Leveraging amino acid embeddings for accurate translation efficiency prediction.

Incorporating pretrained amino-acid sequence embeddings with mRNA-derived features improves translation efficiency prediction accuracy compared with using mRNA features alone.

• Why it matters: Existing translation-efficiency models largely ignore amino-acid-level information, even though ribosome profiling datasets link mRNA sequence context to translation outcomes.
• What they did: The study builds a machine-learning model that combines fixed amino-acid embeddings from a pretrained protein language model with standard mRNA-derived features.
• The result: The embedding-augmented approach integrates protein-level and nucleotide-level signals, enabling more accurate translation efficiency prediction for mRNA sequences.

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