Multi-particle cryo-EM refinement with M visualizes ribosome-antibiotic complex at 3.5 Å in cells

Articles https://doi.org/10.1038/s41592-020-01054-7 Multi-particle cryo-EM refinement with M visualizes ribosome-antibiotic complex at 3.5 Å in cells Dimitry Tegunov 1 ✉, Liang Xue Julia Mahamid 2 ✉ , Christian Dienemann1, Patrick Cramer 2,3 1 ✉ and Cryo-electron microscopy (cryo-EM) enables macromolecular structure determination in vitro and inside cells. In addition to aligning individual particles, accurate registration of sample motion and three-dimensional deformation during exposures are crucial for achieving high-resolution reconstructions. Here we describe M, a software tool that establishes a reference-based, multi-particle refinement framework for cryo-EM data and couples a comprehensive spatial deformation model to in silico correction of electron-optical aberrations. M provides a unified optimization framework for both frame-series and tomographic tilt-series data. We show that tilt-series data can provide the same resolution as frame-series data on a purified protein specimen, indicating that the alignment step no longer limits the resolution obtainable from tomographic data. In combination with Warp and RELION, M resolves to residue level a 70S ribosome bound to an antibiotic inside intact bacterial cells. Our work provides a computational tool that facilitates structural biology in cells. C ryo-EM1 is a widely used method for macromolecular structure determination2,3. Two types of data are commonly analyzed to obtain high-resolution maps. First, samples are prepared at concentrations where individual particles can be distinguished in two-dimensional (2D) projections captured in a transmission electron microscope (TEM), and fractionated exposures at constant stage orientation (frame series) are typically acquired. Such data are then subjected to single-particle analysis (SPA). Second, samples containing multiple particles stacked along the projection axis, or samples that capture portions of crowded cellular environments, favor a tomographic approach to distinguish the particles in three dimensions. Here, the microscope stage is tilted to different angles between subexposures (tilt series). Each subexposure also comprises a frame series (tilt movie). Analysis of recurring structures in this data type has been implemented as subtomogram averaging (STA)4–6. In SPA, many noisy projections of similar particles observed under different orientations are iteratively aligned, classified and averaged to reconstruct three-dimensional (3D) maps of the macromolecules’ Coulomb potential7. SPA refinement algorithms assume that each observation shows a single particle in isolation, and can thus be treated independently from other particles8. The same assumption is made in the closely-related STA workflow9–11, where the reference of a single particle is aligned to each subtomogram and surrounding particles are treated as noise. As samples are irradiated with electrons, beam-induced motion (BIM) leads to changes in particle positions and orientations12. If left uncorrected, these changes decrease the apparent image quality and limit the map resolution. Exposure fractionation into multiple frames captures the particles along their trajectories, allowing for accurate motion registration and the reversal of the detrimental effects of BIM13,14. Unfortunately, the granularity of the motion model is limited by the low signal per particle. Although each particle’s trajectory is unique, correlations exist on a local scale and can be used to regularize the motion model13,15. It is thus beneficial to exploit these correlations and treat the contents of a micrograph or tomogram as a multi-particle system embedded in the same physical space rather than isolated particles. At the data preprocessing stage, the motion model can be fitted based on raw data using reference-free approaches13,14,16–20. Frame series are aligned in two dimensions, whereas tilt series are aligned and used to reconstruct tomograms. Extracted particles are fed into SPA or STA pipelines to obtain 3D references. Reference-based alignment can then improve the model accuracy by aligning the raw data to high-resolution reference projections. Such algorithms exist for both frame and tilt-series data6,15,21,22, and improve the accuracy by enforcing local smoothness between particle trajectories on different spatio-temporal scales. However, most implementations remain different for frame and tilt-series data, and are limited to one reference species even in highly heterogeneous datasets. They are further decoupled from other parts of the refinement process, including rotational alignment and contrast transfer function (CTF) fitting, leading to a fragmented workflow and decreased convergence speed, limiting the final map resolution. Here we present M, a software tool that integrates reference-based refinement of particle motion trajectories with other parts of the structure determination pipeline. We formulate our approach explicitly in a multi-particle framework, which simultaneously optimizes particle poses and hyperparameters describing physically plausible sample deformation within the entire field of view. This allows us to unify the processing of frame and tilt series, define a set of intuitive regularization constraints such as spatial and temporal resolution and include any number of particle species at different resolutions. Coupled with a robust approach to CTF correction Department of Molecular Biology, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany. 2Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany. 3Collaboration for joint PhD degree between EMBL and Heidelberg University, Faculty of Biosciences, Heidelberg University, Heidelberg, Germany. ✉e-mail: ; ; 1 186 Nature Methods | VOL 18 | FebruarY 2021 | 186–193 | www.nature.com/naturemethods Articles NatuRE MEtHODS Simultaneous refinement of deformation models, particle poses and CTF parameters for several iterations Data acquisition et ad at a Updated deformation models and CTF parameters r y p o -fl Im -the on ts Motion correction, CTF estimation, CNN-based particle picking, tilt series alignment through IMOD, tomogram reconstruction, denoising, template matching Poses Refined maps for all species, particle assignment and globally aligned poses Uses u p metad da ted ata Raw frame series, tilt movies Final high-resolution maps for all species rt s Sa ve sm cle ts f po R ti ar r t il Im s, resse Co m p s tores sp ct t ra x ee so me f ra Reconstructs, denoises ent fi n em or re 2D particles, subtomograms, 3D CTFs, metadata Ex t r re c ac ts, ons tr ucts CTF 2D classification, ab initio map generation, 3D classification, 3D refinement Fig. 1 | The Warp–RELION–M pipeline for frame and tilt-series cryo-EM data refinement. Electron microscopy data are preprocessed on-the-fly in Warp, which then exports particles as images or subtomograms. For t (...truncated)