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Deep learning-based k(cat) prediction enables improved enzyme-constrained model reconstruction

Li, Feiran, 1993 (author)
Chalmers tekniska högskola,Chalmers University of Technology
Yuan, Le, 1994 (author)
Chalmers tekniska högskola,Chalmers University of Technology
Lu, Hongzhong, 1987 (author)
Chalmers tekniska högskola,Chalmers University of Technology
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Li, Gang, 1991 (author)
Chalmers tekniska högskola,Chalmers University of Technology
Chen, Yu, 1990 (author)
Chalmers tekniska högskola,Chalmers University of Technology
Engqvist, Martin, 1983 (author)
Chalmers tekniska högskola,Chalmers University of Technology
Kerkhoven, Eduard, 1985 (author)
Chalmers tekniska högskola,Chalmers University of Technology
Nielsen, Jens B, 1962 (author)
Chalmers tekniska högskola,Chalmers University of Technology,BioInnovation Institute (BII)
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 (creator_code:org_t)
2022-06-16
2022
English.
In: Nature Catalysis. - : Springer Science and Business Media LLC. - 2520-1158. ; 5:8, s. 662-672
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  • Journal article (peer-reviewed)
Abstract Subject headings
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  • Enzyme turnover numbers (k(cat)) are key to understanding cellular metabolism, proteome allocation and physiological diversity, but experimentally measured k(cat) data are sparse and noisy. Here we provide a deep learning approach (DLKcat) for high-throughput k(cat) prediction for metabolic enzymes from any organism merely from substrate structures and protein sequences. DLKcat can capture k(cat) changes for mutated enzymes and identify amino acid residues with a strong impact on k(cat) values. We applied this approach to predict genome-scale k(cat) values for more than 300 yeast species. Additionally, we designed a Bayesian pipeline to parameterize enzyme-constrained genome-scale metabolic models from predicted k(cat) values. The resulting models outperformed the corresponding original enzyme-constrained genome-scale metabolic models from previous pipelines in predicting phenotypes and proteomes, and enabled us to explain phenotypic differences. DLKcat and the enzyme-constrained genome-scale metabolic model construction pipeline are valuable tools to uncover global trends of enzyme kinetics and physiological diversity, and to further elucidate cellular metabolism on a large scale.

Subject headings

NATURVETENSKAP  -- Biologi -- Biokemi och molekylärbiologi (hsv//swe)
NATURAL SCIENCES  -- Biological Sciences -- Biochemistry and Molecular Biology (hsv//eng)
NATURVETENSKAP  -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Bioinformatics (hsv//eng)
NATURVETENSKAP  -- Biologi -- Bioinformatik och systembiologi (hsv//swe)
NATURAL SCIENCES  -- Biological Sciences -- Bioinformatics and Systems Biology (hsv//eng)

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