Deep classification of a large cryo-EM dataset defines the conformational landscape of the 26S proteasome
- 24 March 2014
- journal article
- research article
- Published by National Academy of Sciences in Proceedings of the National Academy of Sciences
- Vol. 111 (15) , 5544-5549
Abstract
The 26S proteasome is a 2.5 MDa molecular machine that executes the degradation of substrates of the ubiquitin–proteasome pathway. The molecular architecture of the 26S proteasome was recently established by cryo-EM approaches. For a detailed understanding of the sequence of events from the initial binding of polyubiquitylated substrates to the translocation into the proteolytic core complex, it is necessary to move beyond static structures and characterize the conformational landscape of the 26S proteasome. To this end we have subjected a large cryo-EM dataset acquired in the presence of ATP and ATP-γS to a deep classification procedure, which deconvolutes coexisting conformational states. Highly variable regions, such as the density assigned to the largest subunit, Rpn1, are now well resolved and rendered interpretable. Our analysis reveals the existence of three major conformations: in addition to the previously described ATP-hydrolyzing (ATPh) and ATP-γS conformations, an intermediate state has been found. Its AAA-ATPase module adopts essentially the same topology that is observed in the ATPh conformation, whereas the lid is more similar to the ATP-γS bound state. Based on the conformational ensemble of the 26S proteasome in solution, we propose a mechanistic model for substrate recognition, commitment, deubiquitylation, and translocation into the core particle. Significance The 26S proteasome is a multisubunit molecular machine for the targeted degradation of intracellular proteins. It has an essential role in the maintenance of protein homeostasis. During its functional cycle the proteasome undergoes large-scale conformational changes. For a detailed mechanistic understanding, an analysis of its conformational landscape is indispensable. Capitalizing on a very large dataset of more than 3 million individual particles and using a novel image-classification strategy, we have been able to deconvolute coexisting conformational states. This led to the discovery of conformation intermediates that provide deeper insights into the sequence of events following the initial binding of ubiquitylated substrates.Keywords
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