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This research introduces Latent-Space Codon Optimization (LSCO), a method that transforms the discrete challenge of codon selection into a continuous problem solvable via gradient-based search within a pretrained mRNA language model's latent space. By integrating an uncertainty-aware expression predictor with structural stability constraints, LSCO demonstrates superior performance over heuristic proxies and existing deep generative baselines in real-world antibody production datasets. The work represents a significant methodological advance for applying advanced machine learning techniques to optimize therapeutic protein synthesis and mRNA vaccine design.
Predictor-Guided Latent Space Codon Optimization for Maximizing Protein Expression
arXiv:2610.03098v1 Announce Type: new Abstract: Codon optimization, the process of selecting synonymous codons to improve mRNA translation efficiency and protein expression, is central to therapeutic protein production and mRNA vaccines, yet it remains a hard problem. The design space is discrete and combinatorially large, precluding gradient-based methods, and existing tools rely on heuristic proxies (e.g., Codon Adaptation Index or GC-content) that poorly capture true expression. We introduce Latent-Space Codon Optimization (LSCO), which recasts this discrete problem as a continuous one by mapping sequences into the latent space of a pretrained mRNA language model, enabling efficient gradient-based search. LSCO combines four components: a data-driven expression objective from an uncertainty-aware predictor, a Minimum-Free-Energy regularizer for structural stability, a naturalness prior from a protein-to-codon back-translation model, and constrained decoding for protein fidelity. On a real-world, wet-lab antibody expression dataset, LSCO outperforms simple frequency-based, as well as modern deep generative baselines in predicted expression, while retaining suitable biophysical properties.
Authors:Alberto Caron, Tianyu Cui, Dmytro S. Lituiev, Mangal Prakash, Artem Moskalev, Amina Mollaysa, Bo Zhai, Hirsh Nanda, Daniel M. Poole, Zhongyin Liu, Iman Farasat, Robert Davidson, Nikolay V. Manyakov, Tommaso Mansi, Scott Oloff, Rui Liao
Abstract:Codon optimization, the process of selecting synonymous codons to improve mRNA translation efficiency and protein expression, is central to therapeutic protein production and mRNA vaccines, yet it remains a hard problem. The design space is discrete and combinatorially large, precluding gradient-based methods, and existing tools rely on heuristic proxies (e.g., Codon Adaptation Index or GC-content) that poorly capture true expression. We introduce Latent-Space Codon Optimization (LSCO), which recasts this discrete problem as a continuous one by mapping sequences into the latent space of a pretrained mRNA language model, enabling efficient gradient-based search. LSCO combines four components: a data-driven expression objective from an uncertainty-aware predictor, a Minimum-Free-Energy regularizer for structural stability, a naturalness prior from a protein-to-codon back-translation model, and constrained decoding for protein fidelity. On a real-world, wet-lab antibody expression dataset, LSCO outperforms simple frequency-based, as well as modern deep generative baselines in predicted expression, while retaining suitable biophysical properties.
Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
arXiv:2610.03098 [cs.AI]
(or arXiv:2610.03098v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.03098 arXiv-issued DOI via DataCite (pending registration)
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