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Zincbindpredict—Prediction of Zinc Binding Sites in Proteins
Background: Zinc binding proteins make up a significant proportion of the proteomes of most organisms and, within those proteins, zinc performs rôles in catalysis and structure stabilisation. Identifying the ability to bind zinc in a novel protein can offer insights into its functions and the mechan...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7918553/ https://www.ncbi.nlm.nih.gov/pubmed/33673040 http://dx.doi.org/10.3390/molecules26040966 |
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author | Ireland, Sam M. Martin, Andrew C. R. |
author_facet | Ireland, Sam M. Martin, Andrew C. R. |
author_sort | Ireland, Sam M. |
collection | PubMed |
description | Background: Zinc binding proteins make up a significant proportion of the proteomes of most organisms and, within those proteins, zinc performs rôles in catalysis and structure stabilisation. Identifying the ability to bind zinc in a novel protein can offer insights into its functions and the mechanism by which it carries out those functions. Computational means of doing so are faster than spectroscopic means, allowing for searching at much greater speeds and scales, and thereby guiding complimentary experimental approaches. Typically, computational models of zinc binding predict zinc binding for individual residues rather than as a single binding site, and typically do not distinguish between different classes of binding site—missing crucial properties indicative of zinc binding. Methods: Previously, we created ZincBindDB, a continuously updated database of known zinc binding sites, categorised by family (the set of liganding residues). Here, we use this dataset to create ZincBindPredict, a set of machine learning methods to predict the most common zinc binding site families for both structure and sequence. Results: The models all achieve an MCC ≥ 0.88, recall ≥ 0.93 and precision ≥ 0.91 for the structural models (mean MCC = 0.97), while the sequence models have MCC ≥ 0.64, recall ≥ 0.80 and precision ≥ 0.83 (mean MCC = 0.87), with the models for binding sites containing four liganding residues performing much better than this. Conclusions: The predictors outperform competing zinc binding site predictors and are available online via a web interface and a GraphQL API. |
format | Online Article Text |
id | pubmed-7918553 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-79185532021-03-02 Zincbindpredict—Prediction of Zinc Binding Sites in Proteins Ireland, Sam M. Martin, Andrew C. R. Molecules Article Background: Zinc binding proteins make up a significant proportion of the proteomes of most organisms and, within those proteins, zinc performs rôles in catalysis and structure stabilisation. Identifying the ability to bind zinc in a novel protein can offer insights into its functions and the mechanism by which it carries out those functions. Computational means of doing so are faster than spectroscopic means, allowing for searching at much greater speeds and scales, and thereby guiding complimentary experimental approaches. Typically, computational models of zinc binding predict zinc binding for individual residues rather than as a single binding site, and typically do not distinguish between different classes of binding site—missing crucial properties indicative of zinc binding. Methods: Previously, we created ZincBindDB, a continuously updated database of known zinc binding sites, categorised by family (the set of liganding residues). Here, we use this dataset to create ZincBindPredict, a set of machine learning methods to predict the most common zinc binding site families for both structure and sequence. Results: The models all achieve an MCC ≥ 0.88, recall ≥ 0.93 and precision ≥ 0.91 for the structural models (mean MCC = 0.97), while the sequence models have MCC ≥ 0.64, recall ≥ 0.80 and precision ≥ 0.83 (mean MCC = 0.87), with the models for binding sites containing four liganding residues performing much better than this. Conclusions: The predictors outperform competing zinc binding site predictors and are available online via a web interface and a GraphQL API. MDPI 2021-02-12 /pmc/articles/PMC7918553/ /pubmed/33673040 http://dx.doi.org/10.3390/molecules26040966 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 Ireland, Sam M. Martin, Andrew C. R. Zincbindpredict—Prediction of Zinc Binding Sites in Proteins |
title | Zincbindpredict—Prediction of Zinc Binding Sites in Proteins |
title_full | Zincbindpredict—Prediction of Zinc Binding Sites in Proteins |
title_fullStr | Zincbindpredict—Prediction of Zinc Binding Sites in Proteins |
title_full_unstemmed | Zincbindpredict—Prediction of Zinc Binding Sites in Proteins |
title_short | Zincbindpredict—Prediction of Zinc Binding Sites in Proteins |
title_sort | zincbindpredict—prediction of zinc binding sites in proteins |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7918553/ https://www.ncbi.nlm.nih.gov/pubmed/33673040 http://dx.doi.org/10.3390/molecules26040966 |
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