ppLM-CO:Pre-trained Protein Language Model for Codon Optimization
Bibliographic record
Abstract
Abstract Messenger ribonucleic acid (mRNA) vaccines represent a major advancement in synthetic biology, yet their efficacy remains limited by how efficiently the encoded protein is translated within the host. Since multiple codons can code for the same amino acid, the search space of possible coding sequences (CDS) within mRNA grows exponentially with protein length, making the problem highly underdetermined. Finding CDSs that yield efficient translation hinges on codon optimization—the process of choosing among synonymous codons, while encoding the same protein but differ in their effects on translation speed, tRNA availability, and mRNA secondary structure. Recent deep learning approaches have framed codon optimization as a sequence learning problem, where the goal is to model context-dependent codon usage patterns across the amino acid in protein sequence. However, these methods rely on large sequence models that learn amino-acid embeddings from scratch, leading to computationally intensive training. We propose ppLM-CO, a lightweight codon optimization framework that integrates pretrained protein language models (ppLMs) to directly provide contextual amino-acid embeddings, thereby eliminating the need for embedding learning. This design reduces trainable parameters by over 92% – 99% compared with prior deep models while maintaining complete biological fidelity. In-silico evaluations across three species and two vaccine targets—SARS-CoV-2 spike and Varicella-Zoster Virus (VZV) gE viral proteins—demonstrate that ppLM-CO consistently achieves higher expression and competitive stability, establishing a scalable and biologically consistent approach for codon optimization.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".