# Mass spectrometry

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bioRxiv Oct 28, 2026
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.
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）
PepPr Peptide prior De novo peptide sequencing Mass spectrometry
medRxiv Sep 25, 2026
Ovarian mass triage without a fixed biomarker panel: adaptive plasma proteomics reaches a summary AUC of 0.835 across five validation cohorts
Pre-operative triage of adnexal masses has relied on CA-125, HE4 and ultrasound O-RADS, all with limited sensitivity and specificity. ADAPT-MS instead retrains a classifier for each sample on the proteins actually measured, reading discovery-mode plasma mass spectrometry data directly. Across 623 samples in five independent validation cohorts the classifier reached a summary AUC of 0.835, comparable to O-RADS alone, and rose to 0.922 when combined with O-RADS.
Pre-operative triage of adnexal masses has relied on CA-125, HE4 and ultrasound O-RADS, all with limited sensitivity and specificity. ADAPT-MS instead retrains a classifier for each sample on the proteins actually measured, reading discovery-mode plasma mass spectrometry data directly. Across 623 samples in five independent validation cohorts the classifier reached a summary AUC of 0.835, comparable to O-RADS alone, and rose to 0.922 when combined with O-RADS.
University of Chicago
Johannes Müller（First author）Ernst R. Lengyel（Corresponding author）
ADAPT-MS Adnexal mass malignancy classification Mass spectrometry Ovarian cancer
arXiv Sep 11, 2026
GRACE predicts collision cross sections with geometric residual and early-fusion adduct conditioning, reaching 1.67%, 2.11%, and 2.36% mean percentage difference on random, scaffold, and adduct-sensitive splits
GRACE is a 3D collision cross section (CCS) predictor that adapts a pretrained molecular geometry encoder through geometric residual adduct conditioning via early fusion; on over 9,000 experimental molecule-adduct CCS records it achieves the best mean percentage difference among the evaluated learned models on random, scaffold, and adduct-sensitive splits (1.67%, 2.11%, and 2.36% respectively), shows consistently lower error across four independent external test sets, and attains the lowest mean percent difference against four previously reported physics-based workflows on a held-out set.
GRACE is a 3D collision cross section (CCS) predictor that adapts a pretrained molecular geometry encoder through geometric residual adduct conditioning via early fusion; on over 9,000 experimental molecule-adduct CCS records it achieves the best mean percentage difference among the evaluated learned models on random, scaffold, and adduct-sensitive splits (1.67%, 2.11%, and 2.36% respectively), shows consistently lower error across four independent external test sets, and attains the lowest mean percent difference against four previously reported physics-based workflows on a held-out set.
Cleveland Clinic IBM (United States)
Parthasarathy Suryanarayanan（First author）
GRACE Geometric Residual Adduct Conditioning Early Fusion Residual Learning Mass spectrometry
Nature News Sep 21, 2026
Map of brain 'microproteins' could offer new clues to Alzheimer's disease
Combining mass spectrometry, RNA sequencing and ribosomal profiling on 608 post-mortem dorsolateral prefrontal cortex samples from individuals with and without Alzheimer's disease, the study identified 4,321 microproteins (3,217 of them not previously characterized in the UniProtKB/Swiss-Prot standard human protein catalogue), applied a deep-learning model to rank the mass-spectrometry identification confidence of 3,001 of them with 1,067 rated high confidence, and found dozens of microproteins with altered expression in people with Alzheimer's disease, producing what is described as the largest atlas of microproteins in Alzheimer's disease made so far and making the dataset publicly available.
Combining mass spectrometry, RNA sequencing and ribosomal profiling on 608 post-mortem dorsolateral prefrontal cortex samples from individuals with and without Alzheimer's disease, the study identified 4,321 microproteins (3,217 of them not previously characterized in the UniProtKB/Swiss-Prot standard human protein catalogue), applied a deep-learning model to rank the mass-spectrometry identification confidence of 3,001 of them with 1,067 rated high confidence, and found dozens of microproteins with altered expression in people with Alzheimer's disease, producing what is described as the largest atlas of microproteins in Alzheimer's disease made so far and making the dataset publicly available.
Microproteins Mass spectrometry Ribosomal profiling RNA Sequencing

You’re all caught up

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

Ovarian mass triage without a fixed biomarker panel: adaptive plasma proteomics reaches a summary AUC of 0.835 across five validation cohorts

GRACE predicts collision cross sections with geometric residual and early-fusion adduct conditioning, reaching 1.67%, 2.11%, and 2.36% mean percentage difference on random, scaffold, and adduct-sensitive splits

Map of brain 'microproteins' could offer new clues to Alzheimer's disease