dockECR : Open consensus docking and ranking protocol for virtual screening of small molecules
Copyright © 2021 Elsevier Inc. All rights reserved..
The development of open computational pipelines to accelerate the discovery of treatments for emerging diseases allows finding novel solutions in shorter periods of time. Consensus molecular docking is one of these approaches, and its main purpose is to increase the detection of real actives within virtual screening campaigns. Here we present dockECR, an open consensus docking and ranking protocol that implements the exponential consensus ranking method to prioritize molecular candidates. The protocol uses four open source molecular docking programs: AutoDock Vina, Smina, LeDock and rDock, to rank the molecules. In addition, we introduce a scoring strategy based on the average RMSD obtained from comparing the best poses from each single program to complement the consensus ranking with information about the predicted poses. The protocol was benchmarked using 15 relevant protein targets with known actives and decoys, and applied using the main protease of the SARS-CoV-2 virus. For the application, different crystal structures of the protease, and frames obtained from molecular dynamics simulations were used to dock a library of 79 molecules derived from previously co-crystallized fragments. The ranking obtained with dockECR was used to prioritize eight candidates, which were evaluated in terms of the interactions generated with key residues from the protease. The protocol can be implemented in any virtual screening campaign involving proteins as molecular targets. The dockECR code is publicly available at: https://github.com/rochoa85/dockECR.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:109 |
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| Enthalten in: |
Journal of molecular graphics & modelling - 109(2021) vom: 01. Dez., Seite 108023 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Ochoa, Rodrigo [VerfasserIn] |
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| Links: |
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| Themen: |
Exponential consensus ranking |
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| Anmerkungen: |
Date Completed 05.11.2021 Date Revised 21.12.2022 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.jmgm.2021.108023 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM330974459 |
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| 245 | 1 | 0 | |a dockECR |b Open consensus docking and ranking protocol for virtual screening of small molecules |
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| 500 | |a Date Completed 05.11.2021 | ||
| 500 | |a Date Revised 21.12.2022 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2021 Elsevier Inc. All rights reserved. | ||
| 520 | |a The development of open computational pipelines to accelerate the discovery of treatments for emerging diseases allows finding novel solutions in shorter periods of time. Consensus molecular docking is one of these approaches, and its main purpose is to increase the detection of real actives within virtual screening campaigns. Here we present dockECR, an open consensus docking and ranking protocol that implements the exponential consensus ranking method to prioritize molecular candidates. The protocol uses four open source molecular docking programs: AutoDock Vina, Smina, LeDock and rDock, to rank the molecules. In addition, we introduce a scoring strategy based on the average RMSD obtained from comparing the best poses from each single program to complement the consensus ranking with information about the predicted poses. The protocol was benchmarked using 15 relevant protein targets with known actives and decoys, and applied using the main protease of the SARS-CoV-2 virus. For the application, different crystal structures of the protease, and frames obtained from molecular dynamics simulations were used to dock a library of 79 molecules derived from previously co-crystallized fragments. The ranking obtained with dockECR was used to prioritize eight candidates, which were evaluated in terms of the interactions generated with key residues from the protease. The protocol can be implemented in any virtual screening campaign involving proteins as molecular targets. The dockECR code is publicly available at: https://github.com/rochoa85/dockECR | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Exponential consensus ranking | |
| 650 | 4 | |a Molecular docking | |
| 650 | 4 | |a Open source | |
| 650 | 4 | |a SARS-CoV-2 main protease | |
| 650 | 4 | |a Virtual screening | |
| 650 | 7 | |a Ligands |2 NLM | |
| 700 | 1 | |a Palacio-Rodriguez, Karen |e verfasserin |4 aut | |
| 700 | 1 | |a Clemente, Camila M |e verfasserin |4 aut | |
| 700 | 1 | |a Adler, Natalia S |e verfasserin |4 aut | |
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| 856 | 4 | 0 | |u http://dx.doi.org/10.1016/j.jmgm.2021.108023 |3 Volltext |
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