Identification of Unknown Biological Toxin Proteins Using Mass Spectrometry: A Case Study on De Novo Sequencing of Ricin
peer-reviewed · Toxins · 2025
| Date | 2025-11-18 |
| Type | peer-reviewed |
| Venue | Toxins |
| Publisher | MDPI |
| Contribution | downstream-application |
| DOI | 10.3390/toxins17110564 |
| Citations (OpenAlex) | 1 |
| Venue 2-year citedness | 3.89 |
Abstract
BACKGROUND: The rapid and reliable identification of unknown or highly variable biological toxin proteins, such as the potent Ricin toxin, remains a critical challenge in biodefense and public security. METHODS: To address this, we developed a Heuristic De Novo Sequencing (HDPS) strategy, which combines multiple enzymatic and microwave-assisted acid hydrolysis to generate diverse peptides, followed by a two-stage assembly process integrating de novo sequencing with homology-based database searching for robust error correction. RESULTS: When applied to Ricin, this approach achieved 100% sequence coverage for both its A and B chains, with amino acid-level accuracies of 98.13% and 98.47%, respectively, and successfully corrected potential sequencing ambiguities. CONCLUSIONS: These results demonstrate that HDPS is a highly accurate and effective method for the de novo sequencing of full-length proteins, making it particularly valuable for characterizing unknown or mutated toxins in the absence of comprehensive reference databases.
Methods and tools
- HDPS: Heuristic two-round sequence assembly strategy combining multi-enzyme and microwave-assisted acid hydrolysis, pNovo de novo peptide sequencing, and pFind homology database search with k-mer graph assembly and majority-vote error correction; achieved 100% sequence coverage and >98% amino-acid accuracy on full-length Ricin toxin A and B chains without a reference sequence, outperforming ALPS.
- pNovo 3: Learning-to-rank + pDeep
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