Author

Friederike E. H. Nintzel

University of CambridgeORCID 0000-0002-0637-4383Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:drug response / drug discovery· 2 Approach:experimental assay· 2 Research Focus:protein design· 2 Assay:proteomics· 2 Research Focus:benchmarking· 1 Approach:deep learning· 1 Approach:machine learning (classical)· 1 Approach:wet-lab / biochemical assay· 1

Frequent Co-Authors

Florian HollfelderiD· 3 shared Matthew PenneriD· 2 shared Maximilian GantziD· 2 shared Michael HergeriD· 2 shared Oskar James KleiniD· 2 shared Tomasz S. KamińskiiD· 2 shared Anne-Cathrin Prowald· 1 shared Daniel Boros· 1 shared

Activity by Year

2024-2025 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 40 mutual co-author links

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3 shared papersBiochemistry

Florian HollfelderORCID

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3 papers

Recent·Most cited

BiochemistryJournal of the American Chemical SocietyPeer-reviewedMar 24, 2025

Fluorogenic, sub-single-turnover monitoring of enzymatic reactions involving NAD(P)H provides a generalised platform for directed ultrahigh throughput evolution of biocatalysts in microdroplets

Matthew Penner, Oskar James Klein, Maximilian Gantz…·Florian Hollfelder

The paper develops a fluorogenic assay for NAD(P)H detection to enable high-throughput screening of biocatalysts in microdroplets, demonstrating improved sensitivity and applicability for glycosyl hydrolases and IREDs.

14 citations

proteomicsdeep learning

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BiochemistryACS CatalysisPeer-reviewedApr 10, 2024

Ultrahigh throughput evolution of tryptophan synthase in droplets via an aptamer-biosensor

Remkes A. Scheele, Yanik Weber, Friederike E. H. Nintzel…·Florian Hollfelder

This paper develops a DNA aptamer biosensor to enable high-throughput screening of tryptophan synthase variants in droplets, identifying 5-fold improved enzymes.

17 citations

proteomicsexperimental assay

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Synthetic BiologybioRxivPreprintApr 8, 2024

Microdroplet screening rapidly profiles a biocatalyst to enable its AI-assisted engineering

Maximilian Gantz, Simon V. Mathis, Friederike E. H. Nintzel…·Florian Hollfelder

The paper combines microfluidic screening with machine learning to rapidly engineer biocatalysts, achieving 23-fold improvement in catalytic efficiency through sequence-function mapping.

18 citations

machine learning (classical)protein design

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