Author

Terézia Dorčaková

Technical University of DenmarkORCID 0009-0002-2137-5533Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Systems Biology· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 2 Organism:E. coli· 1 Assay:metabolomics· 1 Research Focus:perturbation response prediction· 1 Assay:protein sequence· 1 Research Focus:variant effect prediction· 1

Frequent Co-Authors

Alberte Heering Estad· 1 shared Alberto Pettenella· 1 shared Anna Melidi· 1 shared Anna RohliniD· 1 shared Anu Oswal· 1 shared Burcu Aykaç FasiD· 1 shared Caspar Rahm· 1 shared Claudia CavaiD· 1 shared

Activity by Year

2026-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 36 mutual co-author links

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1 shared paperBioinformatics

Alberte Heering Estad

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2 papers

Recent·Most cited

Model-based inference of enzyme inhibitions from perturbation-induced metabolic dynamics

Wolfram Liebermeister, Michela Pauletti, Terézia Dorčaková, Caspar Rahm, Mattia Zampieri

This paper introduces a computational framework to infer enzyme targets from metabolic perturbations using time-resolved metabolomics data.

bioRxiv · Jun 3, 2026 · PreprintSystems Biology

MAVISp: A Modular Structure-Based Framework for Protein Variant Effects

Matteo Arnaudi, Mattia Utichi, Kristine Degn, Matteo Tiberti, Ludovica Beltrame, Karolina Krzesińska, Pablo Sánchez-Izquierdo Besora, Eleni Kiachaki, Simone Scrima, Laura Bauer, Katrine Meldgård, Anna Melidi, Lorenzo Favaro, Anu Oswal, Guglielmo Tedeschi, , Elena Papaleo

MAVISp provides a modular structural framework for analyzing protein variant effects, offering over eight million variants across 1000 proteins with curated data and a web server for accessibility.

Protein Science · Apr 7, 2026Peer-reviewedBioinformatics