LEADS-PEP: A Benchmark Data Set for Assessment of Peptide Docking Performance

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LEADS-PEP : A Benchmark Data Set for Assessment of Peptide Docking Performance. / Hauser, Alexander Sebastian; Windshügel, Björn.

In: Journal of Chemical Information and Modeling, Vol. 56, No. 1, 11.01.2016, p. 188-200.

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Hauser, AS & Windshügel, B 2016, 'LEADS-PEP: A Benchmark Data Set for Assessment of Peptide Docking Performance' Journal of Chemical Information and Modeling, vol. 56, no. 1, pp. 188-200. https://doi.org/10.1021/acs.jcim.5b00234

APA

Hauser, A. S., & Windshügel, B. (2016). LEADS-PEP: A Benchmark Data Set for Assessment of Peptide Docking Performance. Journal of Chemical Information and Modeling, 56(1), 188-200. https://doi.org/10.1021/acs.jcim.5b00234

Vancouver

Hauser AS, Windshügel B. LEADS-PEP: A Benchmark Data Set for Assessment of Peptide Docking Performance. Journal of Chemical Information and Modeling. 2016 Jan 11;56(1):188-200. https://doi.org/10.1021/acs.jcim.5b00234

Author

Hauser, Alexander Sebastian ; Windshügel, Björn. / LEADS-PEP : A Benchmark Data Set for Assessment of Peptide Docking Performance. In: Journal of Chemical Information and Modeling. 2016 ; Vol. 56, No. 1. pp. 188-200.

Bibtex

@article{41f71d5108f64473b7c40df7846e971c,
title = "LEADS-PEP: A Benchmark Data Set for Assessment of Peptide Docking Performance",
abstract = "With increasing interest in peptide-based therapeutics also the application of computational approaches such as peptide docking has gained more and more attention. In order to assess the suitability of docking programs for peptide placement and to support the development of peptide-specific docking tools, an independently constructed benchmark data set is urgently needed. Here we present the LEADS-PEP benchmark data set for assessing peptide docking performance. Using a rational and unbiased workflow, 53 protein-peptide complexes with peptide lengths ranging from 3 to 12 residues were selected. The data set is publicly accessible at www.leads-x.org . In a second step we evaluated several small molecule docking programs for their potential to reproduce peptide conformations as present in LEADS-PEP. While most tested programs were capable to generate native-like binding modes of small peptides, only Surflex-Dock and AutoDock Vina performed reasonably well for peptides consisting of more than five residues. Rescoring of docking poses with scoring functions ChemPLP, ChemScore, and ASP further increased the number of top-ranked near-native conformations. Our results suggest that small molecule docking programs are a good and fast alternative to specialized peptide docking programs.",
author = "Hauser, {Alexander Sebastian} and Bj{\"o}rn Windsh{\"u}gel",
year = "2016",
month = "1",
day = "11",
doi = "10.1021/acs.jcim.5b00234",
language = "English",
volume = "56",
pages = "188--200",
journal = "Journal of Chemical Information and Modeling",
issn = "1549-9596",
publisher = "American Chemical Society",
number = "1",

}

RIS

TY - JOUR

T1 - LEADS-PEP

T2 - A Benchmark Data Set for Assessment of Peptide Docking Performance

AU - Hauser, Alexander Sebastian

AU - Windshügel, Björn

PY - 2016/1/11

Y1 - 2016/1/11

N2 - With increasing interest in peptide-based therapeutics also the application of computational approaches such as peptide docking has gained more and more attention. In order to assess the suitability of docking programs for peptide placement and to support the development of peptide-specific docking tools, an independently constructed benchmark data set is urgently needed. Here we present the LEADS-PEP benchmark data set for assessing peptide docking performance. Using a rational and unbiased workflow, 53 protein-peptide complexes with peptide lengths ranging from 3 to 12 residues were selected. The data set is publicly accessible at www.leads-x.org . In a second step we evaluated several small molecule docking programs for their potential to reproduce peptide conformations as present in LEADS-PEP. While most tested programs were capable to generate native-like binding modes of small peptides, only Surflex-Dock and AutoDock Vina performed reasonably well for peptides consisting of more than five residues. Rescoring of docking poses with scoring functions ChemPLP, ChemScore, and ASP further increased the number of top-ranked near-native conformations. Our results suggest that small molecule docking programs are a good and fast alternative to specialized peptide docking programs.

AB - With increasing interest in peptide-based therapeutics also the application of computational approaches such as peptide docking has gained more and more attention. In order to assess the suitability of docking programs for peptide placement and to support the development of peptide-specific docking tools, an independently constructed benchmark data set is urgently needed. Here we present the LEADS-PEP benchmark data set for assessing peptide docking performance. Using a rational and unbiased workflow, 53 protein-peptide complexes with peptide lengths ranging from 3 to 12 residues were selected. The data set is publicly accessible at www.leads-x.org . In a second step we evaluated several small molecule docking programs for their potential to reproduce peptide conformations as present in LEADS-PEP. While most tested programs were capable to generate native-like binding modes of small peptides, only Surflex-Dock and AutoDock Vina performed reasonably well for peptides consisting of more than five residues. Rescoring of docking poses with scoring functions ChemPLP, ChemScore, and ASP further increased the number of top-ranked near-native conformations. Our results suggest that small molecule docking programs are a good and fast alternative to specialized peptide docking programs.

U2 - 10.1021/acs.jcim.5b00234

DO - 10.1021/acs.jcim.5b00234

M3 - Journal article

VL - 56

SP - 188

EP - 200

JO - Journal of Chemical Information and Modeling

JF - Journal of Chemical Information and Modeling

SN - 1549-9596

IS - 1

ER -

ID: 153606851