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Codon Deoptimization of Multispecific Biologics Reduces Mispairing During Transient Mammalian Protein Expression

Chang, T. Z.; Ma, W.; Guo, J.; Hu, J.; Mix, K.; Tang, Y.; Wong, K.; Bric-Furlong, E.; Lennon, A.; Hall, B.; Hoffmann, D.

2026-01-06 bioengineering
10.64898/2026.01.05.694717 bioRxiv
Show abstract

Codon optimization is utilized in biologics design to maximize protein expression. Selecting the host organisms most frequently used codons for each amino acid can significantly enhance recombinant protein expression yields. However, non-optimal codons in mRNA can be critical for functional protein production through inducing pauses in or attenuating protein translation. In our study, we have investigated the effect of deoptimizing serine codons in biologics by shifting them from the five most frequently used codons to the least (TCG). Rare serine codons were strategically inserted into the coding sequences of the constant regions in a trispecific antibody (Protein 1), a bispecific antibody (Protein 2), and multiple non-proprietary bispecific antibodies. We observed that inserting 1-2 rare serine codons within an open reading frame led to expression changes that reduced the formation of mispaired 2x light chain and half-molecule species. Protein purity was drastically increased by incorporating two deoptimized serine codons into a single chain. Notably, we observed a negative correlation between total protein expression yield and final product purity. Taken together, our work demonstrates that incorporation of deoptimized serine codons into a single chain can significantly influence multispecific biologic pairing and enhance final product purity. Our findings align with existing literature showing that rare codon usage modulates translation kinetics and protein folding. Future investigation is warranted to enable a priori identification of the rate-limiting chain in multispecific biologics, thereby guiding strategic codon deoptimization prior to expression.

Published in Frontiers in Bioengineering and Biotechnology (predicted rank #16) · training set

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