Improving chemical shift-based methods in NMR.
We are continuing to work on approaches that interpret and use chemical shift data from Nuclear Magnetic Resonance (NMR), in order to improve our protein sequence-based predictions. The statistical analysis of NMR data forms the basis of most of our research: a good and extensive data set is the key to developing any new prediction or validation methodology. We focus on local residue interactions between amino acids close to each other in the sequence, which are key determinants of protein conformation and dynamics. This behaviour is therefore already present in peptides (short proteins), where we are using extensive Molecular Dynamics (MD) simulations to explore whether we can create a computational framework that can estimate the effect amino acid modifications (such as phosphorylation) will likely have on the conformation and dynamics of proteins.
G.0328.16N (Jan. 1, 2016-Dec. 31, 2019)
FoldMod - The role of local amino acid interactions in the folding of proteins, the stability of their fold and the location of post-translational modifications.
ProteinContour (Jan. 1, 2021-Dec. 12, 2024)
In this project (FWO identifier G028821N), we combine mass spectrometry analysis expertise from UGent (CompOmics group, Lennart Martens) and UCL (Laurent Gatto) with our predictions to map the relationships between post-translational modifications, sub cellular location, interactions and biophysical features of proteins in human cells.
CING (Software tool, External collaboration)
ShiftCrypt (Software tool, In-house)