Cryo-EM is a powerful tool, but helical applications can have pitfalls

Cryo-EM of Helical Polymers

While the application of cryogenic electron microscopy (cryo-EM) to helical polymers in biology has a long history, due to the huge number of helical macromolecular assemblies in viruses, bacteria, archaea, and eukaryotes, the use of cryo-EM to study synthetic soft matter noncovalent polymers has been much more limited. This has mainly been due to the lack of familiarity with cryo-EM in the materials science and chemistry communities, in contrast to the fact that cryo-EM was developed as a biological technique. Nevertheless, the relatively few structures of self-assembled peptide nanotubes and ribbons solved at near-atomic resolution by cryo-EM have demonstrated that cryo-EM should be the method of choice for a structural analysis of synthetic helical filaments. In addition, cryo-EM has also demonstrated that the self-assembly of soft matter polymers has enormous potential for polymorphism, something that may be obscured by techniques such as scattering and spectroscopy. These cryo-EM structures have revealed how far we currently are from being able to predict the structure of these polymers due to their chaotic self-assembly behavior.

Cryo-electron tomography related radiation-damage parameters for individual-molecule 3D structure determination

To understand the dynamic structure-function relationship of soft- and biomolecules, the determination of the three-dimensional (3D) structure of each individual molecule (nonaveraged structure) in its native state is sought-after. Cryo-electron tomography (cryo-ET) is a unique tool for imaging an individual object from a series of tilted views. However, due to radiation damage from the incident electron beam, the tolerable electron dose limits image contrast and the signal-to-noise ratio (SNR) of the data, preventing the 3D structure determination of individual molecules, especially at high-resolution. Although recently developed technologies and techniques, such as the direct electron detector, phase plate, and computational algorithms, can partially improve image contrast/SNR at the same electron dose, the high-resolution structure, such as tertiary structure of individual molecules, has not yet been resolved. Here, we review the cryo-electron microscopy (cryo-EM) and cryo-ET experimental parameters to discuss how these parameters affect the extent of radiation damage. This discussion can guide us in optimizing the experimental strategy to increase the imaging dose or improve image SNR without increasing the radiation damage. With a higher dose, a higher image contrast/SNR can be achieved, which is crucial for individual-molecule 3D structure. With 3D structures determined from an ensemble of individual molecules in different conformations, the molecular mechanism through their biochemical reactions, such as self-folding or synthesis, can be elucidated in a straightforward manner.

DeepTracer-ID: De novo protein identification from cryo-EM maps

The recent revolution in cryo-electron microscopy (cryo-EM) has made it possible to determine macromolecular structures directly from cell extracts. However, identifying the correct protein from the cryo-EM map is still challenging and often needs additional sequence information from other techniques, such as tandem mass spectrometry and/or bioinformatics. Here, we present DeepTracer-ID, a server-based approach to identify the candidate protein in a user-provided organism de novo from a cryo-EM map, without the need for additional information. Our method first uses DeepTracer to generate a protein backbone model that best represents the cryo-EM map, and this model is then searched against the library of AlphaFold2 predictions for all proteins in the given organism. This method is highly accurate and robust for high-resolution cryo-EM maps: in all 13 experimental maps tested blindly, DeepTracer-ID identified the correct proteins as the top candidates. Eight of the maps were of known structures, while the other five unpublished maps were validated by prior protein annotation and careful inspection of the model refined into the map. The program also showed promising results for both homomeric and heteromeric protein complexes. This platform is possible because of the recent breakthroughs in large-scale three-dimensional protein structure prediction.

Helical Indexing in Real Space

Biological structures with helical symmetries of distinct twist, rise, and axial symmetry are abundant and span a wide range of organisms and functions. Performing de novo helical indexing remains challenging because of the steep learning curve involved in Fourier space layer lines. The unknown amount of out-of-plane tilt and the existence of multiple conformations of the helices further complicate indexing. In this work, we introduce a real-space indexing method that leverages the prior knowledge of the tilt and in-plane angles of the helical filaments/tubes, robust ab initio 3D reconstruction capabilities in single particle cryo-EM to obtain asymmetric reconstructions, and automatic indexing of helical parameters directly from the asymmetric density maps. We validated this approach using data from multiple helical structures of distinct helical symmetries, diameters, flexibility, data qualities, and heterogeneous states. The fully automated tool we introduce for real space indexing, HI3D, uses the 2D lattice in the autocorrelation of the cylindrical projection of a 3D density map to identify the helical symmetry. HI3D can often successfully determine the helical parameters of a suboptimal 3D density map, including ab initio single particle asymmetric reconstructions and sub-tomogram averages, with intermediate evidence that can also help assess the map quality. Furthermore, this open-source HI3D is usable independently as a Web application that can be accessed free of installation. With these methods, de novo helical indexing will be significantly more accessible to researchers investigating structures of helical filaments/tubes using cryo-EM.

Atomic structure of Lanreotide nanotubes revealed by cryo-EM

The spectacular developments in cryoelectron microscopy involving new cameras, new microscopes, and new software make it possible today to routinely determine the atomic structures of a large range of molecular assemblies. This has allowed us to solve the atomic structure of nanotubes formed from a peptide, Lanreotide. Its gel, Somatuline, is used as a synthetic growth hormone inhibitor in the treatment of both acromegaly and cancers. The self-assembled nanotube results in a slow release form of the peptide, important pharmacologically. The nanotube structure shows an unexpected complexity and highlights the still unpredictable chemical and physicochemical determinants driving peptide self-assembly.

Functional and versatile nano- and microassemblies formed by biological molecules are found at all levels of life, from cell organelles to full organisms. Understanding the chemical and physicochemical determinants guiding the formation of these assemblies is crucial not only to understand the biological processes they carry out but also to mimic nature. Among the synthetic peptides forming well-defined nanostructures, the octapeptide Lanreotide has been considered one of the best characterized, in terms of both the atomic structure and its self-assembly process. In the present work, we determined the atomic structure of Lanreotide nanotubes at 2.5-Å resolution by cryoelectron microscopy (cryo-EM). Surprisingly, the asymmetric unit in the nanotube contains eight copies of the peptide, forming two tetramers. There are thus eight different environments for the peptide, and eight different conformations in the nanotube. The structure built from the cryo-EM map is strikingly different from the molecular model, largely based on X-ray fiber diffraction, proposed 20 y ago. Comparison of the nanotube with a crystal structure at 0.83-Å resolution of a Lanreotide derivative highlights the polymorphism for this peptide family. This work shows once again that higher-order assemblies formed by even well-characterized small peptides are very difficult to predict.

Python-based Helix Indexer: A graphical user interface program for finding symmetry of helical assembly through Fourier-Bessel indexing of electron microscopic data

Many macromolecules form helical assemblies to carry out their functions. Helical reconstruction from electron microscopic images is a powerful approach for solving high-resolution structures of such assemblies. Determination of the symmetry parameters of the helical assemblies is a prerequisite step in helical reconstruction. The most widely used method for deducing the symmetry is through Fourier-Bessel indexing the diffraction pattern of the helical assemblies. This method, however, often leads to incorrect solutions, due to intrinsic ambiguities in indexing helical diffraction patterns. Here, we present Python-based Helix Indexer (PyHI), which provides a graphical user interface (GUI) to guide the users through the process of symmetry determination. Diffraction patterns can be read into the program directly or calculated on the fly from two-dimensional class averages of helical assemblies. PyHI allows deducing the Bessel orders of diffraction peaks by using both the amplitudes and phases of the diffraction data. Based on the Bessel orders of two unit vectors, the Fourier space lattice is constructed with minimal user inputs. The program then uses a refinement algorithm to optimize the Fourier space lattice, and subsequently generate the helical assembly in real space. The program provides both a publication-quality graphic representation of the helical assembly and the symmetry parameters required for subsequent helical reconstruction steps.