Metal3D: a general deep learning framework for accurate metal ion location prediction in proteins

Metal ions are essential cofactors for many proteins and play a crucial role in many applications such as enzyme design or design of protein-protein interactions because they are biologically abundant, tether to the protein using strong interactions, and have favorable catalytic properties. Computat...

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Published inNature communications Vol. 14; no. 1; p. 2713
Main Authors Dürr, Simon L., Levy, Andrea, Rothlisberger, Ursula
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 11.05.2023
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Abstract Metal ions are essential cofactors for many proteins and play a crucial role in many applications such as enzyme design or design of protein-protein interactions because they are biologically abundant, tether to the protein using strong interactions, and have favorable catalytic properties. Computational design of metalloproteins is however hampered by the complex electronic structure of many biologically relevant metals such as zinc . In this work, we develop two tools - Metal3D (based on 3D convolutional neural networks) and Metal1D (solely based on geometric criteria) to improve the location prediction of zinc ions in protein structures. Comparison with other currently available tools shows that Metal3D is the most accurate zinc ion location predictor to date with predictions within 0.70 ± 0.64 Å of experimental locations. Metal3D outputs a confidence metric for each predicted site and works on proteins with few homologes in the protein data bank. Metal3D predicts a global zinc density that can be used for annotation of computationally predicted structures and a per residue zinc density that can be used in protein design workflows. Currently trained on zinc, the framework of Metal3D is readily extensible to other metals by modifying the training data. Zinc is an essential metal for many proteins. Here, the authors propose a model based on 3D convolutional networks to predict the location of zinc in experimental and computationally predicted structures within a framework readily extensible to other metals.
AbstractList Metal ions are essential cofactors for many proteins and play a crucial role in many applications such as enzyme design or design of protein-protein interactions because they are biologically abundant, tether to the protein using strong interactions, and have favorable catalytic properties. Computational design of metalloproteins is however hampered by the complex electronic structure of many biologically relevant metals such as zinc . In this work, we develop two tools - Metal3D (based on 3D convolutional neural networks) and Metal1D (solely based on geometric criteria) to improve the location prediction of zinc ions in protein structures. Comparison with other currently available tools shows that Metal3D is the most accurate zinc ion location predictor to date with predictions within 0.70 ± 0.64 Å of experimental locations. Metal3D outputs a confidence metric for each predicted site and works on proteins with few homologes in the protein data bank. Metal3D predicts a global zinc density that can be used for annotation of computationally predicted structures and a per residue zinc density that can be used in protein design workflows. Currently trained on zinc, the framework of Metal3D is readily extensible to other metals by modifying the training data. Zinc is an essential metal for many proteins. Here, the authors propose a model based on 3D convolutional networks to predict the location of zinc in experimental and computationally predicted structures within a framework readily extensible to other metals.
Metal ions are essential cofactors for many proteins and play a crucial role in many applications such as enzyme design or design of protein-protein interactions because they are biologically abundant, tether to the protein using strong interactions, and have favorable catalytic properties. Computational design of metalloproteins is however hampered by the complex electronic structure of many biologically relevant metals such as zinc . In this work, we develop two tools - Metal3D (based on 3D convolutional neural networks) and Metal1D (solely based on geometric criteria) to improve the location prediction of zinc ions in protein structures. Comparison with other currently available tools shows that Metal3D is the most accurate zinc ion location predictor to date with predictions within 0.70 ± 0.64 Å of experimental locations. Metal3D outputs a confidence metric for each predicted site and works on proteins with few homologes in the protein data bank. Metal3D predicts a global zinc density that can be used for annotation of computationally predicted structures and a per residue zinc density that can be used in protein design workflows. Currently trained on zinc, the framework of Metal3D is readily extensible to other metals by modifying the training data.
Abstract Metal ions are essential cofactors for many proteins and play a crucial role in many applications such as enzyme design or design of protein-protein interactions because they are biologically abundant, tether to the protein using strong interactions, and have favorable catalytic properties. Computational design of metalloproteins is however hampered by the complex electronic structure of many biologically relevant metals such as zinc . In this work, we develop two tools - Metal3D (based on 3D convolutional neural networks) and Metal1D (solely based on geometric criteria) to improve the location prediction of zinc ions in protein structures. Comparison with other currently available tools shows that Metal3D is the most accurate zinc ion location predictor to date with predictions within 0.70 ± 0.64 Å of experimental locations. Metal3D outputs a confidence metric for each predicted site and works on proteins with few homologes in the protein data bank. Metal3D predicts a global zinc density that can be used for annotation of computationally predicted structures and a per residue zinc density that can be used in protein design workflows. Currently trained on zinc, the framework of Metal3D is readily extensible to other metals by modifying the training data.
Abstract Metal ions are essential cofactors for many proteins and play a crucial role in many applications such as enzyme design or design of protein-protein interactions because they are biologically abundant, tether to the protein using strong interactions, and have favorable catalytic properties. Computational design of metalloproteins is however hampered by the complex electronic structure of many biologically relevant metals such as zinc . In this work, we develop two tools - Metal3D (based on 3D convolutional neural networks) and Metal1D (solely based on geometric criteria) to improve the location prediction of zinc ions in protein structures. Comparison with other currently available tools shows that Metal3D is the most accurate zinc ion location predictor to date with predictions within 0.70 ± 0.64 Å of experimental locations. Metal3D outputs a confidence metric for each predicted site and works on proteins with few homologes in the protein data bank. Metal3D predicts a global zinc density that can be used for annotation of computationally predicted structures and a per residue zinc density that can be used in protein design workflows. Currently trained on zinc, the framework of Metal3D is readily extensible to other metals by modifying the training data.
Metal ions are essential cofactors for many proteins and play a crucial role in many applications such as enzyme design or design of protein-protein interactions because they are biologically abundant, tether to the protein using strong interactions, and have favorable catalytic properties. Computational design of metalloproteins is however hampered by the complex electronic structure of many biologically relevant metals such as zinc . In this work, we develop two tools - Metal3D (based on 3D convolutional neural networks) and Metal1D (solely based on geometric criteria) to improve the location prediction of zinc ions in protein structures. Comparison with other currently available tools shows that Metal3D is the most accurate zinc ion location predictor to date with predictions within 0.70 ± 0.64 Å of experimental locations. Metal3D outputs a confidence metric for each predicted site and works on proteins with few homologes in the protein data bank. Metal3D predicts a global zinc density that can be used for annotation of computationally predicted structures and a per residue zinc density that can be used in protein design workflows. Currently trained on zinc, the framework of Metal3D is readily extensible to other metals by modifying the training data.Zinc is an essential metal for many proteins. Here, the authors propose a model based on 3D convolutional networks to predict the location of zinc in experimental and computationally predicted structures within a framework readily extensible to other metals.
ArticleNumber 2713
Author Rothlisberger, Ursula
Levy, Andrea
Dürr, Simon L.
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  organization: Laboratory of Computational Chemistry and Biochemistry,Institute of Chemical Sciences and Engineering, Swiss Federal Institute of Technology (EPFL)
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SSID ssj0000391844
Score 2.4992838
Snippet Metal ions are essential cofactors for many proteins and play a crucial role in many applications such as enzyme design or design of protein-protein...
Abstract Metal ions are essential cofactors for many proteins and play a crucial role in many applications such as enzyme design or design of protein-protein...
Abstract Metal ions are essential cofactors for many proteins and play a crucial role in many applications such as enzyme design or design of protein-protein...
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SubjectTerms 631/114/1305
631/114/469
631/45/612/1141
631/535/1267
Annotations
Artificial neural networks
Binding Sites
Deep Learning
Density
Design
Electronic structure
Extensibility
Heavy metals
Humanities and Social Sciences
Ions - chemistry
Machine learning
Metal ions
Metalloproteins - metabolism
Metals
Metals - chemistry
multidisciplinary
Neural networks
Predictions
Protein interaction
Proteins
Science
Science (multidisciplinary)
Three dimensional models
Zinc
Zinc - metabolism
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Title Metal3D: a general deep learning framework for accurate metal ion location prediction in proteins
URI https://link.springer.com/article/10.1038/s41467-023-37870-6
https://www.ncbi.nlm.nih.gov/pubmed/37169763
https://www.proquest.com/docview/2812329332
https://search.proquest.com/docview/2813554415
https://pubmed.ncbi.nlm.nih.gov/PMC10175565
https://doaj.org/article/68adb35df3ed462d975edd8518febaf9
Volume 14
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