Generalizable direct protein sequencing with InstaNexus
preprint · bioRxiv · 2025
| Date | 2025-07-25 |
| Type | preprint |
| Venue | bioRxiv |
| Publisher | Cold Spring Harbor Laboratory |
| Contribution | post-processor |
| DOI | 10.1101/2025.07.25.666861 |
| Citations (OpenAlex) | 0 |
Peer-reviewed version: Generalizable Direct Protein Sequencing With InstaNexus (2026-03-02, Molecular & Cellular Proteomics)
Abstract
Protein-based therapeutics, such as antibodies and nanobodies, are not encoded in reference genomes, challenging their accurate characterization via standard proteomics. Current methods rely on indirect inference, fragmented outputs, and labor-intensive workflows, which hinder functional insights and routine application. Here, we present a generalizable, end-to-end workflow for direct protein sequencing, combining streamlined sample preparation, AI-driven de novo peptide sequencing, and tailored assembly to reconstruct contiguous protein sequences. A novel composite scoring framework prioritises longer assemblies and coverage, enhancing accuracy and reducing ambiguity. Validation across diverse protein modalities demonstrates its utility and ability to robustly sequence functionally critical regions of selected proteins. This workflow represents an advance in precision proteomics with promising applications in therapeutic discovery, immune profiling, and protein science.
Methods and tools
- InstaNexus: End-to-end workflow for reference-free sequencing of full-length protein therapeutics. Multi-protease digestion yields overlapping peptides, InstaNovo sequences them de novo and Winnow rescores, then greedy overlap or de Bruijn graph assembly (default k=7, min overlap 3) reconstructs contigs ranked by a composite score over coverage, N50, scaffold count and identity. Validated on nanobodies, monoclonal antibodies and de novo mini-binders.
- InstaNovo: Knapsack beam search
Cites (7)
- InstaNovo-P: A de novo peptide sequencing model for phosphoproteomics (2025) crossref
- InstaNovo enables diffusion-powered de novo peptide sequencing in large-scale proteomics experiments (2025) crossref
- π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing (2025) crossref
- Deep Learning Methods for De Novo Peptide Sequencing (2024) crossref
- Sequence-to-sequence translation from mass spectra to peptides with a transformer model (2024) crossref
- Highly Robust de Novo Full-Length Protein Sequencing (2021) crossref
- PEAKS: powerful software for peptide de novo sequencing by tandem mass spectrometry (2003) crossref