ReviewInternational journal of molecular sciences2022
Structural Bioinformatics and Deep Learning of Metalloproteins: Recent Advances and Applications.
Review in International journal of molecular sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
11 citing papers in PubMed, 23 citations in OpenAlex.
- Master of Metals2: a graph neural network based architecture for the prediction of zinc binding sites in protein structures.Briefings in bioinformatics · 2026Article
- A bioinformatics approach to design minimal biomimetic metal-binding peptides.Communications chemistry · 2025Article
- Benchmarking Zinc-Binding Site Predictors: A Comparative Analysis of Structure-Based Approaches.Journal of chemical information and modeling · 2025Article
- deep-Sep: a deep learning-based method for fast and accurate prediction of selenoprotein genes in bacteria.mSystems · 2025Article
- Recent advances and future challenges in predictive modeling of metalloproteins by artificial intelligence.Molecules and cells · 2025Review
- Bacterial Metallostasis: Metal Sensing, Metalloproteome Remodeling, and Metal Trafficking.Chemical reviews · 2024Review
- Iron: Life's primeval transition metal.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- A database overview of metal-coordination distances in metalloproteins.Acta crystallographica. Section D, Structural biology · 2024Article
- Role of Histidine 310 in Amydetes vivianii firefly luciferase pH and metal sensitivities and improvement of its color tuning properties.Photochemical & photobiological sciences : Official journal of the European Photochemistry Association and the European Society for Photobiology · 2024Article
- Hunting down zinc(II)-binding sites in proteins with distance matrices.Bioinformatics (Oxford, England) · 2023Article
- MetalProGNet: a structure-based deep graph model for metalloprotein-ligand interaction predictions.Chemical science · 2023Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
All living organisms require metal ions for their energy production and metabolic and biosynthetic processes. Within cells, the metal ions involved in the formation of adducts interact with metabolites and macromolecules (proteins and nucleic acids). The proteins that require binding to one or more metal ions in order to be able to carry out their physiological function are called metalloproteins. About one third of all protein structures in the Protein Data Bank involve metalloproteins. Over the past few years there has been tremendous progress in the number of computational tools and techniques making use of 3D structural information to support the investigation of metalloproteins. This trend has been boosted by the successful applications of neural networks and machine/deep learning approaches in molecular and structural biology at large. In this review, we discuss recent advances in the development and availability of resources dealing with metalloproteins from a structure-based perspective. We start by addressing tools for the prediction of metal-binding sites (MBSs) using structural information on apo-proteins. Then, we provide an overview of the methods for and lessons learned from the structural comparison of MBSs in a fold-independent manner. We then move to describing databases of metalloprotein/MBS structures. Finally, we summarizing recent ML/DL applications enhancing the functional interpretation of metalloprotein structures.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.