Enzyme discovery
Despite knowing so much more about enzymes than we ever have before, we realise that we have only scratched the surface of what can be known about them. The challenges with “Enzyme discovery” are manifold; we focus on defining enzyme families based specific characteristics and annotating large environmental samples with unknown extremophiles in order to find novel enzymes.
Hovedinnhold
Enzymes have special properties that confer them with specificity and efficiency. In evolution, only a few residues are responsible for their role. Therefore, identifying function from sequences is challenging and requires methods and databases dedicated to enzymes. In order to create methods and databases, we have to work on finding the defining characteristics from chemical reaction to substrate binding specificity. The knowledge acquired can be used to further discover similar enzymes in other organisms or related enzymes with different biological purposes.
Discovering related enzymes
Some widely occurring biological processes are still poorly understood, in terms of identity of the enzymes that perform them. For some enzymes we have studied, we have come to suspect that there are many others that may be involved in similar processes waiting to be found. Thus, it is important to investigate the defining attributes that can help us find them all.
Discovering novel enzymes from the Norwegian deep sea
Metagenomics and single-cell genomics provide data from uncultivated extremophiles found in challenging environments. Recovering these genomes provides new and unexpected knowledge on known enzymes. Identifying enzymes from these types of samples is challenging due to their under-representation in databases and their highly specialised natures. We are developing methods in order to identify potentially useful enzymes from extremophiles and to discover novel enzymes.
Collaborators:
Norwegian deep sea project: Ida Helene Steen and Runar Stokke (Center for Geobiology)
NATs project: Thomas Arnesen and al (Protein N-terminal acetylation group)
Techniques: Bioinformatics, homology and threading modeling, molecular dynamics simulations, python and R