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Functionally annotating cysteine disulfides and metal binding sites in the plant kingdom using AlphaFold2 predicted
Patrick Willems1, Jingjing Huang2, Joris Messens3
1Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052, Ghent, Belgium; VIB Center for Plant Systems Biology, VIB, 9052, Ghent, Belgium; Department of Biomolecular Medicine, Ghent University, 9052, Ghent, Belgium; Center for Medical Biotechnology, VIB, 9052, Ghent, Belgium.
Free Radical Biology & Medicine
|December 9, 2022
Summary
Deep learning models like AlphaFold2 accurately predict protein structures, revealing insights into plant cysteine residues. This analysis highlights the reliability of predicted disulfide bonds and evolutionary trends in metal coordination and secretion.
Area of Science:
- Biochemistry
- Structural Biology
- Bioinformatics
Background:
- Deep learning models, notably AlphaFold2, achieve high accuracy in predicting three-dimensional protein structures.
- The availability of over 200 million structural models offers a vast resource for functional protein annotation.
- Cysteine residues play critical roles in protein structure, function, and redox biology.
Purpose of the Study:
- To functionally and evolutionarily analyze cysteine residues in plants using AlphaFold2 predicted structures.
- To identify metal ligands coordinated by cysteine residues and systematically analyze cysteine disulfides.
- To assess the reliability of predicted disulfide bonds by comparing them with experimental data.
Main Methods:
- Utilized AlphaFold2 predicted structures for fifteen plant proteomes.
- Analyzed cysteine residue coordination with metal ligands.
- Systematically identified and evaluated cysteine disulfides based on agreement with experimental structures, stereochemistry, subcellular distribution, and cysteine oxidation levels.
- Examined evolutionary trends in metal binding sites and disulfide bond formation.
Main Results:
- The majority of predicted cysteine disulfides (∼96% agreement) are reliable when compared to X-ray and NMR structures.
- Proteomic data and subcellular distribution support the validity of predicted disulfides.
- Evolutionary analysis reveals an increase in zinc-binding sites at the expense of iron-sulfur clusters in plants.
- Disulfide bond formation is elevated in secreted proteins of land plants, potentially aiding adaptation.
Conclusions:
- AlphaFold2 predicted structural models are a valuable resource for studying cysteine residues and protein redox biology in plants.
- Predicted disulfide bonds demonstrate high confidence and provide insights into protein evolution and environmental adaptation.
- The study reveals significant evolutionary shifts in metal coordination and disulfide bond prevalence within the plant kingdom.