ArticleNature communications2026
DiNovo enables high-coverage and high-confidence de novo peptide sequencing via mirror proteases and deep learning.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- DiNovo enables high-coverage and high-confidence de novo peptide sequencing via mirror proteases and deep learning.Nature communications · 2026Article
- Micropeptides Encoded by Noncoding RNAs: Biological Functions and Roles in Diseases.Research (Washington, D.C.) · 2025Review
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Authors and funding
18 authors.
Funding
Abstract
Despite the recent advancements driven by deep learning, de novo peptide sequencing is still constrained by incomplete peptide fragmentation and insufficient protein digestion in current single protease-based proteomic experiments. Here, we present a software system, named DiNovo, for high-coverage and high-confidence de novo peptide sequencing by leveraging the complementarity of mirror proteases. DiNovo is empowered by several innovative algorithms, including a mirror-spectra recognition algorithm independent of pre-sequencing, two sequencing algorithms based on deep learning and graph theory, respectively, and target-decoy mapping, a method for sequencing result evaluation free of prior peptide identification. Compared with the trypsin protease used alone, DiNovo using two pairs of mirror proteases leads to two to three times high-confidence amino acids sequenced. Compared with previous single-protease de novo sequencing algorithms, DiNovo achieves much higher sequence coverage. DiNovo also shows great potential as a practical and powerful alternative to database search for peptide identification with quality control.
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