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Cited by 450

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Efficient docking of peptides to proteins without prior knowledge of the binding site

Open access
Hetényi, Csaba, van der Spoel, David·2002·Protein Science

Abstract

Reliability in docking of ligand molecules to proteins or other targets is an important challenge for molecular modeling. Applications of the docking technique include not only prediction of the binding mode of novel drugs, but also other problems like the study of protein‐protein interactions. Here we present a study on the reliability of the results obtained with the popular AutoDock program. We have performed systematical studies to test the ability of AutoDock to reproduce eight different protein/ligand complexes for which the structure was known, without prior knowledge of the binding site. More specifically, we look at factors influencing the accuracy of the final structure, such as the number of torsional degrees of freedom in the ligand. We conclude that the Autodock program package is able to select the correct complexes based on the energy without prior knowledge of the binding site. We named this application blind docking, as the docking algorithm is not able to “see” the binding site but can still find it. The success of blind docking represents an important finding in the era of structural genomics.

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Metadata

Authors
Hetényi, C., & van der Spoel, D.
Venue
Protein Science
Publisher
Wiley
Published
2002-07
Volume
11
Issue
7
Pages
1729-1737
ISSN
  • 0961-8368 (ISSN-L)
  • 1469-896X
OpenAlex
W2136513242
Cited by
450
References
2
FWCI
11.00
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Efficient docking of peptides to proteins without prior knowledge of the binding site. https://app.cochat.ai/papers/10.1110/ps.0202302
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Hetényi, C., & van der Spoel, D. (2002). Efficient docking of peptides to proteins without prior knowledge of the binding site. Protein Science. https://doi.org/10.1110/ps.0202302

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.