The Switch laboratory is dedicated to the study of molecular mechanisms of protein aggregation. Over the last decade we have developed algorithms to predict aggregating protein sequences (TANGO, WALTZ) as well as the effect of mutations on protein stability (FoldX). We have since used these tools to experimentally explore the impact of protein aggregation on disease (eg. p53 aggregation in cancer), protein homeostasis and recombinant protein technology. Our laboratory has an interdisciplinary setup encompassing bio-informatics, molecular biophysics, cell biology and animal models.
Project Background
In order to investigate protein aggregation in cells, we employ a combination of deep RNA sequencing, mass spectrometry based proteomics and homology modelling. Using these mixed data types we want to construct an integrating pipeline to identify aggregated proteins, map cellular responses, etc.
Second part of the job is to manage external requests for homology modelling and mutation interpretation using home-built methods like SNPeffect, Solubis and FoldX.
1 Ganesan, A. et al. Structural hot spots for the solubility of globular proteins. Nature communications 7, 10816, doi:10.1038/ncomms10816 (2016).
2 Gallardo, R. et al. De novo design of a biologically active amyloid. Science 354, doi:10.1126/science.aah4949 (2016).
3 Xu, J. et al. Gain of function of mutant p53 by coaggregation with multiple tumor suppressors. Nat Chem Biol 7, 285-295, doi:10.1038/nchembio.546 (2011).
4 Maurer-Stroh, S. et al. Exploring the sequence determinants of amyloid structure using position-specific scoring matrices. Nature Methods 7, 237-U109, doi:10.1038/Nmeth.1432 (2010).
5 Fernandez-Escamilla, A. M., Rousseau, F., Schymkowitz, J. & Serrano, L. Prediction of sequence-dependent and mutational effects on the aggregation of peptides and proteins. Nature Biotechnology 22, 1302-1306, doi:10.1038/nbt1012 (2004).
The candidate should have general expertise bioinformatics, programming and setting up web servers. This includes handling ‘omics’ data, such as next gen sequencing and mass spec, but most importantly have a strong background in homology modelling and the analysis of protein structures using 3D viewers as well as force fields. Knowledge of predictor development (ROC curves, PSSMs, HMMs,..) and hands-on wet lab experience are a plus.
email CV and motivation letter