Author

Mark Doerr

Universität GreifswaldORCID 0000-0003-3270-6895Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Organism:bacteria (other)· 1 Research Focus:protein folding & dynamics· 1 Assay:protein sequence· 1 Research Focus:sequence-to-function modeling· 1 Approach:statistical model· 1 Approach:transformer / foundation model· 1 Organism:yeast· 1

Frequent Co-Authors

Uwe T. BornscheueriD· 2 shared Anna JäckeringiD· 1 shared Birgit StrodeliD· 1 shared Frederike GöttschiD· 1 shared Hana FaldynováiD· 1 shared Jan VeleckýiD· 1 shared Jiřı́ DamborskýiD· 1 shared Moritz SchäffleriD· 1 shared

Activity by Year

2024-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 25 mutual co-author links

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2 shared papersBioinformatics

Uwe T. BornscheuerORCID

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2 papers

Recent·Most cited

BioinformaticsbioRxivPreprintSep 26, 2025

SoluProtMut: Siamese Deep Learning for Predicting Solubility Effects of Protein Mutations with Experimental Validation

Jan Velecký, Hana Faldynová, Pedro Hermosilla…·Stanislav Mazurenko

The paper introduces SoluProtMut, a Siamese deep learning model for predicting solubility effects of protein mutations, validated experimentally on 277 mutants.

DOI 10.1101/2025.09.26.676459

bacteria (other)yeast

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BioengineeringJACS AuPeer-reviewedSep 26, 2024

From Bulk to Binding: Decoding the Entry of PET into Hydrolase Binding Pockets

Anna Jäckering, Frederike Göttsch, Moritz Schäffler…·Birgit Strodel

The paper uses molecular dynamics simulations to uncover the binding pathway of PET hydrolases into PET material and identifies key residues affecting PET entry.

34 citationstop 4%

deep learningstatistical model

𝕏 PostBluesky