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Predicting gene sequences with AI to study codon usage patterns.

Tomer Sidi1, Shir Bahiri-Elitzur2, Tamir Tuller2,3

  • 1Department of Computer Science, University of Haifa, Haifa 3303221, Israel.

Proceedings of the National Academy of Sciences of the United States of America
|December 31, 2024
PubMed
Summary
This summary is machine-generated.

AI models learned complex codon usage patterns in bacteria and eukaryotes, significantly outperforming basic methods. These findings advance our understanding of evolutionary selection and offer tools for optimizing protein expression.

Keywords:
codon AI modelcodons predictionmimicking codons

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Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Selective pressures shape codon usage, optimizing biological signals that are not fully understood.
  • Codon usage patterns are influenced by evolutionary processes and gene expression levels.

Purpose of the Study:

  • To train AI models to predict codon usage based on amino acid sequences in various organisms.
  • To investigate the extent to which naturally occurring codon patterns can be learned and utilized for improved prediction.
  • To explore the relationship between prediction accuracy, gene expression, protein length, and evolutionary factors.

Main Methods:

  • Trained artificial intelligence (AI) models on protein sequences from *Saccharomyces cerevisiae*, *Schizosaccharomyces pombe*, *Escherichia coli*, and *Bacillus subtilis*.
  • Evaluated model predictions on separate datasets of proteins with varying lengths and expression levels.
  • Compared AI model performance against naïve frequency-based prediction methods.

Main Results:

  • AI models significantly outperformed frequency-based approaches, indicating learnable dependencies in evolutionary-selected codon usage.
  • Prediction accuracy was higher for highly expressed genes and greater in bacteria than eukaryotes, supporting a link between selection pressure and effective population size.
  • Models showed improved accuracy for longer proteins in *S. cerevisiae* and bacteria, suggesting a connection to cotranslational folding; gene functionality and conservation also impacted performance.

Conclusions:

  • Contemporary AI methods can effectively learn complex, evolutionary-selected codon usage patterns.
  • The developed deep-learning-based prediction tool offers insights into codon optimization for endogenous and heterologous protein expression.
  • Findings support hypotheses linking codon usage complexity to selective pressure and effective population size.