Abstract
The process of turning 2D micrographs into 3D atomic models of the imaged macromolecules has been under rapid development and scrutiny in the field of cryo-EM. Here, some important methods for validation at several stages in this process are described. Firstly, how Fourier shell correlation of two independent maps and phase randomization beyond a certain frequency address the assessment of map resolution is reviewed. Techniques for local resolution estimation and map sharpening are also touched upon. The topic of validating models which are either built de novo or based on a known atomic structure fitted into a cryo-EM map is then approached. Map-model comparison using Q-scores and Fourier shell correlation plots is used to assure the agreement of the model with the observed map density. The importance of annotating the model with B factors to account for the resolvability of individual atoms in the map is illustrated. Finally, the timely topic of detecting and validating water molecules and metal ions in maps that have surpassed ∼2 Å resolution is described.
Highlights
Cryo-EM is becoming more widely used to obtain 3D maps of biomedically important macromolecular complexes at increasing resolution
The map resolution is estimated by Fourier shell correlation (FSC); the two independent reconstructions are converted into Fourier components and the correlation is calculated in shells, where each shell corresponds to a frequency or resolution (Frank & Al-Ali, 1975; Harauz & van Heel, 1986)
Cryo-EM is continuing to have a large impact in the field of structural biology, providing detailed structures of macromolecules in close-to-native environments and even different functional states
Summary
Cryo-EM is becoming more widely used to obtain 3D maps of biomedically important macromolecular complexes at increasing resolution. The process begins with image acquisition, in which 2D images or micrographs are obtained, typically as a stack of movie frames (Li et al, 2013). The frames are averaged while applying motion correction, which adjusts for the motion of particles during image acquisition (Campbell et al, 2012). From the motion-corrected images, particles are picked, typically manually at first, and automatically by template matching or convolutional neural networks trained on the manually picked images (Bell et al, 2018; Bepler et al, 2019; Wagner et al, 2019). The picked particles are aligned in order to find 2D class averages, which in turn are used to generate an initial 3D model (Bell et al, 2016; Punjani et al, 2017; Zivanov et al, 2018). Since the biochemically purified particles do not necessarily assume a single conformation, particle-classification methods have been developed to identify similar subsets of particles yielding structures of different states (Punjani et al, 2017; Zivanov et al, 2018)
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