ArticleNature communications2021
Deep learning the collisional cross sections of the peptide universe from a million experimental values.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 80 papers.
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Who cites it
80 citing papers in PubMed.
- Artificial Intelligence and Machine Learning in Mass Spectrometry-Based Proteomics and Metabolomics.Methods in molecular biology (Clifton, N.J.) · 2027Article
- Evaluation of TIMS-derived peptide mobility measurements: calibration strategy and MS1/MS2 discrepancies.Analytical and bioanalytical chemistry · 2026Article
- Foundation model enables interpretable open and error-tolerant searching for mass spectrometry-based proteomics.Bioinformatics (Oxford, England) · 2026Article
- EasySCP unveils extensive liver zonation at single-cell proteomics resolution.Nature communications · 2026Article
- Bimodal Peptide Collision Cross Section Distribution Reflects Two Stable Conformations in the Gas Phase.Journal of proteome research · 2026Article
- Adapting general representations of pretrained vision foundation models to seismic understanding.Communications engineering · 2026Article
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- Advances in Mass Spectrometry Instrumentation and Methodology for Analysis of Alternative Protein Isoforms.Journal of mass spectrometry : JMS · 2026Review
- XL-MSDigger: a deep learning-based, versatile solution for cross-linking mass spectrometry.Nature communications · 2026Article
- PepFoundry: A Pipeline for Building Machine-Learning Ready Representations of Nonstandard Peptides Containing Cycles, Non-natural Residues, Polymer Units, and More.Journal of chemical information and modeling · 2026Article
- Large Data Set Analysis Reveals Structural Origin of Peptide Collisional Cross Section Bimodal Behavior.Journal of the American Society for Mass Spectrometry · 2026Article
- How to Use Quantum Chemistry for Analyzing Mass Spectrometry Data.Mass spectrometry (Tokyo, Japan) · 2026Review
- Applications and Prospects of Artificial Intelligence in ProteomicsCurrent protein & peptide science · 2026Review
- AIRPred: A Deep Learning Model Predictor for Peptide Intensity Ratios in Cross-Linking Mass Spectrometry Improves Cross-Link Spectrum Matching.Analytical chemistry · 2025Article
- Trapped Ion Mobility Improves Annotation Accuracy in LC-HRMS Screening Applications for Exposomics.Analytical chemistry · 2025Article
- Comparative Multi-Omics Analysis and Antitumor Activity ofJournal of microbiology and biotechnology · 2025Article
- The impact of exercise on skeletal muscle proteome of prediabetic subjects analyzed with data independent mass spectrometry.Scientific reports · 2025Article
- Assigning Peptide Structure from Ion-Mobility Mass Spectrometry Collision Cross Section Data.Journal of the American Society for Mass Spectrometry · 2025Article
- Collisional Cross-Section Prediction for Multiconformational Peptide Ions with IM2Deep.Analytical chemistry · 2025Article
20 more citing papers are in PubMed but not listed here.
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The size and shape of peptide ions in the gas phase are an under-explored dimension for mass spectrometry-based proteomics. To investigate the nature and utility of the peptide collisional cross section (CCS) space, we measure more than a million data points from whole-proteome digests of five organisms with trapped ion mobility spectrometry (TIMS) and parallel accumulation-serial fragmentation (PASEF). The scale and precision (CV < 1%) of our data is sufficient to train a deep recurrent neural network that accurately predicts CCS values solely based on the peptide sequence. Cross section predictions for the synthetic ProteomeTools peptides validate the model within a 1.4% median relative error (R > 0.99). Hydrophobicity, proportion of prolines and position of histidines are main determinants of the cross sections in addition to sequence-specific interactions. CCS values can now be predicted for any peptide and organism, forming a basis for advanced proteomics workflows that make full use of the additional information.
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