Author

Johannes Zerweck

JPT Peptide Technologies (Germany)

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

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Bioinformatics· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 1 Research Focus:protein structure prediction· 1 Assay:proteomics· 1

Frequent Co-Authors

Andreas F. R. HühmeriD· 1 shared Armin SoleymaniniyaiD· 1 shared Bernard DelangheiD· 1 shared Bernhard KüsteriD· 1 shared Daniel Paul ZolgiD· 1 shared Florian P BayeriD· 1 shared Florian SeefriediD· 1 shared Guillaume MédardiD· 1 shared

Activity by Year

2025-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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1 shared paperBioinformatics

Andreas F. R. HühmerORCID

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1 paper

Recent·Most cited

Learning the Unseen: Data-Augmented Deep Learning for PTM Discovery with Prosit-PTM

Wassim Gabriel, Daniel Paul Zolg, Victor Giurcoiu, Omar Osama Shouman, Polina Prokofeva, Florian Seefried, Florian P Bayer, Ludwig Lautenbacher, Armin Soleymaniniya, Karsten Schnatbaum, Johannes Zerweck, Tobias Knaute, Bernard Delanghe, Andreas F. R. Hühmer, Holger Wenschuh, , Mathias Wilhelm

This paper introduces Prosit-PTM, a deep learning model for PTM discovery that improves zero-shot prediction using a synthetic dataset and chemically-informed encoding.

bioRxiv · Nov 7, 2025 · PreprintBioinformatics