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Open AccessArticle

Zincbindpredict—Prediction of Zinc Binding Sites in Proteins

Division of Biosciences, Institute of Structural and Molecular Biology, University College London, Darwin Building, Gower Street, London WC1E 6BT, UK
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Academic Editors: Angelo Facchiano, Antonio Rosato, Francesco Musiani and Claudia Andreini
Molecules 2021, 26(4), 966; https://doi.org/10.3390/molecules26040966
Received: 27 November 2020 / Revised: 26 January 2021 / Accepted: 9 February 2021 / Published: 12 February 2021
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. View Full-Text
Keywords: zinc; metal binding; proteins; prediction; machine learning zinc; metal binding; proteins; prediction; machine learning
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MDPI and ACS Style

Ireland, S.M.; Martin, A.C.R. Zincbindpredict—Prediction of Zinc Binding Sites in Proteins. Molecules 2021, 26, 966. https://doi.org/10.3390/molecules26040966

AMA Style

Ireland SM, Martin ACR. Zincbindpredict—Prediction of Zinc Binding Sites in Proteins. Molecules. 2021; 26(4):966. https://doi.org/10.3390/molecules26040966

Chicago/Turabian Style

Ireland, Sam M.; Martin, Andrew C.R. 2021. "Zincbindpredict—Prediction of Zinc Binding Sites in Proteins" Molecules 26, no. 4: 966. https://doi.org/10.3390/molecules26040966

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