25962-0 · Cited by 262 · see citing works
Recent advances in colon drug delivery systems
Arévalo-Pérez, R., Maderuelo, C., & Lanao, J. M. (2020) · Journal of Controlled Release · doi:10.1016/j.jconrel.2020.09.026 · Cited by 179 · see citing works
Lactate as a Signaling Molecule That Regulates Exercise-Induced Adaptations
Nalbandian, M., & Takeda, M. (2016) · Biology · doi:10.3390/biology5040038 · Cited by 174 · see citing works
Cusenza, M. A., Guarino, F., Longo, S., Ferraro, M., & Cellura, M. (2019) · Journal of Energy Storage · doi:10.1016/j.est.2019.100845 · Cited by 150 · see citing works
Karpagam, R., Jawaharraj, K., & Gnanam, R. (2021) · Science of The Total Environment · doi:10.1016/j.scitotenv.2020.144236 · Cited by 146 · see citing works
Explaining the (non-)adoption of advanced data analytics in auditing: A process theory
Krieger, F., Drews, P., & Velte, P. (2021) · International Journal of Accounting Information Systems · doi:10.1016/j.accinf.2021.100511 · Cited by 133 · see citing works
Does Observability Affect Prosociality?
Bradley, A., Lawrence, C., & Ferguson, E. (2018) · Proceedings of the Royal Society B: Biological Sciences · doi:10.1098/rspb.2018.0116 · Cited by 122 · see citing works
Emerging radiative materials and prospective applications of radiative sky cooling - A review
Farooq, A. S., Zhang, P., Gao, Y., & Gulfam, R. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.110910 · Cited by 106 · see citing works
Solbrig, L., Jones, R., Kavanagh, D., May, J., Parkin, T., & Andrade, J. (2017) · Internet Interventions · doi:10.1016/j.invent.2016.12.003 · Cited by 86 · see citing works
A cross-validated cytoarchitectonic atlas of the human ventral visual stream
Rosenke, M., Weiner, K. S., Barnett, M. A., Zilles, K., Amunts, K., Goebel, R., & Grill-Spector, K. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2017.02.040 · Cited by 97 · see citing works
Understanding Service Usage and Needs for Adults with ASD: The Importance of Living Situation
Dudley, K. M., Klinger, M. R., Meyer, A., Powell, P., & Klinger, L. G. (2019) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-018-3729-0 · Cited by 92 · see citing works
Dubois-Comtois, K., Suffren, S., St-Laurent, D., Milot, T., & Lemelin, J.-P. (2021) · Journal of Developmental & Behavioral Pediatrics · doi:10.1097/dbp.0000000000000935 · Cited by 36 · see citing works
Kim, H. B., Iwamatsu, T., Nishio, K.-I., Komatsu, H., Mukai, T., Odate, Y., & Sasaki, M. (2020) · Energy and Buildings · doi:10.1016/j.enbuild.2020.110151 · Cited by 22 · see citing works
Evolution of Cooperation with Heterogeneous Conditional Cooperators
Battu, B., Pammi, V. S. C., & Srinivasan, N. (2018) · Scientific Reports · doi:10.1038/s41598-018-22593-2 · Cited by 11 · see citing works
Khaliq, M., Noor, Z., Moazzam, M., Saeed, F., Tul-Zohra, K., Tariq, E., Muhammad Shahbaz, H., & Khaliq, K. (2026) · Annals of Medicine · doi:10.1080/07853890.2026.2637282 · Cited by 4 · see citing works
“They provide a three-dimensional model of peptide-ligand interactions, scoring the binding affinity and identifying the most thermodynamically favorable conformation [212,213].”
- 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
- Publisher
- American Chemical Society (ACS)
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review