Generalizable Direct Protein Sequencing With InstaNexus

Area of Science:

• Proteomics
• Biotechnology
• Computational Biology

Background:

• Accurate protein sequencing is crucial for biological research and therapeutic development.
• Protein-based therapeutics like antibodies are not genomically encoded, complicating their characterization using standard proteomics.
• Existing methods for protein characterization are indirect, fragmented, labor-intensive, and limit functional insights.

Purpose of the Study:

• To develop a generalizable, end-to-end workflow for direct protein sequencing.
• To enable accurate characterization of protein-based therapeutics and other non-genomic proteins.
• To improve functional insights and routine application of protein analysis.

Main Methods:

• Streamlined sample preparation for direct protein sequencing.
• AI-driven de novo peptide sequencing for fragment analysis.
• Tailored assembly algorithms and a novel composite scoring framework for sequence reconstruction.

Main Results:

• Successfully reconstructed contiguous protein sequences from diverse protein modalities.
• Demonstrated robust sequencing of functionally critical protein regions.
• The workflow enhances accuracy and reduces ambiguity in protein sequence determination.

Conclusions:

• The presented workflow offers a significant advance in precision proteomics.
• It provides a generalizable and accurate method for direct protein sequencing.
• Potential applications include therapeutic discovery, immune profiling, and broader protein science.