In silico modeling of multi-domain proteins in biological condensates

ended

Neurodegenerative disorders are one of the leading causes of disabilities and death in elderly populations worldwide. To this date, no established treatment can either prevent or slow down the progression of these diseases. Their occurrences and progression are strongly correlated with insoluble biological condensates composed of nucleic acids and proteins, where the latter are multi-domain proteins with both folded and disordered domains. The formation and the existence of biological condensates can be described through a combination of density (e.g., liquid-liquid phase separation) and network (percolation) transitions. These phase transitions are highly context-dependent (pH, temperature, etc.) and composition-dependent, i.e., the presence of other biopolymers, where these system variations can either enhance or suppress transitions or have a profound effect on the material properties of condensates formed. Currently, we lack computational approaches that can account for such system variations and predict the phase behavior of biological condensates. In this project, I will develop for the first time a quantitative computational model that will enable scanning a large chemical space, as well as, be temperature-sensitive. Next, I will use this model to identify the role of folded and disordered domains in the formation of condensates for two protein families related to amyotrophic lateral sclerosis, frontotemporal dementia, and autism spectrum disorder through large-scale molecular dynamics simulations. Lastly, I will deliver a microscopic understanding of how certain system variations are linked to neurodegenerative diseases, e.g., pH, the presence of other biopolymers, and changes in protein sequence affect the properties of biological condensates. I will carry out this project in the research group of Prof. Kresten Lindorff-Larsen at the University of Copenhagen (UCPH).