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Co-evolution-based prediction of metal-binding sites in proteomes by machine learning
Yao Cheng1,2, Haobo Wang1,2, Hua Xu3
1Synthetic and Functional Biomolecules Center, Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, Peking University, Beijing, China.
Nature Chemical Biology
|January 2, 2023
Summary
MetalNet, a new co-evolution pipeline, identifies thousands of novel metal-binding sites in proteins. This tool enhances our understanding of metalloproteins and metal biology across species.
Area of Science:
- Biochemistry
- Structural Biology
- Bioinformatics
Background:
- Metal ions are crucial for protein structure, recognition, and catalysis.
- Existing methods for predicting metal-binding sites rely on sequence or structural motifs.
- A significant portion of the metalloproteome remains uncharacterized.
Purpose of the Study:
- To develop a novel computational pipeline, MetalNet, for systematic prediction of metal-binding sites in proteomes.
- To expand the known catalog of metalloproteins and investigate their biological roles.
- To provide a tool for exploring the understudied metalloproteome.
Main Methods:
- Developed a co-evolution-based computational pipeline named MetalNet.
- Applied MetalNet to proteomes of four representative prokaryotic species.
- Utilized biochemical and structural validation for newly predicted sites.
Main Results:
- Predicted 4,849 potential metalloproteins, significantly expanding annotated metalloproteomes.
- Validated previously unannotated metal-binding sites in proteins like E. coli citX (PDB: 7DCM, 7DCN).
- Successfully identified known zinc-binding sites in the human spliceosome complex.
Conclusions:
- MetalNet is an effective tool for discovering and annotating metal-binding sites.
- The pipeline significantly advances the study of metal biology and the metalloproteome.
- MetalNet enables the interrogation of previously hidden metalloproteins.
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