Author

Erik Riedel

Technical University of Munich

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Biochemistry· 1 Bioinformatics· 1 Molecular Biology· 1

Signature Assays, Organisms & Methods

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Organism:human· 3 Assay:proteomics· 3 Approach:experimental assay· 2 Organism:Arabidopsis· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Organism:mouse· 1 Research Focus:protein folding & dynamics· 1

Frequent Co-Authors

Chien‐Yun LeeiD· 3 shared Sophia LaposchaniD· 3 shared Mathias WilhelmiD· 2 shared Rebecca Meelker GonzáleziD· 2 shared Wassim GabrieliD· 2 shared Andrew FlatleyiD· 1 shared Anna Fürst· 1 shared Bernhard KüsteriD· 1 shared

Activity by Year

2025-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 34 mutual co-author links

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3 shared papersMolecular Biology

Chien‐Yun LeeORCID

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3 papers

Recent·Most cited

Development of Motif-Specific Monoclonal Antibodies for Global Protein Citrullination Detection with Minimal Cross-Reactivity to Homocitrullination

Sophia Laposchan, Erik Riedel, Andrew Flatley, Selina Pasquero, Regina Feederle, Chien‐Yun Lee

This paper develops monoclonal antibodies specific for citrullination, showing high sensitivity and minimal cross-reactivity with homocitrullination.

Cell Reports Methods · Mar 27, 2026Peer-reviewedMolecular Biology

High-throughput chemical proteomics workflow for profiling protein citrullination dynamics

Rebecca Meelker González, Sophia Laposchan, Erik Riedel, Anna Fürst, Naomi O'Sullivan, Wassim Gabriel, Mathias Wilhelm, Percy Alexander Knolle, Guillaume Médard, Bernhard Küster, Chien‐Yun Lee

The paper develops a chemical proteomics workflow to profile protein citrullination dynamics, achieving high sensitivity and throughput in detecting citrullinated peptides across biological systems.

Nature Communications · Feb 21, 2026Peer-reviewedBiochemistry

Deep Learning Enhances Precision of Citrullination Identification in Human and Plant Tissue Proteomes

Wassim Gabriel, Rebecca Meelker González, Sophia Laposchan, Erik Riedel, Gönül Dündar, Brigitte Poppenberger, Mathias Wilhelm, Chien‐Yun Lee

The paper introduces a deep learning model to improve citrullination site identification in proteomes, achieving high precision and sensitivity in human and plant tissues.

Molecular & Cellular Proteomics · Feb 5, 2025Peer-reviewedBioinformatics