Signal peptides are rarely considered in mRNA therapy development and optimization. Advances in high throughput signal peptide analysis and computational models are creating new possibilities for smarter mRNA design.
What “the perfect match” can mean for mRNA development
Optimizing protein production in mRNA development is challenging due to overlapping cellular processes related to stability, translation, protein folding, and secretion, as well as protein-specific optimization requirements.
Strategies to increase yield and stability include codon optimization, modulation of untranslated regions, 5’ cap and poly(A) tail modifications, chemical modifications, delivery system enhancers, ribosome recruitment, translation enhancers and more.
But one remains unexplored: Signal peptides – small but mighty elements in protein engineering – are a powerful strategy to improve protein production.
Signal peptides are highly diverse, and why some function better than others in a specific context remains unclear (1). Finding the“perfect match” can be a laborious process that too often relies on trial and error.
The solution? Testing thousands of signal peptide-substrate combinations simultaneously helps identify high-performing signal peptides that boost protein production.
Why achieving a higher protein output in mRNA is important
1. Improve therapeutic effect. Incorporating a signal peptide into the antigen sequence can help with proper antigen display or secretion, which is important for more effective and targeted immune responses.
2. Lower doses without losing effect. If an mRNA molecule can produce more protein, less vaccine is needed, reducing costs and potential side effects.
3. Faster and longer therapeutic effects. Increased protein production can accelerate therapeutic effects and prolong efficacy.
4. Beyond vaccines: In enzyme replacement therapies, increased protein production can improve symptom management in genetic disorders. Using mRNA could reduce reliance on gene therapy. For patients, this reduces the risk of permanent genetic changes and, with proper cloaking of the vesicles in which mRNA is introduced into cells, immune reactions to vectors.
How signal peptides navigate the ER for enhanced mRNA therapeutic output
Signal peptides guide the ribosome-mRNA complex to the endoplasmic reticulum (ER), enabling co-translational insertion of the translated target protein into the ER lumen. Their role is not, however, limited to this guiding task as they also play a significant role in regulating target protein’s folding and post-translational modifications (2-3). Signal peptides affect the biogenesis of the target protein at many different stages all the way from the start of the protein’s translation to its final folding and processing steps.
When it comes to enhancing mRNA-based therapies, signal peptide engineering can increase the immunogenicity of mRNA vaccines by optimizing antigen expression, secretion, and presentation and can assist mRNA-delivery-based enzyme-replacement therapies by boosting the cellular expression of the functional enzyme.
The devil you know: Why usually only a handful of signal peptides are analyzed
The biotechnology industry has only scratched the surface in using signal peptides to optimize protein production. It is common to test a handful of familiar signal peptides – even if the selected signal peptides are mediocre, and not top performers.
The technical challenge of analyzing signal peptides partly explains why they continue to be underutilized in the biotechnology industry.
What’s more: Suboptimal signal peptides can lead to low expression or misfolding, potentially acting as a suppressor of immune activation rather than a trigger in mRNA-based vaccines, and nullify the effectiveness of mRNA-based enzyme-replacement therapy.
High throughput in numbers: Going beyond manual selection
- Avenue Biosciences’ technology analyzes over 5,500 signal peptides simultaneously to identify the best performing candidates.
- The high throughput strategy builds unique datasets on how signal peptides interact with the target substrate.
- The technology uses machine learning to generate synthetic signal peptides, expanding the library beyond naturally occurring signal peptides.
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References:
1) Wilkinson, P., Jackson, B., Fermor, H. et al. A new mRNA structure prediction-based approach to identifying improved signal peptides for bone morphogenetic protein 2. BMC Biotechnol 24, 34(2024).
2) McCaul, Nicholas et al. Intramolecular quality control:HIV-1 envelope gp160 signal-peptide cleavage as a functional folding checkpoint, Cell Reports, Volume 36, Issue 9 (2021).
3) Sha Sun, Xia Li, Malaiyalam Mariappan; Signal sequences encode information for protein folding in the endoplasmic reticulum. J Cell Biol (2023)
