Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.
DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.
The aggregation of natively disordered alpha-Synuclein (aSyn) into amyloid fibrils is a hallmark of Parkinson's and other neurodegenerative diseases. Understanding aSyn's pathological role remains a major challenge due to its complex, context-dependent energy landscape characterized by conformational plasticity and fibril polymorphism. Here, we present a systematic mutational analysis as a quantitative probe of the aSyn energy landscape, focusing on electrostatic contributions to key aggregation pathways. We engineered aSyn variants with one to eight lysine-to-glutamine substitutions and analyzed their aggregation under controlled conditions to delineate their effects on nucleation, elongation, seed amplification, fibril stability, and fibril polymorphism. We find that aSyn aggregation from a homogenous solution can be modelled well using global properties, including protein concentration, charge, and ionic strength. Microscopic pathways and the resulting fibril polymorphs are instead modulated by sequence-specific effects. We identify mutations of residues found in fibril cores as perturbations that significantly modify the aSyn free energy landscape, creating pathways and energy minima not accessible to the WT under the same experimental conditions. In contrast, mutations outside of the fibril core affect the magnitude of the relevant energy barriers whilst overall maintaining a WT-like free energy landscape. Our work outlines a scalable, quantitative framework that increases the informational output of the mutational studies of aSyn using conventional assays. The approach can be extended by incorporating additional mutational and functional data to deepen our understanding of aSyn's energy landscape and its role in health and disease.