Author

Aurélien F. A. Moumbock

University of FreiburgORCID 0000-0002-6034-2016Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Organism:bacteria (other)· 1 Research Focus:benchmarking· 1 Assay:cryo-EM· 1 Approach:experimental assay· 1 Approach:machine learning (classical)· 1 Research Focus:protein structure prediction· 1 Approach:statistical model· 1

Frequent Co-Authors

Stefan GüntheriD· 2 shared Albert Suades· 1 shared Ammar QaseemiD· 1 shared Andrew QuigleyiD· 1 shared Bjoern PetersiD· 1 shared David DrewiD· 1 shared Deniz Bicer· 1 shared Els PardoniD· 1 shared

Activity by Year

2020-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 19 mutual co-author links

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2 shared papersBioinformatics

Stefan GüntherORCID

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3 papers

Recent·Most cited

Structural insights into Pseudomonas aeruginosa lysine-specific uptake mechanism for extremely low pH regulation

Deniz Bicer, Rei Matsuoka, Aurélien F. A. Moumbock, Preethi Sukumar, Albert Suades, Harish Cheruvara, Andrew Quigley, David Drew, Els Pardon, Jan Steyaert, Peter J. F. Henderson, Martin Caffrey, Julia J. Griese, Emmanuel Nji

The paper reports the cryo-EM structure of Pseudomonas aeruginosa LysP bound to lysine and a nanobody, revealing its transport mechanism and inhibition by L-4-thialysine.

Nature Communications · Dec 4, 2025Peer-reviewedBiochemistry

Towards the prediction of non-peptidic epitopes

Paul F. Zierep, Randi Vita, Nina Blazeska, Aurélien F. A. Moumbock, Jason Greenbaum, Bjoern Peters, Stefan Günther

This paper presents a method to predict epitope activity of non-peptidic compounds using machine learning and the IEDB database, achieving ROC-AUC values between 0.69-0.96.

PLoS Computational Biology · Feb 18, 2022Peer-reviewedBioinformatics

Automated recognition of functional compound-protein relationships in literature

Kersten Döring, Ammar Qaseem, Michael Becer, Jianyu Li, Pankaj Mishra, Mingjie Gao, Pascal A Kirchner, Florian Sauter, Kiran K. Telukunta, Aurélien F. A. Moumbock, Philippe E Thomas, Stefan Günther

The paper develops a benchmark dataset and two machine learning models to classify compound-protein relationships in scientific abstracts as functional or non-functional.

PLoS ONE · Mar 3, 2020Peer-reviewedBioinformatics