Research focus

​The VIB Switch Laboratory, K.U.Leuven focuses on essential cellular processes where functional regulation is governed by protein conformational switches that have to be actively controlled to ensure cell viability. The lab employs a multidisciplinary approach that combines in vitro biophysical techniques and computational structural biology methods with advanced cell biological studies.

The research focuses on: 

• protein (mis)folding, aggregation and amyloidosis: what is the relationship between the structure of protein aggregates and chaperone recognition?
• protein dynamics and signal transduction: how do conformational fluctuations of the native state achieve signal transduction? 
• non-synonymous SNPs and the molecular phenotype of proteins: how does human genetic variability affect protein stability, folding and aggregation?

Algorithms – We have contributed to the development of several computational algorithms to predict and simulate protein aggregation (TANGO) and amyloidosis (in progress), but also protein stability, dynamics and folding (FoldX).

Protein Aggregation – Using TANGO, we were able to show that proteins are subject to strong evolutionary pressure against aggregation and that proteins possess specialized residues, termed gatekeepers, which are specifically selected to oppose protein aggregation, and that chaperones evolved to recognize such gatekeeper residues. The precise interaction between chaperones and the gatekeeper motif is currently being investigated using both simulations and wet lab experimentation on chaperone specificity and peptide aggregation.

Signal Transduction – In the field of signal transduction, the FoldX algorithm is used to simulate protein dynamics and to determine communication channels for signal transduction between distal sites in globular proteins, using a framework borrowed from noisy channel theory.

SNPeffect – Single nucleotide polymorphisms constitute the most fundamental type of genetic variation in human populations. An important goal in genomic research is to understand how this variability affects protein function, and whether or not particular SNPs are associated with disease susceptibility. Accordingly, the SNPeffect database uses sequence- and structure-based bioinformatics tools to predict the effect of non-synonymous SNPs on the molecular phenotype of proteins.