De novo peptide sequencing from mirror-protease mass spectra, reading paired complementary spectra to recover near-complete fragment ion coverage.
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De novo peptide sequencing reads an amino acid sequence directly out of a tandem mass spectrum, with no protein database to match against. It is the only route to peptides no database contains: antibodies from an immunized animal, neoantigens, venom proteins, and metaproteomes. The obstacle is that one spectrum rarely carries every fragment ion needed to read a sequence off unambiguously, and one protease rarely digests a proteome fully.
DiNovo, from Yan Fu's group at the Chinese Academy of Sciences with collaborators at Shandong University of Technology and the Beijing Institute of Lifeomics, attacks the problem from the experiment rather than the model. Mirror proteases cut on opposite sides of the same residue: trypsin and LysargiNase both act at lysine and arginine, Lys-C and Lys-N at lysine. Digesting one sample with both yields mirror peptides sharing an identical interior sequence but carrying that residue at opposite termini. Under higher-energy collisional dissociation the trypsin product yields rich y ions and the LysargiNase product rich b ions, filling each other's gaps.
DiNovo makes this usable end to end: it recognizes which spectra form mirror pairs, sequences them with either a deep network or a graph algorithm, and estimates a false discovery rate on the result. Its neural sequencer, MirrorNovo, is the first deep learning model built for mirror spectra, in the lineage of single-spectrum sequencers such as DeepNovo, PointNovo, and Casanovo.
MirrorNovo matches observed peaks in both spectra against 18 theoretical fragment ion types: singly and doubly charged b, y, and a ions, their neutral-loss forms, and six internal ion types. A T-Net of three 1D convolutional layers extracts peak features, a GRU captures dependencies across peaks, three fully connected layers follow, and a softmax emits probabilities over 20 amino acids with leucine and isoleucine merged. Decoding is iterative, using beam search, a knapsack constraint, and bidirectional prediction, stopping when prefix and precursor masses agree within 20 ppm. Training used mirror pairs annotated at 1% FDR from Vero, MC2155, and human testis samples.
Across eight E. coli and yeast datasets, mirror spectra reached average fragment ion coverage of 98.4% and 98.0%, against 90.2% and 89.7% for single spectra. Against single-protease sequencers — PEAKS, pNovo3, PointNovo, and Casanovo, the last trained on 30 million MassIVE-KB spectra — DiNovo returned 110.2% to 195.3% more high-confidence peptides, 35.9% to 74.2% higher amino acid coverage, and 10.3% to 19.9% higher protein coverage. Against trypsin alone it sequenced 458.9% and 509.2% more high-confidence peptides. At matched 1% FDR it recovered 85.4% and 73.7% of the amino acids and 92.4% and 91% of the proteins found by the pFind search engine.
The beneficiaries are groups sequencing proteins databases do not cover — antibody characterization, immunopeptidomics, venomics, and metaproteomics — where completeness matters more than throughput. Target-decoy mapping matters more broadly: putting de novo results on the same FDR footing as a database search lets de novo sequencing be reported as a primary identification method rather than a fallback. Adopting it does require changing the wet-lab protocol: a sample must be split and digested with both members of each protease pair.
DiNovo is the first system to support the full mirror-protease workflow, from pairing spectra through sequencing to quality control — Lys-Sequencer was never released and pNovoM shipped without the pairing step that makes the strategy work. MirrorNovo trained on far fewer spectra than Casanovo or π-PrimeNovo because annotated mirror pairs are scarce, leaving clear headroom as such data accumulates. Pairing also scales quadratically in spectrum count, needing tighter retention-time bounds or spectral clustering on large runs. The source is archived on Zenodo under LGPL-3.0, though the README also limits DiNovo to academic use, a restriction LGPL-3.0 itself does not impose. The trained MirrorNovo weights ship only inside the 570 MB Windows standalone package, not the repository or the Zenodo archive, and the deep sequencer needs a GPU.
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