Computational Methods for Interpretation of EM Maps at Subnanometer Resolution


Driven by a remarkable number of technological advances in recent years, electron cryo‐microscopy (cryo‐EM) has played an increasingly important role in deciphering macromolecular structure and function. Today, cryo‐EM routinely achieves subnanometer resolutions, with current state‐of‐the‐art reconstructions approaching atomic resolutions. Though achieving high‐resolution density maps is still a challenging aspect of cryo‐EM, it is possible to isolate individual subunits, identify secondary structures elements and accurately fit atomic models in reconstructions of large macromolecular assemblies. Additionally, computational modelling and feature recognition tools are routinely employed to construct backbone and atomic models of entire assemblies directly from a density map. Together, these tools provide researchers with unprecedented insight into structure and function, revealing everything from subunit arrangements to complex sets of interactions in macromolecular assemblies.

Key Concepts:

  • Cryo‐EM is capable of imaging large macromolecular assemblies in order to understand their structure and function.

  • Analysing macromolecular assemblies requires a variety of complex computational tools.

  • In subnanometer resolution cryo‐EM density maps, it is possible to see individual protein subunits, as well as higher‐resolution structural features.

  • It is now possible to construct backbone and atomic models directly from cryo‐EM density maps at near‐atomic resolutions.

Keywords: cryo‐EM; segmentation; fitting; modelling; macromolecular assemblies

Figure 1.

Resolution in cryo‐EM. Using cryo‐EM, GroEL has been imaged and reconstructed at a variety of resolutions. Shown in the top row are three reconstructions of GroEL at 10.4, 7.8 and 4.2 Å resolution (from left to right EMDB Ids: 1040, 1200 and 5001, respectively). In the bottom row, a single subunit is shown with the X‐ray crystal structure (PDB ID: 1SS8) of fit to the density map. At ∼10 Å resolution, the overall size and shape of the assembly is evident and allows for the segmentation of individual protein subunits. At ∼8 Å resolution, secondary structure elements begin to be resolved. Helices appear as density rods and β‐sheets appear as thin, flat planes. However, connections between the SSEs are not clearly visible. At ∼4 Å, higher resolution features, such as the pitch of the α‐helix and separation of β‐strands is seen. At this resolution, it is possible to observe the connections between SSEs and construct a model directly from the density map with the aid of computational modelling tools.

Figure 2.

Segmentation of a cryo‐EM density map. Four representative cryo‐EM density maps are shown in which individual proteins or domains have been isolated using different techniques. (a) Segger, a semi‐automated utility for segmentation, was used to identify the 16 subunits in the 4.3 Å resolution structure of MM‐CPN (EMDB ID: 5137). (b) Segment3D, a utility in EMAN, was used to automatically identify potential domains in one subunit of the homotetrameric Ca2+ release channel (EMDB ID: 1275, 9.6 Å resolution). (c) VolRover was used to automatically identify the two subunits in the Thermus thermophilus 70S ribosome along with the t‐RNA (EMDB ID: 5030, 6.4 Å resolution). (d) The tail‐spike and its components were isolated from the bacteriophage P‐SSP7 reconstruction manually using Amira (EMDB ID: 1713, 4.6 Å resolution). Individual images were provided by Greg Pintile (Figure a), Irina Serysheva (Figure b) and Xiangan Liu (Figure d).

Figure 3.

Analysis of a cryo‐EM density map. Over the past decade, a number of computational tools have been developed to extract information from a cryo‐EM density map. (a) SSEHunter was used to identify SSEs in the 9.5 Å resolution reconstruction of Rotavirus. Two copies of the inner capsid protein P3 are shown along with their respective SSEs; helices are shown as green cylinders and sheets are represented as cyan planes. (b) CP from Venezuelan Encephalitis Virus (PDB ID: 1EP5) was solved by X‐ray crystallography and fit to the 4.4 Å density map using Foldhunter. (c) An example of flexible fitting is shown in which Flex‐EM was used to flexibly fit the crystal structure of the N‐terminal portion of the human apoptosome (PDB IDs: 1Z6T, 2B4Z) in the 9.5 Å resolution cryo‐EM structure of the apoptosome–procaspase CARD complex (EMDB ID: 5186, PDB ID: 3IYT). (d) Constrained comparative modelling with Modeller was used to generate a model for the 80S ribosome (from Thermomyces lanuginosus) (EMDB IDs: 1067, 1345, PDB IDs: 3JYV, 3JYW, 3JYX), including the rpS19e protein (red) from the 40S subunit. In (e), a model for VP26, a small capsid protein in HSV‐1 (8.9 Å resolution), was constructed using Rosetta with density constraints. Individual images were provided by Corey Hryc (Figure b) and Maya Topf (Figure c, d).

Figure 4.

Modelling in cryo‐EM. As cryo‐EM is now approaching atomic resolutions, it is possible to construct protein structural models directly from a cryo‐EM density map. (a) A basic protocol for de novo model building is shown using the group II chaperonin, MM‐CPN, which was imaged to 4.3 Å resolution (EMDB ID: 5137). (b) A model for the bacteriophage P22 capsid protein constructed from the 3.8 Å resolution density map of the infectious virus is shown (PDB ID: 2XYZ, EMDB ID: 1824). This model was constructed using the de novo modelling approach in (a), in combination with a constrained homology modelling approach (Figure e). In the P22 structure, not all side chains could be resolved; the resulting model is Cα‐only model. (c) A full atomic model for the human adenovirus penton protein was constructed directly from the 3.6 Å resolution density map with a similar protocol (PDB ID: 3IYN, EMDB ID: 5172). Images for panels b and c were provided by Corey Hryc.



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Further Reading

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Baker, Matthew L, Baker, Mariah R, and Cong, Yao(Jan 2012) Computational Methods for Interpretation of EM Maps at Subnanometer Resolution. In: eLS. John Wiley & Sons Ltd, Chichester. [doi: 10.1002/9780470015902.a0023174]