Author

Bilge Atay

Humboldt-Universität zu BerlinORCID 0009-0008-6968-8858Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Immunology· 2

Signature Assays, Organisms & Methods

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Research Focus:drug response / drug discovery· 2 Organism:human· 2 Assay:proteomics· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Approach:deep learning· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Badeel Kh Q ZaghlaiD· 2 shared Martin G. KlattiD· 2 shared Agnieszka BlumiD· 1 shared Alexander Sinclair· 1 shared Anna Sanecka-DuiniD· 1 shared Bartłomiej Król-JózagaiD· 1 shared Benedetta Leoni· 1 shared Bjoern Steinmann· 1 shared

Activity by Year

2026-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 30 mutual co-author links

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2 shared papersImmunology

Badeel Kh Q ZaghlaORCID

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2 papers

Recent·Most cited

ImmunologybioRxivPreprintAug 4, 2026

Systematic De-Risking of TCR-Mimic Therapeutics Through Proteome-Wide Off-Target Landscaping and a Generalizable Design Rule Framework

Simon Schuster, Frederike A. Hartl, Jennifer Stehle…·Jörg Birkenfeld

This paper introduces ValidaTe, a framework to predict and mitigate off-target liabilities in TCR-mimic antibodies, demonstrating its effectiveness with CTA MAGE-A4 targets.

DOI 10.64898/2026.07.30.741432

humanproteomics

𝕏 PostBluesky

ImmunologyMolecular & Cellular ProteomicsPeer-reviewedMar 27, 2026

Identification of tumor-specific MHC ligands through improved biochemical isolation and incorporation of machine learning

Shima Mecklenbräuker, Piotr Skoczylas, Paweł Biernat…·Martin G. Klatt

This paper improves the isolation of tumor-specific MHC ligands through hydrophobicity and post-translational modification considerations and introduces a machine learning model for predicting MHC ligand presentation.

3 citations

humanproteomics

𝕏 PostBluesky