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Predicting Antifouling Activity and Acetylcholinesterase Inhibition of Marine-Derived Compounds Using a Computer-Aided Drug Design Approach

Biofouling is the undesirable growth of micro- and macro-organisms on artificial water-immersed surfaces, which results in high costs for the prevention and maintenance of this process (billion €/year) for aquaculture, shipping and other industries that rely on coastal and off-shore infrastructure....

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Autores principales: Gaudêncio, Susana P., Pereira, Florbela
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8879326/
https://www.ncbi.nlm.nih.gov/pubmed/35200658
http://dx.doi.org/10.3390/md20020129
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author Gaudêncio, Susana P.
Pereira, Florbela
author_facet Gaudêncio, Susana P.
Pereira, Florbela
author_sort Gaudêncio, Susana P.
collection PubMed
description Biofouling is the undesirable growth of micro- and macro-organisms on artificial water-immersed surfaces, which results in high costs for the prevention and maintenance of this process (billion €/year) for aquaculture, shipping and other industries that rely on coastal and off-shore infrastructure. To date, there are still no sustainable, economical and environmentally safe solutions to overcome this challenging phenomenon. A computer-aided drug design (CADD) approach comprising ligand- and structure-based methods was explored for predicting the antifouling activities of marine natural products (MNPs). In the CADD ligand-based method, 141 organic molecules extracted from the ChEMBL database and literature with antifouling screening data were used to build the quantitative structure–activity relationship (QSAR) classification model. An overall predictive accuracy score of up to 71% was achieved with the best QSAR model for external and internal validation using test and training sets. A virtual screening campaign of 14,492 MNPs from Encinar’s website and 14 MNPs that are currently in the clinical pipeline was also carried out using the best QSAR model developed. In the CADD structure-based approach, the 125 MNPs that were selected by the QSAR approach were used in molecular docking experiments against the acetylcholinesterase enzyme. Overall, 16 MNPs were proposed as the most promising marine drug-like leads as antifouling agents, e.g., macrocyclic lactam, macrocyclic alkaloids, indole and pyridine derivatives.
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spelling pubmed-88793262022-02-26 Predicting Antifouling Activity and Acetylcholinesterase Inhibition of Marine-Derived Compounds Using a Computer-Aided Drug Design Approach Gaudêncio, Susana P. Pereira, Florbela Mar Drugs Article Biofouling is the undesirable growth of micro- and macro-organisms on artificial water-immersed surfaces, which results in high costs for the prevention and maintenance of this process (billion €/year) for aquaculture, shipping and other industries that rely on coastal and off-shore infrastructure. To date, there are still no sustainable, economical and environmentally safe solutions to overcome this challenging phenomenon. A computer-aided drug design (CADD) approach comprising ligand- and structure-based methods was explored for predicting the antifouling activities of marine natural products (MNPs). In the CADD ligand-based method, 141 organic molecules extracted from the ChEMBL database and literature with antifouling screening data were used to build the quantitative structure–activity relationship (QSAR) classification model. An overall predictive accuracy score of up to 71% was achieved with the best QSAR model for external and internal validation using test and training sets. A virtual screening campaign of 14,492 MNPs from Encinar’s website and 14 MNPs that are currently in the clinical pipeline was also carried out using the best QSAR model developed. In the CADD structure-based approach, the 125 MNPs that were selected by the QSAR approach were used in molecular docking experiments against the acetylcholinesterase enzyme. Overall, 16 MNPs were proposed as the most promising marine drug-like leads as antifouling agents, e.g., macrocyclic lactam, macrocyclic alkaloids, indole and pyridine derivatives. MDPI 2022-02-08 /pmc/articles/PMC8879326/ /pubmed/35200658 http://dx.doi.org/10.3390/md20020129 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 Article
Gaudêncio, Susana P.
Pereira, Florbela
Predicting Antifouling Activity and Acetylcholinesterase Inhibition of Marine-Derived Compounds Using a Computer-Aided Drug Design Approach
title Predicting Antifouling Activity and Acetylcholinesterase Inhibition of Marine-Derived Compounds Using a Computer-Aided Drug Design Approach
title_full Predicting Antifouling Activity and Acetylcholinesterase Inhibition of Marine-Derived Compounds Using a Computer-Aided Drug Design Approach
title_fullStr Predicting Antifouling Activity and Acetylcholinesterase Inhibition of Marine-Derived Compounds Using a Computer-Aided Drug Design Approach
title_full_unstemmed Predicting Antifouling Activity and Acetylcholinesterase Inhibition of Marine-Derived Compounds Using a Computer-Aided Drug Design Approach
title_short Predicting Antifouling Activity and Acetylcholinesterase Inhibition of Marine-Derived Compounds Using a Computer-Aided Drug Design Approach
title_sort predicting antifouling activity and acetylcholinesterase inhibition of marine-derived compounds using a computer-aided drug design approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8879326/
https://www.ncbi.nlm.nih.gov/pubmed/35200658
http://dx.doi.org/10.3390/md20020129
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