Author

Lucie Barthe

Centre National de la Recherche Scientifique

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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Bioengineering· 2 Biochemistry· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 3 Research Focus:protein design· 2 Assay:protein sequence· 2 Approach:statistical model· 2 Organism:bacteria (other)· 1 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Organism:E. coli· 1

Frequent Co-Authors

Delphine DessauxiD· 3 shared Luis Fernando Garcia-AllesiD· 3 shared Sophie BarbeiD· 3 shared Thomas SchiexiD· 3 shared Bessam Azizi· 2 shared Gianluca CiociiD· 2 shared Marianne DefresneiD· 2 shared Samuel BuchetiD· 2 shared

Activity by Year

2024-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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3 shared papersBioengineering

Delphine DessauxORCID

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3 papers

Recent·Most cited

A Generative Neuro-Symbolic AI for Protein Sequence Design

Marianne Defresne, Delphine Dessaux, Samuel Buchet, Lucie Barthe, Liza Ammar Khodja, Bessam Azizi, Valentin Durante, Gianluca Cioci, Simon de Givry, Alain Roussel, Luis Fernando Garcia-Alles, Thomas Schiex, Sophie Barbe

This paper introduces EffieDes, a neuro-symbolic AI framework for protein sequence design that combines deep learning with automated reasoning to generate sequences with complex functional properties.

Advanced Science · Jul 30, 2026Peer-reviewedBioengineering

Promiscuous structural cross-compatibilities between major shell components of Klebsiella pneumoniae bacterial microcompartments

Lucie Barthe, Damien Balestrino, Bessam Azizi, Delphine Dessaux, Vanessa Soldan, Jérémy Esque, Thomas Schiex, Sophie Barbe, Luis Fernando Garcia-Alles

This study identifies structural cross-compatibilities between BMC-H paralogs in Klebsiella pneumoniae, revealing hetero-hexamer formation and reinterpreting existing mass spectrometry data.

PLoS ONE · May 7, 2025Peer-reviewedBiochemistry

Designing symmetrical multi-component proteins using a hybrid generative AI approach

Delphine Dessaux, Samuel Buchet, Lucie Barthe, Marianne Defresne, Gianluca Cioci, Simon de Givry, Luis Fernando Garcia-Alles, Thomas Schiex, Sophie Barbe

This paper introduces a hybrid generative AI approach to design multi-component proteins by combining deep learning and automated reasoning, showing improved performance over existing methods in creating heteromeric assemblies.

bioRxiv · Jun 13, 2024 · PreprintBioengineering