Characterisation of molecular motions in cryo-EM single-particle data by multi-body refinement in RELION.

Takanori Nakane,Sjors Hw Scheres,Erik Lindahl,Dari Kimanius

doi:10.7554/elife.36861

Abstract

Macromolecular complexes that exhibit continuous forms of structural flexibility pose a challenge for many existing tools in cryo-EM single-particle analysis. We describe a new tool, called multi-body refinement, which models flexible complexes as a user-defined number of rigid bodies that move independently from each other. Using separate focused refinements with iteratively improved partial signal subtraction, the new tool generates improved reconstructions for each of the defined bodies in a fully automated manner. Moreover, using principal component analysis on the relative orientations of the bodies over all particle images in the data set, we generate movies that describe the most important motions in the data. Our results on two test cases, a cytoplasmic ribosome from Plasmodium falciparum, and the spliceosomal B-complex from yeast, illustrate how multi-body refinement can be useful to gain unique insights into the structure and dynamics of large and flexible macromolecular complexes.

Highlights

In electron cryo-microscopy single-particle analysis, biological macromolecules are embedded in a thin layer of vitrified buffer and imaged in a transmission electron microscope
The first data set comprises 105,247 particle images of a Plasmodium falciparum ribosome bound to the drug emetine (Wong et al, 2014)
The main assumption in conventional cryo-EM single-particle analysis is that every experimental particle image is a two-dimensional projection of a common three-dimensional structure

Summary

Introduction

In electron cryo-microscopy (cryo-EM) single-particle analysis, biological macromolecules are embedded in a thin layer of vitrified buffer and imaged in a transmission electron microscope. In principle, this represents a single-molecule imaging technique that provides unique information about the structure of individual macromolecular complexes. Many popular image classification approaches are based on competitive refinement of a userdefined number of references These methods effectively divide the data into a discrete number of subsets or classes, each of which is assumed to be structurally homogeneous, for example see Heymann et al (2004) and Gao et al (2004) for early applications. Useful are so-called unsupervised classification approaches, which do not require prior knowledge about the structural

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Conclusion