arXiv AI
Oct 5

Predictor-Guided Latent Space Codon Optimization for Maximizing Protein Expression

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.

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
arXiv Machine Learning
Oct 12

MORAD: Reinforcement Learning with Multi-Objective Rewards for RNA Inverse Design

MORAD is an online reinforcement learning framework that trains a single shared policy for backbone‑conditioned RNA diffusion models, enabling structure‑conditioned RNA inverse design across diverse target backbones. It integrates backbone‑conditioned group sampling, group‑relative reward normalization, and a forward‑process diffusion objective, jointly optimizing six measurements—including tertiary geometry, base pairing, ensemble behavior, and sequence composition—by mapping each to a bounded desirability score and combining them with a weighted geometric mean. Compared to a tertiary‑structure‑only reward, MORAD improves 10 of 11 evaluation metrics, achieving significant gains in GDT‑TS, pairing MCC, and normalized ensemble defect, and surpasses existing methods such as RiboDiffusion, gRNAde, and RDesign on tertiary structural accuracy, secondary‑structure pairing, and thermodynamic quality.

By Jixin Tang, Ji Guo, Jun Zhao