Predicting gene sequences with AI to study codon usage patterns

• 成果类型: Article
• 署名作者: Sidi, Tomer; Bahiri-Elitzur, Shir; Tuller, Tamir; Kolodny, Rachel
• 署名单位: University of Haifa; Tel Aviv University; Tel Aviv University
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-10613
• DOI: 10.1073/pnas.2410003121
• 发表日期: 2025-01-07
• 关键词: protein-structure; translation; expression; BIAS; CONSERVATION; elongation; optimality; EFFICIENCY; EVOLUTION; selection

摘要

Selective pressure acts on the codon use, optimizing multiple, overlapping signals that are only partially understood. We trained AI models to predict codons given their amino acid sequence in the eukaryotes Saccharomyces cerevisiae and Schizosaccharomyces pombe and the bacteria Escherichia coli and Bacillus subtilis to study the extent to which we can learn patterns in naturally occurring codons to improve predictions. We trained our models on a subset of the proteins and evaluated their predictions on large, separate sets of proteins of varying lengths and expression levels. Our models significantly outperformed na & iuml;ve frequency- based approaches, demonstrating that there are learnable dependencies in evolutionary- selected codon usage. The prediction accuracy advantage of our models is greater for highly expressed genes and is greater in bacteria than eukaryotes, supporting the hypothesis that there is a monotonic relationship between selective pressure for complex codon patterns and effective population size. In S . cerevisiae and bacteria, our models were more accurate for longer proteins, suggesting that the learned patterns may be related to cotranslational folding. Gene functionality and conservation were also important determinants that affect the performance of our models. Finally, we showed that using information encoded in homologous proteins has only a minor effect on prediction accuracy, perhaps due to complex codon-usage codes in genes undergoing rapid evolution. Our study employing contemporary AI methods offers a unique perspective and a deep- learning- based prediction tool for evolutionary- selected codons. We hope that these can be useful to optimize codon usage in endogenous and heterologous proteins.