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Machine Learning Approaches for Metalloproteins

Metalloproteins are a family of proteins characterized by metal ion binding, whereby the presence of these ions confers key catalytic and ligand-binding properties. Due to their ubiquity among biological systems, researchers have made immense efforts to predict the structural and functional roles of...

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Detalles Bibliográficos
Autores principales: Yu, Yue, Wang, Ruobing, Teo, Ruijie D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8878495/
https://www.ncbi.nlm.nih.gov/pubmed/35209064
http://dx.doi.org/10.3390/molecules27041277
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author Yu, Yue
Wang, Ruobing
Teo, Ruijie D.
author_facet Yu, Yue
Wang, Ruobing
Teo, Ruijie D.
author_sort Yu, Yue
collection PubMed
description Metalloproteins are a family of proteins characterized by metal ion binding, whereby the presence of these ions confers key catalytic and ligand-binding properties. Due to their ubiquity among biological systems, researchers have made immense efforts to predict the structural and functional roles of metalloproteins. Ultimately, having a comprehensive understanding of metalloproteins will lead to tangible applications, such as designing potent inhibitors in drug discovery. Recently, there has been an acceleration in the number of studies applying machine learning to predict metalloprotein properties, primarily driven by the advent of more sophisticated machine learning algorithms. This review covers how machine learning tools have consolidated and expanded our comprehension of various aspects of metalloproteins (structure, function, stability, ligand-binding interactions, and inhibitors). Future avenues of exploration are also discussed.
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spelling pubmed-88784952022-02-26 Machine Learning Approaches for Metalloproteins Yu, Yue Wang, Ruobing Teo, Ruijie D. Molecules Review Metalloproteins are a family of proteins characterized by metal ion binding, whereby the presence of these ions confers key catalytic and ligand-binding properties. Due to their ubiquity among biological systems, researchers have made immense efforts to predict the structural and functional roles of metalloproteins. Ultimately, having a comprehensive understanding of metalloproteins will lead to tangible applications, such as designing potent inhibitors in drug discovery. Recently, there has been an acceleration in the number of studies applying machine learning to predict metalloprotein properties, primarily driven by the advent of more sophisticated machine learning algorithms. This review covers how machine learning tools have consolidated and expanded our comprehension of various aspects of metalloproteins (structure, function, stability, ligand-binding interactions, and inhibitors). Future avenues of exploration are also discussed. MDPI 2022-02-14 /pmc/articles/PMC8878495/ /pubmed/35209064 http://dx.doi.org/10.3390/molecules27041277 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
Yu, Yue
Wang, Ruobing
Teo, Ruijie D.
Machine Learning Approaches for Metalloproteins
title Machine Learning Approaches for Metalloproteins
title_full Machine Learning Approaches for Metalloproteins
title_fullStr Machine Learning Approaches for Metalloproteins
title_full_unstemmed Machine Learning Approaches for Metalloproteins
title_short Machine Learning Approaches for Metalloproteins
title_sort machine learning approaches for metalloproteins
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8878495/
https://www.ncbi.nlm.nih.gov/pubmed/35209064
http://dx.doi.org/10.3390/molecules27041277
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