Data storage and retrieval with unnatural proteins expressed via E. coli

Area of Science:

• Biotechnology
• Synthetic Biology
• Proteomics

Background:

• Proteins offer high-capacity, stable data storage but face challenges in unnatural protein expression and sequencing for retrieval.
• Existing data storage methods like DNA face limitations in stability and capacity.

Purpose of the Study:

• To develop a robust framework for protein-based digital data storage and retrieval.
• To demonstrate the feasibility of encoding, expressing, and recovering data from proteins.
• To compare protein data storage stability and security with existing methods.

Main Methods:

• Encoding digital data into amino acid sequences within collagen-like protein templates.
• Expressing data-bearing proteins using E. coli.
• Sequencing proteins via tryptic digestion and LC-MS/MS analysis for data recovery.
• Utilizing affinity tags for random access and cryptographic data protection.

Main Results:

• Successful expression of data-bearing proteins in E. coli.
• Complete data recovery from protein mixtures using LC-MS/MS analysis.
• Demonstrated superior stability of data-bearing proteins compared to DNA.
• Established random access and cryptographic protection for protein data.

Conclusions:

• Protein-based data storage is a viable and robust technology.
• This framework enables high-capacity, stable data storage with enhanced security.
• Opens new avenues in data storage, synthetic biology, and proteomics.