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Protein Abundance Prediction Through Machine Learning Methods

View ORCID ProfileMauricio Ferreira, View ORCID ProfileRafaela Ventorim, View ORCID ProfileEduardo Almeida, View ORCID ProfileSabrina Silveira, View ORCID ProfileWendel Silveira
doi: https://doi.org/10.1101/2020.09.17.302182
Mauricio Ferreira
1Department of Microbiology, Universidade Federal de Viçosa, Viçosa, Minas Gerais, 36570-900, Brazil
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Rafaela Ventorim
1Department of Microbiology, Universidade Federal de Viçosa, Viçosa, Minas Gerais, 36570-900, Brazil
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Eduardo Almeida
1Department of Microbiology, Universidade Federal de Viçosa, Viçosa, Minas Gerais, 36570-900, Brazil
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Sabrina Silveira
2Department of Computer Science, Universidade Federal de Viçosa, Viçosa, Minas Gerais, 36570-900, Brazil
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Wendel Silveira
1Department of Microbiology, Universidade Federal de Viçosa, Viçosa, Minas Gerais, 36570-900, Brazil
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  • For correspondence: wendel.silveira@ufv.br
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ABSTRACT

Proteins are responsible for most physiological processes, and their abundance provides crucial information for systems biology research. However, absolute protein quantification, as determined by mass spectrometry, still has limitations in capturing the protein pool. Protein abundance is impacted by translation kinetics, which rely on features of codons. In this study, we evaluated the effect of codon usage bias of genes on protein abundance. Notably, we observed differences regarding codon usage patterns between genes coding for highly abundant proteins and genes coding for less abundant proteins. Analysis of synonymous codon usage and evolutionary selection showed a clear split between the two groups. Our machine learning models predicted protein abundances from codon usage metrics with remarkable accuracy, achieving R2 values higher than previously reported in the literature. Upon integration of the predicted protein abundance in enzyme-constrained genome-scale metabolic models, the simulated phenotypes closely matched experimental data, which demonstrates that our predictive models are valuable tools for systems metabolic engineering approaches.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/LabFisUFV/protein_abundance_prediction

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted September 19, 2020.
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Protein Abundance Prediction Through Machine Learning Methods
Mauricio Ferreira, Rafaela Ventorim, Eduardo Almeida, Sabrina Silveira, Wendel Silveira
bioRxiv 2020.09.17.302182; doi: https://doi.org/10.1101/2020.09.17.302182
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Protein Abundance Prediction Through Machine Learning Methods
Mauricio Ferreira, Rafaela Ventorim, Eduardo Almeida, Sabrina Silveira, Wendel Silveira
bioRxiv 2020.09.17.302182; doi: https://doi.org/10.1101/2020.09.17.302182

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