Continuous changes in structure mapped by manifold embedding of single-particle data in cryo-EM

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• 作者：Joachim Frank^a ; ^b ; ^{jf2192@cumc.columbia.edu" class="auth_mail" title="E-mail the corresponding author} ; Abbas Ourmazd^c ; ^{ourmazd@uwm.edu" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Classification ; Heterogeneity ; Machine learning ; Molecular machines ; Protein synthesis ; Ribosome
• 刊名：Methods
• 出版年：2016
• 出版时间：1 May 2016
• 年：2016
• 卷：100
• 期：Complete
• 页码：61-67
• 全文大小：1465 K

文摘

Cryo-electron microscopy, when combined with single-particle reconstruction, is a powerful method for studying macromolecular structure. Recent developments in detector technology have pushed the resolution into a range comparable to that of X-ray crystallography. However, cryo-EM is able to separate and thus recover the structure of each of several discrete structures present in the sample. For the more general case involving continuous structural changes, a novel technique employing manifold embedding has been recently demonstrated. Potentially, the entire work-cycle of a molecular machine may be observed as it passes through a continuum of states, and its free-energy landscape may be mapped out. This technique will be outlined and discussed in the context of its application to a large single-particle dataset of yeast ribosomes.