Viral-Mimicry and Translational Kinetics: Advanced Modeling for Tobacco-Based Expression

In this publication published in Synthetic and Systems Biotechnology, we evaluate the sequence features that actually drive heterologous expression in tobacco. By compiling the largest known dataset of plant-based expression studies, we found that predictive accuracy increases significantly when modeling the "tRNA supply-demand" and amino acid composition rather than just host codon bias. Notably, the study reveals that successful synthetic sequences often mimic the codon usage of natural plant pathogens like viruses and agrobacteria.

Key Finding: Our integrated model achieved a 0.57 correlation with actual protein yield, providing a definitive practical guideline for the design of efficient, high-titer plant-based expression vectors.

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https://www.sciencedirect.com/science/article/pii/S2405805X24001546#appsec1