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# De novo peptide sequencing

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  1. bioRxiv

    Mass-spec sequencing stops relying on spectra alone: PepPr adds a proteome prior and lifts median peptide recall by 15.8 points across nine species

    PepPr uses a peptide language model trained on a reference proteome as a prior, fused with spectral evidence at every step of autoregressive decoding rather than applied as post hoc correction. On the nine-species dataset with Casanovo as the base model, median peptide recall rose by 15.8 percentage points, improving all nine species; on single-amino-acid variants and antibodies, variant identification was not suppressed, and recall of peptides spanning the variable-constant boundary rose from 0.23 to 0.68.

    Technical University of Munich
    Samuel Khan（First author）Julien Gagneur（Corresponding author）
    PepPrPeptide priorDe novo peptide sequencingMass spectrometry
  2. bioRxiv

    π-SPECFormer learns from 291 million mass spectra and flags six mutation peptides missing from databases in lung adenocarcinoma

    Conventional database searching assigns high-confidence peptides to only about 20% to 31% of acquired spectra, leaving the rest unread. π-SPECFormer was pre-trained on 291 million tandem mass spectra in two stages: self-supervised learning of fragmentation patterns, then refinement with peptide–spectrum match groupings. It beat task-specific baselines in de novo sequencing, spectrum clustering and modification detection, and in 103 lung adenocarcinoma samples it produced nine high-confidence mutation peptides, six of them absent from the public databases examined.

    Tsinghua UniversityPeng Cheng LaboratoryGuangzhou LaboratoryThe Hong Kong University of Science and Technology (Guangzhou)Westlake University
    Tianze Ling（First author）Cheng Chang（Corresponding author）
    π-SPECFormerProteomic dark matterDe novo peptide sequencingContrastive Learning
  3. 粒曼生物（ELEM Bio）

    Proteomics gets a billion-scale spectral corpus: MassNet holds 548 million PSMs and leads de novo sequencing across 15 species

    Proteomics has plenty of public mass spectra, but mixed formats, missing metadata and a human-heavy species mix make them hard to train deep models on. MassNet gathers DDA data from 648 projects across 35 species, holds 548 million PSMs, and stores spectra in the tensor-based MSDT format with traceable metadata. On a benchmark spanning 15 species and 9.66 million PSMs, the XuanjiNovo model trained on this corpus reaches peptide recall of 0.45 on human and 0.68 on mouse data, and 0.25 on tomato, above the listed de novo sequencing models.

    MassNetXuanjiNovoMass Spectrometry DDA Tensor (MSDT)De novo peptide sequencing
  4. Nature Methods

    A billion-scale mass spectral corpus arrives: 1.54 billion spectra across 35 species give de novo sequencing models a shared starting point

    MassNet is an AI-ready mass spectrometry proteomics corpus of about 1.54 billion tandem mass spectra from 648 projects across 35 species, paired with a columnar tensor storage format called MSDT. Previously, mass spectra were scattered across heterogeneous formats such as MGF and mzML, with peak lists stored separately from peptide annotations and coverage skewed toward human samples. XuanjiNovo, a non-autoregressive model trained on this corpus, improves over state-of-the-art methods by 38.35% to 143.57% on human Astral data and also supports retention time prediction and PSM rescoring.

    MassNetXuanjiNovoMass Spectrometry DDA Tensor (MSDT)De novo peptide sequencingCurriculum Learning

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