Volume 85, April 2024, 102788Author links open overlay panelAbstract

Many protein and nucleoprotein complexes exist as helical polymers. As a result, much effort has been invested in developing methods for using electron microscopy to determine the structure of these assemblies. With the revolution in cryo-electron microscopy (cryo-EM), it has now become routine to reach a near-atomic level of resolution for these structures, and it is the exception when this is not possible. However, the greatest challenge is frequently determining the correct symmetry. This review focuses on why this can be so difficult and the current absence of a better approach than trial-and-error.

Section snippetsDetermining helical symmetry

There are a number of approaches for estimating the helical symmetry of a polymer, but the most powerful one involves analysis of the power spectrum [13, 14, 15, 16]. Any helical polymer will have axial periodicities, and these give rise to layer lines in the power spectrum where diffraction only occurs on these layer lines and is zero off them. Fourier-Bessel analysis [17,18] can be used to “index” such a power spectrum, assigning Bessel orders to each layer line, and these assignments give

A better method for determining helical symmetry?

A recent paper [29] proposes a method that is almost breathtaking in its simplicity: generate an ab initio 3D reconstruction without any imposed symmetry, and then use cross-correlations to analyze this reconstructed volume for the helical symmetry parameters (rise and twist per subunit). However, there are two main problems with what is proposed here. This approach is actually one of the suggested paths for doing helical reconstruction in cryoSPARC. This is fully explained in the cryoSPARC

Symmetry and resolution

At some finite resolution one may actually find that two different symmetries yield almost identical reconstructions. This was shown for a bacterial mating pilus [30] where, at 5 Å resolution, two symmetries yielded almost the same maps with nice rod-like density for α-helices. If one were unable to extend the resolution, then the true symmetry would have been ambiguous. But one of the two symmetries led to a 3.9 Å map, while the incorrect symmetry would never improve beyond 5 Å. This might

Future outlook

In this very brief review I have discussed some of the challenges in symmetry determination, one of the most problematic aspects of helical reconstruction. Will these difficulties always be with us? I suspect not, given the huge advances over the past several years in machine learning. While I have discussed that searching for the correct symmetry maximizing a metric such as estimated resolution or minimizing out-of-plane tilt will likely lead to false solutions, recognition of protein

Declaration of competing interest

None.

Acknowledgments

Work in my laboratory has been supported by National Institutes of Health [grant number GM122510]. I thank Fengbin Wang and Ravi Sonani for providing data used in this review. I thank all members of my lab for helpful comments.

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