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Estimating the Similarity between Protein Pockets

With the exponential increase in publicly available protein structures, the comparison of protein binding sites naturally emerged as a scientific topic to explain observations or generate hypotheses for ligand design, notably to predict ligand selectivity for on- and off-targets, explain polypharmac...

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Detalles Bibliográficos
Autores principales: Eguida, Merveille, Rognan, Didier
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9604425/
https://www.ncbi.nlm.nih.gov/pubmed/36293316
http://dx.doi.org/10.3390/ijms232012462
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author Eguida, Merveille
Rognan, Didier
author_facet Eguida, Merveille
Rognan, Didier
author_sort Eguida, Merveille
collection PubMed
description With the exponential increase in publicly available protein structures, the comparison of protein binding sites naturally emerged as a scientific topic to explain observations or generate hypotheses for ligand design, notably to predict ligand selectivity for on- and off-targets, explain polypharmacology, and design target-focused libraries. The current review summarizes the state-of-the-art computational methods applied to pocket detection and comparison as well as structural druggability estimates. The major strengths and weaknesses of current pocket descriptors, alignment methods, and similarity search algorithms are presented. Lastly, an exhaustive survey of both retrospective and prospective applications in diverse medicinal chemistry scenarios illustrates the capability of the existing methods and the hurdle that still needs to be overcome for more accurate predictions.
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spelling pubmed-96044252022-10-27 Estimating the Similarity between Protein Pockets Eguida, Merveille Rognan, Didier Int J Mol Sci Review With the exponential increase in publicly available protein structures, the comparison of protein binding sites naturally emerged as a scientific topic to explain observations or generate hypotheses for ligand design, notably to predict ligand selectivity for on- and off-targets, explain polypharmacology, and design target-focused libraries. The current review summarizes the state-of-the-art computational methods applied to pocket detection and comparison as well as structural druggability estimates. The major strengths and weaknesses of current pocket descriptors, alignment methods, and similarity search algorithms are presented. Lastly, an exhaustive survey of both retrospective and prospective applications in diverse medicinal chemistry scenarios illustrates the capability of the existing methods and the hurdle that still needs to be overcome for more accurate predictions. MDPI 2022-10-18 /pmc/articles/PMC9604425/ /pubmed/36293316 http://dx.doi.org/10.3390/ijms232012462 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Eguida, Merveille
Rognan, Didier
Estimating the Similarity between Protein Pockets
title Estimating the Similarity between Protein Pockets
title_full Estimating the Similarity between Protein Pockets
title_fullStr Estimating the Similarity between Protein Pockets
title_full_unstemmed Estimating the Similarity between Protein Pockets
title_short Estimating the Similarity between Protein Pockets
title_sort estimating the similarity between protein pockets
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9604425/
https://www.ncbi.nlm.nih.gov/pubmed/36293316
http://dx.doi.org/10.3390/ijms232012462
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