Literature DB >> 31721338

Cross-docking benchmark for automated pose and ranking prediction of ligand binding.

Shayne D Wierbowski1, Bentley M Wingert2, Jim Zheng3, Carlos J Camacho2.   

Abstract

Significant efforts have been devoted in the last decade to improving molecular docking techniques to predict both accurate binding poses and ranking affinities. Some shortcomings in the field are the limited number of standard methods for measuring docking success and the availability of widely accepted standard data sets for use as benchmarks in comparing different docking algorithms throughout the field. In order to address these issues, we have created a Cross-Docking Benchmark server. The server is a versatile cross-docking data set containing 4,399 protein-ligand complexes across 95 protein targets intended to serve as benchmark set and gold standard for state-of-the-art pose and ranking prediction in easy, medium, hard, or very hard docking targets. The benchmark along with a customizable cross-docking data set generation tool is available at http://disco.csb.pitt.edu. We further demonstrate the potential uses of the server in questions outside of basic benchmarking such as the selection of the ideal docking reference structure.
© 2019 The Protein Society.

Keywords:  affinity ranking; cross-docking; docking; drug discovery; pose prediction; small molecule; virtual screening

Mesh:

Substances:

Year:  2019        PMID: 31721338      PMCID: PMC6933848          DOI: 10.1002/pro.3784

Source DB:  PubMed          Journal:  Protein Sci        ISSN: 0961-8368            Impact factor:   6.725


  25 in total

1.  The Protein Data Bank.

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Journal:  Nucleic Acids Res       Date:  2000-01-01       Impact factor: 16.971

2.  Binding MOAD (Mother Of All Databases).

Authors:  Liegi Hu; Mark L Benson; Richard D Smith; Michael G Lerner; Heather A Carlson
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3.  Assessment of programs for ligand binding affinity prediction.

Authors:  Ryangguk Kim; Jeffrey Skolnick
Journal:  J Comput Chem       Date:  2008-06       Impact factor: 3.376

4.  Protein-ligand docking against non-native protein conformers.

Authors:  Marcel L Verdonk; Paul N Mortenson; Richard J Hall; Michael J Hartshorn; Christopher W Murray
Journal:  J Chem Inf Model       Date:  2008-11       Impact factor: 4.956

5.  D3R Grand Challenge 2: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies.

Authors:  Zied Gaieb; Shuai Liu; Symon Gathiaka; Michael Chiu; Huanwang Yang; Chenghua Shao; Victoria A Feher; W Patrick Walters; Bernd Kuhn; Markus G Rudolph; Stephen K Burley; Michael K Gilson; Rommie E Amaro
Journal:  J Comput Aided Mol Des       Date:  2017-12-04       Impact factor: 3.686

6.  Optimal affinity ranking for automated virtual screening validated in prospective D3R grand challenges.

Authors:  Bentley M Wingert; Rick Oerlemans; Carlos J Camacho
Journal:  J Comput Aided Mol Des       Date:  2017-09-16       Impact factor: 3.686

Review 7.  Improving small molecule virtual screening strategies for the next generation of therapeutics.

Authors:  Bentley M Wingert; Carlos J Camacho
Journal:  Curr Opin Chem Biol       Date:  2018-06-17       Impact factor: 8.822

8.  Lessons learned in empirical scoring with smina from the CSAR 2011 benchmarking exercise.

Authors:  David Ryan Koes; Matthew P Baumgartner; Carlos J Camacho
Journal:  J Chem Inf Model       Date:  2013-02-12       Impact factor: 4.956

9.  Open Babel: An open chemical toolbox.

Authors:  Noel M O'Boyle; Michael Banck; Craig A James; Chris Morley; Tim Vandermeersch; Geoffrey R Hutchison
Journal:  J Cheminform       Date:  2011-10-07       Impact factor: 5.514

10.  CSAR data set release 2012: ligands, affinities, complexes, and docking decoys.

Authors:  James B Dunbar; Richard D Smith; Kelly L Damm-Ganamet; Aqeel Ahmed; Emilio Xavier Esposito; James Delproposto; Krishnapriya Chinnaswamy; You-Na Kang; Ginger Kubish; Jason E Gestwicki; Jeanne A Stuckey; Heather A Carlson
Journal:  J Chem Inf Model       Date:  2013-05-10       Impact factor: 4.956

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  11 in total

1.  Cross-docking benchmark for automated pose and ranking prediction of ligand binding.

Authors:  Shayne D Wierbowski; Bentley M Wingert; Jim Zheng; Carlos J Camacho
Journal:  Protein Sci       Date:  2019-11-28       Impact factor: 6.725

2.  Improving protein-ligand docking and screening accuracies by incorporating a scoring function correction term.

Authors:  Liangzhen Zheng; Jintao Meng; Kai Jiang; Haidong Lan; Zechen Wang; Mingzhi Lin; Weifeng Li; Hongwei Guo; Yanjie Wei; Yuguang Mu
Journal:  Brief Bioinform       Date:  2022-05-13       Impact factor: 13.994

3.  Discovery of inhibitors targeting protein tyrosine phosphatase 1B using a combined virtual screening approach.

Authors:  Dan Zhao; Lu Sun; Shijun Zhong
Journal:  Mol Divers       Date:  2021-10-16       Impact factor: 3.364

4.  Three-Dimensional Convolutional Neural Networks and a Cross-Docked Data Set for Structure-Based Drug Design.

Authors:  Paul G Francoeur; Tomohide Masuda; Jocelyn Sunseri; Andrew Jia; Richard B Iovanisci; Ian Snyder; David R Koes
Journal:  J Chem Inf Model       Date:  2020-09-10       Impact factor: 4.956

5.  Druggable hot spots in trypanothione reductase: novel insights and opportunities for drug discovery revealed by DRUGpy.

Authors:  Olivia Teixeira; Pedro Lacerda; Thamires Quadros Froes; Maria Cristina Nonato; Marcelo Santos Castilho
Journal:  J Comput Aided Mol Des       Date:  2021-06-28       Impact factor: 3.686

6.  Combination of consensus and ensemble docking strategies for the discovery of human dihydroorotate dehydrogenase inhibitors.

Authors:  Garri Chilingaryan; Narek Abelyan; Arsen Sargsyan; Karen Nazaryan; Andre Serobian; Hovakim Zakaryan
Journal:  Sci Rep       Date:  2021-06-01       Impact factor: 4.379

7.  A 3D structural SARS-CoV-2-human interactome to explore genetic and drug perturbations.

Authors:  Shayne D Wierbowski; Siqi Liang; Yuan Liu; You Chen; Shagun Gupta; Nicole M Andre; Steven M Lipkin; Gary R Whittaker; Haiyuan Yu
Journal:  Nat Methods       Date:  2021-11-29       Impact factor: 47.990

8.  GNINA 1.0: molecular docking with deep learning.

Authors:  Andrew T McNutt; Paul Francoeur; Rishal Aggarwal; Tomohide Masuda; Rocco Meli; Matthew Ragoza; Jocelyn Sunseri; David Ryan Koes
Journal:  J Cheminform       Date:  2021-06-09       Impact factor: 5.514

Review 9.  Application of Various Molecular Modelling Methods in the Study of Estrogens and Xenoestrogens.

Authors:  Anna Helena Mazurek; Łukasz Szeleszczuk; Thomas Simonson; Dariusz Maciej Pisklak
Journal:  Int J Mol Sci       Date:  2020-09-03       Impact factor: 5.923

10.  Comprehensive virtual screening of 4.8 k flavonoids reveals novel insights into allosteric inhibition of SARS-CoV-2 MPRO.

Authors:  Gabriel Jiménez-Avalos; A Paula Vargas-Ruiz; Nicolás E Delgado-Pease; Gustavo E Olivos-Ramirez; Patricia Sheen; Manolo Fernández-Díaz; Miguel Quiliano; Mirko Zimic
Journal:  Sci Rep       Date:  2021-07-29       Impact factor: 4.379

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