Aleksander Rebane

Aleksander Rebane's picture
Postdoctoral Associate
Ph.D. 2018, Yale University
Thesis Advisor: 
Yongli Zhang and James E. Rothman
Dissertation Title: 
Exploring free energy landscapes of SNARE assembly using optical tweezers
Dissertation Abstract: 

Scientists have long sought to understand the working principles of protein machinery. A decisive step towards this goal has been the development of the Gibbs free energy landscape of protein folding. However, measurement of energy landscapes has remained challenging, particularly when folding occurs over one or more intermediates. An important example is soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) complex assembly, in which the energetics and kinetics of multiple assembly steps are coupled to distinct stages of vesicle maturation and membrane fusion in synaptic exocytosis. As a result, a quantitative test of this fundamental biophysical mechanism remains outstanding. In recent years it has become possible to measure energy landscapes of proteins in the presence of force using a single-molecule manipulation technique called optical tweezers (OT). However, derivation of energy landscapes in the absence of force from OT data has remained difficult. Here, we present a comprehensive OT data analysis method that uses information from high-resolution protein structures to derive a simplified energy landscape of protein folding at zero force by model fitting of the experimental measurements. We apply our method to derive the energetics, kinetics, and intermediate conformations of SNARE assembly for the wild-type complex and a number of mutants with known phenotypes. We characterize how the steps in SNARE assembly function in the respective stages of synaptic exocytosis and provide quantitative verification of the coupling mechanism. Finally, we investigate the mechanism by which two SNARE mutations cause severe neurological disease. In sum, our work provides a complete methodology to measure energy landscapes to reveal the underlying mechanisms of protein function.

Degree Date: 
May, 2018