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Step-by-step guide to efficient subtomogram averaging of virus-like particles with Dynamo

['Stefano Scaramuzza', 'Bioem Lab', 'Biozentrum', 'University Of Basel', 'Basel', 'Daniel Castaño-Díez']

Date: 2021-09

Subtomogram averaging (STA) is a powerful image processing technique in electron tomography used to determine the 3D structure of macromolecular complexes in their native environments. It is a fast growing technique with increasing importance in structural biology. The computational aspect of STA is very complex and depends on a large number of variables. We noticed a lack of detailed guides for STA processing. Also, current publications in this field often lack a documentation that is practical enough to reproduce the results with reasonable effort, which is necessary for the scientific community to grow. We therefore provide a complete, detailed, and fully reproducible processing protocol that covers all aspects of particle picking and particle alignment in STA. The command line–based workflow is fully based on the popular Dynamo software for STA. Within this workflow, we also demonstrate how large parts of the processing pipeline can be streamlined and automatized for increased throughput. This protocol is aimed at users on all levels. It can be used for training purposes, or it can serve as basis to design user-specific projects by taking advantage of the flexibility of Dynamo by modifying and expanding the given pipeline. The protocol is successfully validated using the Electron Microscopy Public Image Archive (EMPIAR) database entry 10164 from immature HIV-1 virus-like particles (VLPs) that describe a geometry often seen in electron tomography.

Funding: This research has been supported by the Human Frontiers Science Program (HFSP) grant RGP0017/2020 and the Swiss National Science Foundation (SNF) grant 205321 179041. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Introduction

Cellular organelles and biological macromolecules such as proteins and complexes thereof play a fundamental role in almost all life sciences. In structural biology, the molecular structure of these particles is studied to gain information about their morphology and function. Electron tomography is a well established and quickly evolving technique that, additionally to determining the 3D structure of the particles of interest, also allows to image the particles in situ, and, therefore, to draw conclusions about their cellular context, geometry, and interactions with their environment.

A powerful image analysis technique in electron tomography is subtomogram averaging (STA), where copies of the same particle of interest within a tomogram are extracted independently and then aligned and averaged to a common reference in order to increase the signal and detail of the underlying structure. STA has led to many breakthroughs in structural biology, and method development is ongoing [1–5]. A big challenge in STA is the high complexity of the technique, caused by the often intricate geometries of the biological structures that often show large variations between projects. This makes tasks such as locating particles (particle picking) within the tomograms particularly difficult.

Various software for STA exist. Among the popular ones are Dynamo [6,7], TOM [8], AV3 [9], PyTOM [10,11], EM-Clarity [12], RELION [13], EMAN2 [14], PEET [15,16], M [17], and MLTOMO [18]. Guides and tutorials on how to use these software packages can be found on the corresponding websites. For Dynamo and RELION, there are published processing protocols covering specific parts of the processing pipeline [13,19].

Published structures in STA are often difficult to reproduce due to the lack of in-depth information on the methods, since providing this information is usually beyond the scope of such publications. To date, only a few protocols and tutorials are available that go deeply into the practical aspects of STA processing [13,19]. Our experience in teaching STA showed that while users often have a good grasp on the theory, they often struggle with exactly those practical details that are rarely available. We want to meet the need for such information and therefore created this protocol that is intended to provide a complete, detailed, and fully reproducible step-by-step guide for particle picking and particle alignment in Dynamo. The script-based approach shows how the Dynamo tools can be combined with MATLAB scripts to create a straightforward, versatile, and ready-to-use solution. The shown pipeline can also serve as a basis for user-specific projects, since it can be extended or adapted to other geometries such as, e.g., lipid tubes or other types of surfaces. We also demonstrate how parts of the processing pipeline can be streamlined and automated to improve productivity. The protocol is aimed at users of all levels and can be used for training or as a guide to set up user-specific projects.

The Dynamo software for STA, which is written mainly in MATLAB (MathWorks (www.mathworks.com)), was chosen for this report because of its popularity and versatility. Functions that independently address all steps needed in the STA processing can be individually called and combined with conventional MATLAB scripts, making the software very flexible and allowing to set up customizable processing pipelines with high levels of automation. This versatility is essential for STA because it allows to design and adapt image processing strategies dependent on the often unique geometries of the analyzed samples.

In this protocol, we process the Electron Microscopy Public Image Archive (EMPIAR) dataset with the ID 10164 (related Protein Data Bank (PDB) entry 5l93) from immature HIV-1 virus-like particles (VLPs) [20] using the 5 tomograms that were used in [21]. We chose this dataset because it has been already used for benchmarking in various other STA projects [12]. More importantly, the sample geometry in this specific dataset consists of particles on the surface of a sphere, which is a geometry often seen in electron tomography. The same protocol can therefore easily be adapted to any similar samples such as, e.g., membrane proteins reconstituted in lipid vesicles or any other type of spherical viruses.

The emphasis of the protocol is on particle picking and particle alignment in Dynamo. Nevertheless, the pre- and postprocessing steps are briefly explained to ensure full reproducibility. In an effort to reduce the number of variables for the users, we limited the dependency on third-party software in those steps by using simple 2D contrast transfer function (CTF) correction of the tomograms. The way we perform pre- and postprocessing is one of many ways to do it right, and users are free to use their preferred software for those steps. The protocol can be summarized in the following 3 parts:

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[1] Url: https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3001318

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