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Signal Peptides Generated by Attention-Based Neural Networks.

Zachary Wu¹, Kevin K Yang¹, Michael J Liszka²

¹Department of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, United States.

ACS Synthetic Biology

|July 11, 2020

Summary

Researchers developed a novel AI model to design new signal peptides (SPs) for protein secretion. These AI-generated SPs are functional and show diverse sequences, matching industrial standards.

Keywords:

Bacillus subtilis machine learning protein design secretion signal peptides

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Area of Science:

Biotechnology
Bioinformatics
Molecular Biology

Background:

Signal peptides (SPs) are short amino acid chains crucial for protein secretion in cells.
Current methods for SP identification and design are limited.

Purpose of the Study:

To develop an AI-driven method for generating novel and functional signal peptide sequences.
To experimentally validate the efficacy of AI-generated SPs in protein secretion.

Main Methods:

Trained an attention-based neural network (Transformer model) on a comprehensive dataset of SP sequences from Swiss-Prot.
Appended model-generated SPs to enzymes expressed in *Bacillus subtilis*.
Experimentally assessed the secreted activity of the modified enzymes.

Main Results:

The Transformer model successfully generated diverse and functional signal peptide sequences.
AI-generated SPs demonstrated secreted enzyme activity competitive with established industrial SPs.
Generated SPs exhibited significant sequence diversity, with identities as low as 58% to known native SPs.

Conclusions:

AI, specifically the Transformer model, offers a powerful tool for designing functional signal peptides.
This approach can enhance protein secretion efficiency in industrial applications, such as in *Bacillus subtilis*.
The generated SPs provide novel alternatives to native sequences, expanding the toolkit for protein engineering.