A Transformer-based Multi-omics Model for Translation Efficiency in S. cerevisiae

Precise regulation of protein synthesis is fundamental to cellular homeostasis and remains a primary target for synthetic biology applications. However, the non-linear relationship between mRNA abundance and protein levels presents complexities that poses challenges for predictive engineering. Here, we present TRIM, a Transformer-based RNA Inference Model that leverages full-length mRNA sequences and multi-omics data to predict translation efficiency. By employing a Parallel Expert Mixer, TRIM achieves robust prediction accuracy ( R ² ≥ 0.8,Pearson r ≥ 0.9). Trained on multimodal data from massive Saccharomyces cerevisiae isolates, TRIM demonstrates outstanding biological interpretability, helping to decipher complex translational patterns such as synergistic effects between bases, sequence-dependent codon preference in different stages, and distinct attention on key secondary structures. These results indicate that the integration of multi-omics data with holistic sequence modeling can effectively decode the cis-regulatory grammar of translation as well as providing a scalable and interpretable generative framework for future synthetic biology engineering.