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Improving Blind Docking in DOCK6 through an Automated Preliminary Fragment Probing Strategy

Probing protein surfaces to accurately predict the binding site and conformation of a small molecule is a challenge currently addressed through mainly two different approaches: blind docking and cavity detection-guided docking. Although cavity detection-guided blind docking has yielded high success...

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Autores principales: Jofily, Paula, Pascutti, Pedro G., Torres, Pedro H. M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7956365/
https://www.ncbi.nlm.nih.gov/pubmed/33668914
http://dx.doi.org/10.3390/molecules26051224
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author Jofily, Paula
Pascutti, Pedro G.
Torres, Pedro H. M.
author_facet Jofily, Paula
Pascutti, Pedro G.
Torres, Pedro H. M.
author_sort Jofily, Paula
collection PubMed
description Probing protein surfaces to accurately predict the binding site and conformation of a small molecule is a challenge currently addressed through mainly two different approaches: blind docking and cavity detection-guided docking. Although cavity detection-guided blind docking has yielded high success rates, it is less practical when a large number of molecules must be screened against many detected binding sites. On the other hand, blind docking allows for simultaneous search of the whole protein surface, which however entails the loss of accuracy and speed. To bridge this gap, in this study, we developed and tested BLinDPyPr, an automated pipeline which uses FTMap and DOCK6 to perform a hybrid blind docking strategy. Through our algorithm, FTMap docked probe clusters are converted into DOCK6 spheres for determining binding regions. Because these spheres are solely derived from FTMap probes, their locations are contained in and specific to multiple potential binding pockets, which become the regions that are simultaneously probed and chosen by the search algorithm based on the properties of each candidate ligand. This method yields pose prediction results (45.2–54.3% success rates) comparable to those of site-specific docking with the classic DOCK6 workflow (49.7–54.3%) and is half as time-consuming as the conventional blind docking method with DOCK6.
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spelling pubmed-79563652021-03-16 Improving Blind Docking in DOCK6 through an Automated Preliminary Fragment Probing Strategy Jofily, Paula Pascutti, Pedro G. Torres, Pedro H. M. Molecules Article Probing protein surfaces to accurately predict the binding site and conformation of a small molecule is a challenge currently addressed through mainly two different approaches: blind docking and cavity detection-guided docking. Although cavity detection-guided blind docking has yielded high success rates, it is less practical when a large number of molecules must be screened against many detected binding sites. On the other hand, blind docking allows for simultaneous search of the whole protein surface, which however entails the loss of accuracy and speed. To bridge this gap, in this study, we developed and tested BLinDPyPr, an automated pipeline which uses FTMap and DOCK6 to perform a hybrid blind docking strategy. Through our algorithm, FTMap docked probe clusters are converted into DOCK6 spheres for determining binding regions. Because these spheres are solely derived from FTMap probes, their locations are contained in and specific to multiple potential binding pockets, which become the regions that are simultaneously probed and chosen by the search algorithm based on the properties of each candidate ligand. This method yields pose prediction results (45.2–54.3% success rates) comparable to those of site-specific docking with the classic DOCK6 workflow (49.7–54.3%) and is half as time-consuming as the conventional blind docking method with DOCK6. MDPI 2021-02-25 /pmc/articles/PMC7956365/ /pubmed/33668914 http://dx.doi.org/10.3390/molecules26051224 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Jofily, Paula
Pascutti, Pedro G.
Torres, Pedro H. M.
Improving Blind Docking in DOCK6 through an Automated Preliminary Fragment Probing Strategy
title Improving Blind Docking in DOCK6 through an Automated Preliminary Fragment Probing Strategy
title_full Improving Blind Docking in DOCK6 through an Automated Preliminary Fragment Probing Strategy
title_fullStr Improving Blind Docking in DOCK6 through an Automated Preliminary Fragment Probing Strategy
title_full_unstemmed Improving Blind Docking in DOCK6 through an Automated Preliminary Fragment Probing Strategy
title_short Improving Blind Docking in DOCK6 through an Automated Preliminary Fragment Probing Strategy
title_sort improving blind docking in dock6 through an automated preliminary fragment probing strategy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7956365/
https://www.ncbi.nlm.nih.gov/pubmed/33668914
http://dx.doi.org/10.3390/molecules26051224
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