Literature DB >> 25900849

Docking small peptides remains a great challenge: an assessment using AutoDock Vina.

Robert Rentzsch, Bernhard Y Renard.   

Abstract

There is a growing interest in the mechanisms and the prediction of how flexible peptides bind proteins, often in a highly selective and conserved manner. While both existing small-molecule docking methods and custom protocols can be used, even short peptides make difficult targets owing to their high torsional flexibility. Any benchmarking should therefore start with those. We compiled a meta-data set of 47 complexes with peptides up to five residues, based on 11 related studies from the past decade. Although their highly varying strategies and constraints preclude direct, quantitative comparisons, we still provide a comprehensive overview of the reported results, using a simple yet stringent measure: the quality of the top-scoring peptide pose. Using the entire data set, this is augmented by our own benchmark of AutoDock Vina, a freely available, fast and widely used docking tool. It particularly addresses non-expert users and was therefore implemented in a highly integrated manner. Guidelines addressing important issues such as the amount of sampling required for result reproducibility are so far lacking. Using peptide docking as an example, this is the first study to address these issues in detail. Finally, to encourage further, standardized benchmarking efforts, the compiled data set is made available in an accessible, transparent and extendable manner.
© The Author 2015. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Keywords:  AutoDock Vina; benchmark; docking; peptides; protein–peptide docking

Mesh:

Substances:

Year:  2015        PMID: 25900849     DOI: 10.1093/bib/bbv008

Source DB:  PubMed          Journal:  Brief Bioinform        ISSN: 1467-5463            Impact factor:   11.622


  31 in total

1.  Systematic Testing of Belief-Propagation Estimates for Absolute Free Energies in Atomistic Peptides and Proteins.

Authors:  Rory M Donovan-Maiye; Christopher J Langmead; Daniel M Zuckerman
Journal:  J Chem Theory Comput       Date:  2017-12-22       Impact factor: 6.006

2.  Extensive benchmark of rDock as a peptide-protein docking tool.

Authors:  Daniel Soler; Yvonne Westermaier; Robert Soliva
Journal:  J Comput Aided Mol Des       Date:  2019-07-03       Impact factor: 3.686

3.  AutoDock CrankPep: combining folding and docking to predict protein-peptide complexes.

Authors:  Yuqi Zhang; Michel F Sanner
Journal:  Bioinformatics       Date:  2019-12-15       Impact factor: 6.937

4.  Fully Blind Docking at the Atomic Level for Protein-Peptide Complex Structure Prediction.

Authors:  Chengfei Yan; Xianjin Xu; Xiaoqin Zou
Journal:  Structure       Date:  2016-09-15       Impact factor: 5.006

5.  DINC 2.0: A New Protein-Peptide Docking Webserver Using an Incremental Approach.

Authors:  Dinler A Antunes; Mark Moll; Didier Devaurs; Kyle R Jackson; Gregory Lizée; Lydia E Kavraki
Journal:  Cancer Res       Date:  2017-11-01       Impact factor: 12.701

6.  Interfacial Binding Sites for Cholesterol on G Protein-Coupled Receptors.

Authors:  Anthony G Lee
Journal:  Biophys J       Date:  2019-04-02       Impact factor: 4.033

7.  Protein-peptide docking using CABS-dock and contact information.

Authors:  Maciej Blaszczyk; Maciej Pawel Ciemny; Andrzej Kolinski; Mateusz Kurcinski; Sebastian Kmiecik
Journal:  Brief Bioinform       Date:  2019-11-27       Impact factor: 11.622

8.  The AutoDock suite at 30.

Authors:  David S Goodsell; Michel F Sanner; Arthur J Olson; Stefano Forli
Journal:  Protein Sci       Date:  2020-09-12       Impact factor: 6.725

9.  Modeling the binding of protoporphyrin IX, verteporfin, and chlorin e6 to SARS-CoV-2 proteins.

Authors:  Oskar I Koifman; Natalia Sh Lebedeva; Yury A Gubarev; Mikhail O Koifman
Journal:  Chem Heterocycl Compd (N Y)       Date:  2021-05-14       Impact factor: 1.277

10.  Machine learning assessment of the binding region as a tool for more efficient computational receptor-ligand docking.

Authors:  Matjaž Simončič; Miha Lukšič; Maksym Druchok
Journal:  J Mol Liq       Date:  2022-02-18       Impact factor: 6.165

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