Pre-trained protein language model for codon optimization
This work addresses the problem of improving protein yield and immune response in mRNA vaccines for applications like infectious disease prevention, representing an incremental advance by adapting existing language models to a specific domain.
The authors tackled codon optimization for mRNA vaccine design by using a pre-trained protein language model to generate optimized open reading frames (ORFs), which outperformed natural sequences in computational metrics for stability and expression and showed enhanced performance against benchmark ORFs for SARS-CoV-2 and varicella-zoster virus antigens.
Motivation: Codon optimization of Open Reading Frame (ORF) sequences is essential for enhancing mRNA stability and expression in applications like mRNA vaccines, where codon choice can significantly impact protein yield which directly impacts immune strength. In this work, we investigate the use of a pre-trained protein language model (PPLM) for getting a rich representation of amino acids which could be utilized for codon optimization. This leaves us with a simpler fine-tuning task over PPLM in optimizing ORF sequences. Results: The ORFs generated by our proposed models outperformed their natural counterparts encoding the same proteins on computational metrics for stability and expression. They also demonstrated enhanced performance against the benchmark ORFs used in mRNA vaccines for the SARS-CoV-2 viral spike protein and the varicella-zoster virus (VZV). These results highlight the potential of adapting PPLM for designing ORFs tailored to encode target antigens in mRNA vaccines.