Meet the Höcker Lab
Birte Höcker
Group leader
Josef Kynast
Researcher
Merve Ayyildiz
Researcher
Jakob Noske
Researcher
University of Bayreuth
Group leader
Researcher
Researcher
Researcher
We study the evolution of protein folds and functions to understand structure-function
relationships and to use this data
to develop strategies for protein engineering and design. Examples of our work include the
combination of fragments from
different folds to construct new proteins, the de novo design of an idealized four-fold
symmetric TIM-barrel as well as the
change of ligand-specificity in different binding proteins. In addition we are developing
our own program code, e.g. PocketOptimizer
to optimize predictability of ligand binding and help identify affinity changing mutations
in proteins.
Addressing these questions
in protein folding and ligand recognition requires an interdisciplinary approach and relies
on the combination of experimental and
theoretical methods. The spectrum of techniques we apply ranges from computational (sequence
analysis and macromolecular modeling and
design), via protein biochemistry (spectroscopy, calorimetry, and enzymology) to structural
biology (X-ray crystallography and, in
collaboration, NMR). The overarching goal is to achieve a better understanding of protein
folding and small molecule recognition by
studying the evolutionary history of proteins and by applying the gained knowledge to
further advance the rational design of proteins.
Kynast J.P., Höcker B. (2023) Atligator Web: A Graphical User Interface for Analysis and Design of Protein–Peptide Interactions BioDesign Res doi: 10.34133/bdr.0011
Noske J., Kynast J.P., Lemm D., Schmidt S., Höcker B. (2022) PocketOptimizer 2.0: A modular framework for computer-aided ligand-binding design Prot Sci doi: 10.1002/pro.4516
Kynast J.P., Schwägerl F., Höcker B. (2022) ATLIGATOR: Editing protein interactions with an atlas-based approach Bioinformatics doi: 10.1093/bioinformatics/btac685
Gisdon J.F., Kynast J.P., Ayyildiz M., Hine A.V., Plückthun A., Höcker B. (2022) Modular peptide binders - development of a predective technology as alternative for reagent antibodies Biol Chem doi: 10.1515/hsz-2021-0384
Ferruz N., Schmidt S., Höcker B. (2022) ProtGPT2 is a deep unsupervised language model for protein design Nat Commun doi: 10.1038/s41467-022-32007-7
Ferruz N., Noske J., Höcker B. (2021) Protlego: A Python package for the analysis and design of chimeric proteins Bioinformatics doi: 10.1093/bioinformatics/btab253