ArticleProtein science : a publication of the Protein Society2021
Identifying metal binding amino acids based on backbone geometries as a tool for metalloprotein engineering.
Article in Protein science : a publication of the Protein Society, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 11 citations in OpenAlex.
- Exploring and Targeting the Connection of Iron and Copper Homeostasis to Neurodegenerative Diseases.MedComm · 2026Review
- Hunting down zinc(II)-binding sites in proteins with distance matrices.Bioinformatics (Oxford, England) · 2023Article
- Structural Bioinformatics and Deep Learning of Metalloproteins: Recent Advances and Applications.International journal of molecular sciences · 2022Review
- Machine Learning Approaches for Metalloproteins.Molecules (Basel, Switzerland) · 2022Review
- A Comprehensive Review of Computation-Based Metal-Binding Prediction Approaches at the Residue Level.BioMed research international · 2022Review
- Identifying metal binding amino acids based on backbone geometries as a tool for metalloprotein engineering.Protein science : a publication of the Protein Society · 2021Article
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Metal cofactors within proteins perform a versatile set of essential cellular functions. In order to take advantage of the diverse functionality of metalloproteins, researchers have been working to design or modify metal binding sites in proteins to rationally tune the function or activity of the metal cofactor. This study has performed an analysis on the backbone atom geometries of metal-binding amino acids among 10 different metal binding sites within the entire protein data bank. A set of 13 geometric parameters (features) was identified that is capable of predicting the presence of a metal cofactor in the protein structure with overall accuracies of up to 97% given only the relative positions of their backbone atoms. The decision tree machine-learning algorithm used can quickly analyze an entire protein structure for the presence of sets of primary metal coordination spheres upon mutagenesis, independent of their original amino acid identities. The methodology was designed for application in the field of metalloprotein engineering. A cluster analysis using the data set was also performed and demonstrated that the features chosen are useful for identifying clusters of structurally similar metal-binding sites.
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