Despite existing in hundreds of thousands and expressing significant diversity, signal peptides have largely remained unexplored in commercial protein engineering.
This is a missed opportunity as almost all therapeutic proteins and mRNA therapies contain a signal peptide that can be engineered for optimized production and functionality.
Signal peptides impact protein biogenesis on several levels
Signal peptides are short, amino acid sequences located at the N-terminus of proteins.They guide newly synthesized proteins to endoplasmic reticulum and regulate protein folding, post-translational modification and expression rate.
In short: Signal peptides have an important role in modulating protein biogenesis, and they can significantly impact the efficacy, stability, yield and safety of therapeutic proteins (1).
Why hasn’t the full potential of signal peptides been harnessed in production protocols?
For a long time, many areas in protein biologics were done through trial and error. This is the case for signal peptides, and as a result, the data on how individual signal peptides impact their target proteins is still limited. Typically, biotechnology companies only analyze a handful of signal peptides when optimizing their production.
After massive advances in machine learning and AI algorithms, the full potential of protein engineering is beginning to unfold.
Machine learning-assisted lab techniques can assess signal peptides in thousands
All proteins going through the secretory pathway have a unique pool of high-performing signal peptides.
Today, it is possible to screen and analyze simultaneously thousands of natural and synthetic signal peptides to identify the one that maximizes protein expression. This works for any protein substrate of interest – from mRNA therapies to monoclonal antibodies, and more.
The end-game is to significantly increase expression yield and quality so that time to market and costs could be reduced.
Reducing costs of protein biologics is becoming a necessity
While biologics represent therapeutic innovation, they also represent a significant cost in medicine.
For example, synthetic proteins, such as bispecific antibodies, are complex proteins that are challenging to produce. These challenges involve obtaining adequate yield, correct protein folding and post translational modification.
Industry-scale production issues also dominate other fields of biotechnology development: The expression of membrane proteins such as GRCPs, mRNA therapies, some monoclonal antibodies, and many other commercially interesting proteins.
In fact, more than 50% of recombinant protein production processes are categorized as difficult-to-express proteins, meaning that they tend to fail at the expression stage.
High throughput signal peptide engineering in protein production has the potential to enhance expression without impacting the quality of the end product (2).
References:
1) Hegde, Ramanujan S. et al. 2006. The surprising complexity of signal sequences: Trends in Biochemical Sciences. Volume 31, Issue 10, 563 – 571
2) Mascarenhaset al. Signal Peptide Optimization: Effect On Recombinant Monoclonal IgG Productivity, Product Quality And Antigen-Binding Affinity.
