arXiv AI October 5, 2026 By 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

Predictor-Guided Latent Space Codon Optimization for Maximizing Protein Expression

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Predictor-Guided Latent Space Codon Optimization (LSCO) transforms the discrete codon selection problem into a continuous optimization task by embedding sequences into the latent space of a pretrained mRNA language model, allowing gradient-based search. LSCO integrates a data-driven expression objective from an uncertainty-aware predictor, a Minimum-Free-Energy regularizer for structural stability, a naturalness prior derived from a protein-to-codon back-translation model, and constrained decoding to preserve protein fidelity. In a real-world wet-lab antibody expression dataset, LSCO outperforms both simple frequency-based methods and modern deep generative baselines in predicted expression while maintaining appropriate biophysical properties.

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