Cumulating MS Signal enables polyclonal antibody analysis
preprint · bioRxiv · 2025
| Date | 2025-04-05 |
| Type | preprint |
| Venue | bioRxiv |
| Publisher | Cold Spring Harbor Laboratory |
| Contribution | algorithm |
| DOI | 10.1101/2025.03.31.645874 |
| Citations (OpenAlex) | 0 |
Abstract
Unraveling the complexities of protein systems via Mass Spectrometry (MS), particularly polyclonal antibodies, demands innovative analytical strategies. Here, we introduce the cumulative MS score (cMS), a novel mathematical framework that transcends traditional spectrum-matching, integrating MS evidence across multiple sample injections to achieve robust de novo peptide sequencing annotation. This approach, shifting from isolated spectrum analysis to a holistic MS signal-based methodology, was rigorously evaluated and validated across diverse sample types and experimental conditions. We applied this framework to characterize a complex polyclonal antibody mixture of Streptococcus pyogenes M1 protein binders derived from intravenous immunoglobulin (IVIG), revealing predominant variable heavy (VH) and light (VL) chain subgroups consistent with established genetic studies. Furthermore, we successfully identified conserved complementarity-determining region (CDR) features and predicted stable antibody-antigen interactions through molecular dynamics simulations, demonstrating the method’s potential for dissecting intricate antibody responses. This work establishes a powerful alternative to conventional tandem mass spectrometry MS/MS data analysis, enabling deeper insights into protein systems and paving the way for targeted therapeutic development.
Methods and tools
- Cumulative MS score (cMS): Scoring framework that accumulates evidence for a residue across repeated injections rather than scoring one spectrum at a time, to make de novo annotation robust enough to read VH/VL and CDR sequences out of a POLYCLONAL antibody mixture.
Data used
- Multienzyme deep learning models improve peptide de novo sequencing by mass spectrometry proteomics (as deposited) · PXD037803
Cites (25)
- PepGo: a deep learning and tree search-based model for de novo peptide sequencing (2025) both
- A transformer-based semi-autoregressive framework for high-speed and accurate de novo peptide sequencing (2025) both
- NovoRank: Refinement for De Novo Peptide Sequencing Based on Spectral Clustering and Deep Learning (2025) both
- π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing (2025) crossref
- Deep Learning Methods for De Novo Peptide Sequencing (2024) both
- De Novo Sequencing of Peptides from Tandem Mass Spectra and Applications in Proteogenomics (2024) both
- De novo protein sequencing of antibodies for identification of neutralizing antibodies in human plasma post SARS-CoV-2 vaccination (2024) both
- Accounting for Digestion Enzyme Bias in Casanovo (2024) crossref
- A Handle on Mass Coincidence Errors in De Novo Sequencing of Antibodies by Bottom-up Proteomics (2024) both
- Sequence-to-sequence translation from mass spectra to peptides with a transformer model (2024) both
- PowerNovo: de novo peptide sequencing via tandem mass spectrometry using an ensemble of transformer and BERT models (2024) both
- π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing (2024) semanticscholar
- Multimodal Mass Spectrometry Identifies a Conserved Protective Epitope in S. pyogenes Streptolysin O (2024) both
- Bidirectional de novo peptide sequencing using a transformer model (2024) both
- Transformer-Based De Novo Peptide Sequencing for Data-Independent Acquisition Mass Spectrometry (2023) both
- Multienzyme deep learning models improve peptide de novo sequencing by mass spectrometry proteomics (2023) both
- Comprehensive evaluation of peptide de novo sequencing tools for monoclonal antibody assembly (2023) both
- pNovo 3: precise de novo peptide sequencing using a learning-to-rank framework (2019) both
- DeepNovoV2: Better de novo peptide sequencing with deep learning (2019) both
- Precision De Novo Peptide Sequencing Using Mirror Proteases of Ac-LysargiNase and Trypsin for Large-scale Proteomics (2019) both
- De novo peptide sequencing by deep learning (2017) both
- Evaluating de novo sequencing in proteomics: already an accurate alternative to database-driven peptide identification? (2017) both
- pNovo+: De Novo Peptide Sequencing Using Complementary HCD and ETD Tandem Mass Spectra (2013) both
- PepNovo: de novo peptide sequencing via probabilistic network modeling (2005) both
- PEAKS: powerful software for peptide de novo sequencing by tandem mass spectrometry (2003) both