Author

F. Emil Thomasen

University of CopenhagenORCID 0000-0002-2096-4873Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2 Biophysics· 2

Signature Assays, Organisms & Methods

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Research Focus:protein structure prediction· 3 Approach:deep learning· 2 Assay:protein structure· 2 Approach:Bayesian inference· 1 Research Focus:benchmarking· 1 Approach:contrastive learning· 1 Research Focus:drug response / drug discovery· 1 Assay:electronic health records· 1

Frequent Co-Authors

Kresten Lindorff‐LarseniD· 4 shared Carlos Pintado‐GrimaiD· 1 shared Charlotte Mary DeaneiD· 1 shared Davide LucianoiD· 1 shared Gastón CourtadeiD· 1 shared Giulio TeseiiD· 1 shared Massimiliano BonomiiD· 1 shared Matteo CagiadaiD· 1 shared

Activity by Year

2024-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 17 mutual co-author links

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4 shared papersBioinformatics

Kresten Lindorff‐LarsenORCID

Mutual co-authors in cluster

Author profile →

4 papers

Recent·Most cited

Full-Length Molecular Models of Brain-Derived α-Synuclein Fibrils Reveal a Fuzzy-Coat-Mediated Mechanism for Selective Peptide Binding

Carlos Pintado‐Grima, Oriol Bárcenas, Giulio Tesei, F. Emil Thomasen, Kresten Lindorff‐Larsen, Salvador Ventura

bioRxiv · Apr 20, 2026 · PreprintBioinformatics

Computational characterization of the xanthan gum glycosyltransferase GumK

Davide Luciano, F. Emil Thomasen, Kresten Lindorff‐Larsen, Gastón Courtade

This paper uses computational methods to study GumK, a glycosyltransferase, revealing its dynamic behavior and substrate specificity mechanisms.

PLoS Computational Biology · Dec 10, 2025Peer-reviewedBiophysics

AF2χ: Predicting protein side-chain rotamer distributions with AlphaFold2

Matteo Cagiada, F. Emil Thomasen, Sergey Ovchinnikov, Charlotte Mary Deane, Kresten Lindorff‐Larsen

AF2χ predicts protein side-chain rotamer distributions using AlphaFold2, outperforming simulations and existing methods in speed and accuracy.

bioRxiv · Apr 16, 2025 · PreprintBiophysics

Accurate model and ensemble refinement using cryo-electron microscopy maps and Bayesian inference

Samuel E. Hoff, F. Emil Thomasen, Kresten Lindorff‐Larsen, Massimiliano Bonomi

This paper introduces EMMIVox, a Bayesian method for refining cryo-EM maps into high-quality structural models and ensembles, balancing data fit with physico-chemical accuracy.

PLoS Computational Biology · Jul 15, 2024Peer-reviewedBioinformatics