Author

Christian Schleberger

Novartis (Switzerland)ORCID 0000-0002-5601-1724Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Research Focus:protein structure prediction· 2 Research Focus:benchmarking· 1 Approach:deep learning· 1 Research Focus:drug response / drug discovery· 1 Approach:experimental assay· 1 Research Focus:gene regulatory network inference· 1 Organism:human· 1 Organism:mouse· 1

Frequent Co-Authors

Bojana PočučaiD· 1 shared Celia Méndez–GarcíaiD· 1 shared Fabian SesterhenniD· 1 shared Philip HartoutiD· 1 shared Rohan GorantlaiD· 1 shared

Activity by Year

2023-2026 · peak 1/yr

Co-Author Collaboration Network

5 top collaborators · 4 mutual co-author links

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1 shared paperBioinformatics

Bojana PočučaORCID

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2 papers

Recent·Most cited

BioinformaticsbioRxivPreprintSep 27, 2026

Targeted finetuning enables co-folding models to learn ligand-induced protein conformational states

Rohan Gorantla, Christian Schleberger, Fabian Sesterhenn

The paper shows targeted finetuning can enable co-folding models to learn ligand-induced conformational states not present in training data.

DOI 10.64898/2026.09.21.752570

protein structuredeep learning

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BioinformaticsBioinformaticsPeer-reviewedJul 31, 2023

Investigating the human and non-obese diabetic mouse MHC class II immunopeptidome using protein language modelling

Philip Hartout, Bojana Počuča, Celia Méndez–García, Christian Schleberger

The paper trains a transformer model to predict MHCII peptide presentation in humans and mice, showing improved performance with multi-organism data and enabling in silico prediction in a type 1 diabetes model.

2 citations

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