Intro

The main objective of our lab is to augment our understanding on the inner workings of enzymes and decode how conformational dynamics encode enzymes’ capacity to accelerate chemical transformations while maintaining the functional plasticity of accepting structurally diverse substrates. Harnessing this knowledge is the founding step to tackle the daunting tasks of a) design novel drugs in silico and b) the design of novel biocatalysts with tailor made functionalities.

We approach this formidable challenge by an eclectic mix of techniques — borrowed from whichever area of experimental science that promises to shed light on the behaviour of enzymes. We emphasize on single molecule studies (functional, FRET, particle tracking) that offer the potential to directly observe the existence, quantify the abundance and dependence on mutations, of behaviors that were masked in conventional assays due to averaging a large number of unsynchronized molecules.

Read More

Select Projects


Current Members

Nikos S. Hatzakis

Associate Professor

Min Zhang

Assistant Professor

Codruta Ignea

Marie Curie Postdoc. Fellow

Matias E. Moses

Industrial postdoc, Novozymes

Simon Bo Jensen

Postdoc

Mette G. Malle

Postdoc

Søren S.-R. Bohr

Ph.D. Fellow

Camilla D. Thorlaksen

Ph.D. fellow, Novo Nordisk STAR programme

Jacob Kæstel-Hansen

Ph.D fellow

Ge Huang

Ph.D fellow

Freja J. Bohr

Master Student

Yanet G. Bustamante

Master Student

Annette Juma

Master Student

Henrik Pinholt

Master Student

Lampros Spanos

Visiting Erasmus Research Assistant

Emily Winther Sørensen

Bachelor Student

Marcus Winther Dreisler

Bachelor Student

Norman Pedersen

Bachelor Student

Steen Bender

Bachelor Student

Select Publications

Link to full list of publications

A large size-selective DNA nanopore with sensing applications..

Thomsen, S. P., et al. Nature Communications 175, 5655 (2019)

Direct observation of proton pumping by a eukaryotic P-type ATPase.

Veshaguri, S. et al. Science 351, 1469–1473 (2016)

Follow us on Twitter


Software

DeepFRET

DeepFRET

Rapid and automated single molecule FRET data classification using deep learning.

Extraction of liposome intensity

Extraction of liposome intensity

Python based script for extraction and analysis of .tif formatted image files. The script can extract the intensity from individual liposomes based in a changeable ROI size given initially and subtract local background

Cell analyzer HEK293/PYY/eYFP

Cell analyzer HEK293/PYY/eYFP

Python based script for extraction and analysis of .tif formatted image files. The script will identify cells on a image based on set thresholds and parameters. From this a mask will be created to extract the intensity from up to three channels, here corresponding to signal from membrane stain (blue), yellow fluorescent protein (YFP, green) and Cy5/Atto655 (red).

Single Particle Tracking of Lipases

Single Particle Tracking of Lipases

Python Script for analysing .tif movies of lipases diffusing on a surface. Code used to make data for Bohr, S. S.-R. et al. Scientific Reports, 2019

Funding