ArticleNature communications2021
Retention time prediction using neural networks increases identifications in crosslinking mass spectrometry.
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 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 48 citations in OpenAlex.
- XL-MSDigger: a deep learning-based, versatile solution for cross-linking mass spectrometry.Nature communications · 2026Article
- Applications and Prospects of Artificial Intelligence in ProteomicsCurrent protein & peptide science · 2026Review
- Protein Secondary Structure Patterns in Short-Range Cross-Link Atlas.Angewandte Chemie (International ed. in English) · 2025Article
- Prosit-XL: enhanced cross-linked peptide identification by fragment intensity prediction to study protein interactions and structures.Nature communications · 2025Article
- Recent Applications of Artificial Intelligence and Related Technical Challenges in MALDI MS and MALDI-MSI: A Mini Review.Mass spectrometry (Tokyo, Japan) · 2025Review
- Proteome-wide non-cleavable crosslink identification with MS Annika 3.0 reveals the structure of the C. elegans Box C/D complex.Communications chemistry · 2024Article
- Proteome-scale recombinant standards and a robust high-speed search engine to advance cross-linking MS-based interactomics.Nature methods · 2024Article
- Artificial Intelligence in Metabolomics: A Current Review.Trends in analytical chemistry : TRAC · 2024Article
- Retention time dataset for heterogeneous molecules in reversed-phase liquid chromatography.Scientific data · 2024Article
- Evaluating Imide-Based Mass Spectrometry-Cleavable Cross-Linkers for Structural Proteomics Studies.JACS Au · 2024Article
- Chemical cross-linking and mass spectrometry enabled systems-level structural biology.Current opinion in structural biology · 2024Review
- Rescoring Peptide Spectrum Matches: Boosting Proteomics Performance by Integrating Peptide Property Predictors Into Peptide Identification.Molecular & cellular proteomics : MCP · 2024Review
- Recent Developments in Machine Learning for Mass Spectrometry.ACS measurement science au · 2024Review
- Instrumentation at the Leading Edge of Proteomics.Analytical chemistry · 2024Review
- AI-guided pipeline for protein-protein interaction drug discovery identifies a SARS-CoV-2 inhibitor.Molecular systems biology · 2024Article
- Generic and accurate prediction of retention times in liquid chromatography by post-projection calibration.Communications chemistry · 2024Article
- Improved Peptide Backbone Fragmentation Is the Primary Advantage of MS-Cleavable Crosslinkers.Analytical chemistry · 2022Article
- Statistical analysis of isocratic chromatographic data using Bayesian modeling.Analytical and bioanalytical chemistry · 2022Article
- Two-Dimensional Fractionation Method for Proteome-Wide Cross-Linking Mass Spectrometry Analysis.Analytical chemistry · 2022Article
- Label-free visual proteomics: Coupling MS- and EM-based approaches in structural biology.Molecular cell · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
Abstract
Crosslinking mass spectrometry has developed into a robust technique that is increasingly used to investigate the interactomes of organelles and cells. However, the incomplete and noisy information in the mass spectra of crosslinked peptides limits the numbers of protein-protein interactions that can be confidently identified. Here, we leverage chromatographic retention time information to aid the identification of crosslinked peptides from mass spectra. Our Siamese machine learning model xiRT achieves highly accurate retention time predictions of crosslinked peptides in a multi-dimensional separation of crosslinked E. coli lysate. Importantly, supplementing the search engine score with retention time features leads to a substantial increase in protein-protein interactions without affecting confidence. This approach is not limited to cell lysates and multi-dimensional separation but also improves considerably the analysis of crosslinked multiprotein complexes with a single chromatographic dimension. Retention times are a powerful complement to mass spectrometric information to increase the sensitivity of crosslinking mass spectrometry analyses.
Indexed as
Identifiers
What OpenQuestion holds
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.