ArticleNature methods2019
High-quality MS/MS spectrum prediction for data-dependent and data-independent acquisition data analysis.
Article in Nature methods, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 117 papers.
What it found
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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
117 citing papers in PubMed.
- Artificial Intelligence and Machine Learning in Mass Spectrometry-Based Proteomics and Metabolomics.Methods in molecular biology (Clifton, N.J.) · 2027Article
- MassNet: billion-scale AI-ready mass spectral corpus empowering scalable deep learning in proteomics.Nature methods · 2026Article
- Article
- Deep Learning Prediction of O-Glycopeptide Tandem Mass Spectra Enhances O-Glycoproteomics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Bimodal Peptide Collision Cross Section Distribution Reflects Two Stable Conformations in the Gas Phase.Journal of proteome research · 2026Article
- Integration of alternative fragmentation techniques into standard LC-MS workflows using a single deep learning model enhances proteome coverage.Nature methods · 2026Article
- XL-MSDigger: a deep learning-based, versatile solution for cross-linking mass spectrometry.Nature communications · 2026Article
- Better Inputs, Better Learning: A Peptide Embedding Tutorial for Proteomic Mass Spectrometry.Journal of proteome research · 2026Article
- The State of Peptide Detectability in Computational Proteomics and Guidelines for AI Applications.Computational and structural biotechnology journal · 2026Article
- Applications and Prospects of Artificial Intelligence in ProteomicsCurrent protein & peptide science · 2026Review
- Less missing values-evaluation of proteomics workflows for the quantification of (small) proteins.microLife · 2026Article
- Carafe enables high quality in silico spectral library generation for data-independent acquisition proteomics.Nature communications · 2025Article
- Artificial Intelligence and the Evolving Landscape of Immunopeptidomics.Proteomics. Clinical applications · 2025Review
- FastSpel: A Method for Fast Spectral Library Generation.Journal of proteome research · 2025Article
- DeepSecMS Advances DIA-Based Selenoproteome Profiling Through Cys-to-Sec Proxy Training.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- HLA Ligand Atlas DIA: extending the benign immunopeptidomics resource with increased sensitivity through data-independent acquisition mass spectrometry.Journal for immunotherapy of cancer · 2025Article
- Carafe enables high qualitybioRxiv : the preprint server for biology · 2025Article
- The Future of a Myriad of Accelerated Biodiscoveries Lies in AI-Powered Mass Spectrometry and Multiomics Integration.Journal of mass spectrometry : JMS · 2025Article
- Recombinant Protein Spectral Library (rPSL) DIA-MS method improves identification and quantification of low-abundance cancer-associated and kynurenine pathway proteins.Communications chemistry · 2025Article
- Peptide Property Prediction for Mass Spectrometry Using AI: An Introduction to State of the Art Models.Proteomics · 2025Review
57 more citing papers are in PubMed but not listed here.
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Peptide fragmentation spectra are routinely predicted in the interpretation of mass-spectrometry-based proteomics data. However, the generation of fragment ions has not been understood well enough for scientists to estimate fragment ion intensities accurately. Here, we demonstrate that machine learning can predict peptide fragmentation patterns in mass spectrometers with accuracy within the uncertainty of measurement. Moreover, analysis of our models reveals that peptide fragmentation depends on long-range interactions within a peptide sequence. We illustrate the utility of our models by applying them to the analysis of both data-dependent and data-independent acquisition datasets. In the former case, we observe a q-value-dependent increase in the total number of peptide identifications. In the latter case, we confirm that the use of predicted tandem mass spectrometry spectra is nearly equivalent to the use of spectra from experimental libraries.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.