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Related Experiment Video

Updated: Oct 19, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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Protein Abundance Prediction Through Machine Learning Methods.

Mauricio Ferreira1, Rafaela Ventorim1, Eduardo Almeida1

  • 1Department of Microbiology, Universidade Federal de Viçosa, Viçosa, MG 36570-900, Brazil.

Journal of Molecular Biology
|September 26, 2021
PubMed
Summary

Gene codon usage bias significantly impacts protein abundance. Machine learning models accurately predict protein levels from codon patterns, improving systems biology and metabolic engineering. This research offers new insights into protein quantification.

Keywords:
codon usage biasmetabolic engineeringmetabolic modellingquantitative proteomicssystems biology

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Area of Science:

  • Systems biology
  • Molecular biology
  • Bioinformatics

Background:

  • Proteins drive physiological processes, making their quantification vital for systems biology.
  • Current protein quantification methods like mass spectrometry have limitations.
  • Protein abundance is influenced by translation kinetics, which depend on codon features.

Purpose of the Study:

  • To investigate the impact of codon usage bias on protein abundance.
  • To develop predictive models for protein abundance based on codon usage.
  • To integrate these predictions into metabolic models for enhanced systems biology applications.

Main Methods:

  • Analysis of synonymous codon usage and evolutionary selection patterns.
  • Development and validation of machine learning models to predict protein abundance from codon metrics.
  • Integration of predicted protein abundances into enzyme-constrained genome-scale metabolic models.

Main Results:

  • Observed distinct codon usage patterns between genes encoding highly and lowly abundant proteins.
  • Machine learning models achieved high accuracy in predicting protein abundances, correlating well with experimental data.
  • Simulated phenotypes using predicted protein abundances closely matched experimental data.

Conclusions:

  • Codon usage bias is a significant determinant of protein abundance.
  • Machine learning models based on codon usage offer a powerful tool for predicting protein levels.
  • These predictive models enhance the accuracy and utility of systems metabolic engineering.