Novel computational methods for in vitro and in situ cryo-electron microscopy

Over the past decade, advances in microscope hardware and image data processing algorithms have made cryo-electron microscopy (cryo-EM) a dominant technique for protein structure determination. Near-atomic resolution can now be obtained for many challenging in vitro samples using single-particle analysis (SPA), while sub-tomogram averaging (STA) can obtain sub-nanometer resolution for large protein complexes in a crowded cellular environment. Reaching high resolution requires large amounts of im-age data. Modern transmission electron microscopes (TEMs) automate the acquisition process and can acquire thousands of micrographs or hundreds of tomographic tilt se-ries over several days without intervention. In a first step, the data must be pre-processed: Micrographs acquired as movies are cor-rected for stage and beam-induced motion. For tilt series, additional alignment of all micrographs in 3D is performed using gold- or patch-based fiducials. Parameters of the contrast-transfer function (CTF) are estimated to enable its reversal during SPA refine-ment. Finally, individual protein particles must be located and extracted from the aligned micrographs. Current pre-processing algorithms, especially those for particle picking, are not robust enough to enable fully unsupervised operation. Thus, pre-processing is start-ed after data collection, and takes several days due to the amount of supervision re-quired. Pre-processing the data in parallel to acquisition with more robust algorithms would save time and allow to discover bad samples and microscope settings early on. Warp is a new software for cryo-EM data pre-processing. It implements new algorithms for motion correction, CTF estimation, tomogram reconstruction, as well as deep learn-ing-based approaches to particle picking and image denoising. The algorithms are more accurate and robust, enabling unsupervised operation. Warp integrates all pre-processing steps into a pipeline that is executed on-the-fly during data collection. Inte-grated with SPA tools, the pipeline can produce 2D and 3D classes less than an hour into data collection for favorable samples. Here I describe the implementation of the new algorithms, and evaluate them on various movie and tilt series data sets. I show that un-supervised pre-processing of a tilted influenza hemagglutinin trimer sample with Warp and refinement in cryoSPARC can improve previously published resolution from 3.9 Å to 3.2 Å. Warp’s algorithms operate in a reference-free manner to improve the image resolution at the pre-processing stage when no high-resolution maps are available for the particles yet. Once 3D maps have been refined, they can be used to go back to the raw data and perform reference-based refinement of sample motion and CTF in movies and tilt series. M is a new tool I developed to solve this task in a multi-particle framework. Instead of following the SPA assumption that every particle is single and independent, M models all particles in a field of view as parts of a large, physically connected multi-particle system. This allows M to optimize hyper-parameters of the system, such as sample motion and deformation, or higher-order aberrations in the CTF. Because M models these effects accurately and optimizes all hyper-parameters simultaneously with particle alignments, it can surpass previous reference-based frame and tilt series alignment tools. Here I de-scribe the implementation of M, evaluate it on several data sets, and demonstrate that the new algorithms achieve equally high resolution with movie and tilt series data of the same sample. Most strikingly, the combination of Warp, RELION and M can resolve 70S ribosomes bound to an antibiotic at 3.5 Å inside vitrified Mycoplasma pneumoniae cells, marking a major advance in resolution for in situ imaging

Cross-validation tests for cryo-electron microscopy using an independent set of images

ABSTRACT: In addition to the chemical composition, information about the three-dimensional structure of a biomolecule is vital for understanding its biological function. For many years, resolv- ing structures of biomolecules was exclusive of X-ray crystallography and nuclear magnetic resonance (NMR) techniques. However, due to technological and software improvements, cryo-electron microscopy (cryo-EM) has emerged as an alternative for resolving complexes that were infeasible for crystallization or too large for NMR. Currently, cryo-EM is able to provide near-atomic resolution and close-to-native structures . Moreover, it enables extracting dynamical information, such as free-energy landscapes, from thermal states in the micrographs. The “resolution revolution” in cryo-EM has provoked an avalanche of reported cryo-EM maps. Recent statistics show an exponentially-growing number of reported maps spatially resolved by cryo-EM with their mean resolution decreasing from ∼ 10 Å (in 2013) to 4 Å (for 2018). The resolution revolution brings with it the need of creating robust and reliable methodolo- gies to validate the increasingly large number of maps. Some advances have been done along these lines: the tilt-pair analysis , the gold-standard procedure and the high-frequency randomization have shown to be reliable validation tools. However, it has recently been shown that these methods remain sensitive to overfitting (treating noise as true signal) and subjective criteria. In this work, I will present a novel methodology for validating cryo-EM maps. The method is based on cross-validation criteria where the reconstructed maps are compared against a set of experimental images (raw data) not used in the reconstruction procedure. Such comparison is carried out by calculating the probability that an image is the projection of a given map. The information from these probabilities led us to propose two validation criteria, which are tested over three well-behaved systems and two systems that present overfitting. The results prove that our methodology is able to identify overfitted maps

Gold nanomaterials and their potential use as cryo-electron tomography labels

Rapid advances in cryo-electron tomography (cryo-ET) are driving a revolution in cellular structural biology. However, unambiguous identification of specific biomolecules within cellular tomograms remains challenging. Overcoming this obstacle and reliably identifying targets in the crowded cellular environment is of major importance for the understanding of cellular function and is a pre-requisite for high-resolution structural analysis. The use of highly-specific, readily visualised and adjustable labels would help mitigate this issue, improving both data quality and sample throughput. While progress has been made in cryo-CLEM and in the development of cloneable high-density tags, technical issues persist and a robust 'cryo-GFP' remains elusive. Readily-synthesized gold nanomaterials conjugated to small 'affinity modules' may represent a solution. The synthesis of materials including gold nanoclusters (AuNCs) and gold nanoparticles (AuNPs) is increasingly well understood and is now within the capabilities of non-specialist laboratories. The remarkable chemical and photophysical properties of <3nm diameter nanomaterials and their emergence as tools with widespread biomedical application presents significant opportunities to the cryo-microscopy community. In this review, we will outline developments in the synthesis, functionalisation and labelling uses of both AuNPs and AuNCs in cryo-ET, while discussing their potential as multi-modal probes for cryo-CLEM

Solving Challenging Structures using Single-Particle Cryogenic Electron Microscopy

Single-particle cryogenic electron microscopy (cryo-EM) has become a powerful mainstay tool in high resolution structural biology thanks to advances in hardware, software and sample preparation technology. In my thesis, I utilized this technique to unravel the function of various challenging biological macromolecules. My first focus was bacterial ribosomal biogenesis: understanding how bacteria assemble their ribosomes. Ribosomes are the factories of the cell, responsible for manufacturing all proteins. Ribosomes themselves are huge, with the bacterial version made of 52 proteins and 4566 RNA nucleotides. How these components assemble has long been a mystery. Early groundbreaking work sketched out a biogenesis pathway using purified components in vitro – but under non-physiological conditions. We sought to understand how the bacterial ribosome – specifically the large subunit 50S – is built inside the cell. To achieve this, we engineered a conditional knock-out bacterial strain that lacked one specific ribosomal protein (L17). This caused the cells to accumulate incomplete intermediates along the 50S biogenesis pathway. These intermediates were purified and examined with mass spectrometry and single-particle cryo-EM. Two major hurdles arose in this project: firstly, the biogenesis intermediates exhibited a preferred orientation when vitrified for cryo-EM analysis. This means that instead of showing many different views required for reconstruction of the 3D structure, the intermediates only adopted one view on the cryo-EM grid. To overcome this problem, we engineered a method to induce additional views on the microscope by tilting the stage. Using another test protein that also exhibited preferred orientation (hemagglutinin), we optimized and characterized this new tilt methodology and showed it was generally applicable to overcoming preferred orientation, regardless of type of specimen. We also created a software tool, called 3DFSC (3dfsc.salk.edu), for other microscopists to calculate the degree of directional anisotropy in their structures due to preferred orientation. Using this tilt strategy finally enabled the structural elucidation of our 50S intermediates. The second challenge in the project was the large amount of heterogeneity present in the sample. Through hierarchical 3D classification schemes using the latest software tools, we obtained 14 different 50S intermediate structures, all from imaging a single cryo-EM grid. By analyzing the missing components of each intermediate, and corroborating these observations with mass spectrometry data, we outlined the first in vivo 50S assembly pathway, and showed that ribosome assembly occurs step-wise and in parallel pathways. My second focus was on pushing the resolution limits of single-particle cryo-EM using adeno-associated virus (AAV) serotype 2 homogeneous virus-like particles (VLPs) that lack DNA. Exploiting several technical advances to improve resolution, including use of gold grids, per-particle CTF refinement, and correction for Ewald sphere curvature, we managed to obtain a 1.86 Å resolution reconstruction of the AAV2L336C variant VLP, the highest resolution icosahedral virus reconstruction solved by single-particle cryo-EM to date. Using our structure, we were able to show improvements using Ewald sphere curvature correction and shed light on the mechanistic basis as to why the L336C mutation resulted in defects in genome packaging and infectivity compared to the WT viral particles. My third focus was the understanding of small membrane proteins involved in infectious diseases. Membrane proteins are a challenge to work with due to the need for them to be extracted from the lipid bilayer for studies as compared to soluble proteins. Infectious diseases have a huge burden on society, with the top three infectious agents accounting for 2.7 million deaths in 2016. The third most deadly infectious disease is malaria, a mosquito-borne parasite which kills 450,000 people annually. One drug used early on for treating malaria was chloroquine but its usefulness waned due to development of resistance. Chloroquine resistance is mediated by the chloroquine resistance transporter (PfCRT). Although small (49 kDa) for single-particle cryo-EM, we solved its structure by using fragment antibody technology to add mass and help with image alignment and 3D reconstruction. The 3.2 Å structure resembles other drug metabolite transporters, and the chloroquine resistance mutations map to a ring around the central cavity, suggesting this central pore as the drug binding site. Tuberculosis (TB) is the top killer, above malaria and HIV/AIDS, being responsible for 1.3 million deaths. In TB, a common antibiotic target is the bacterium’s cell wall synthesis machinery. One family of such enzymes is the arabinosyltransferases, which synthesize the critical arabinose sugars. Using single-particle cryo-EM, we solved two high resolution structures of one such essential enzyme, AftD. Due to the low yield of the protein, a picoliter automated sample dispensing robot was crucial to allow for initial cryo-EM analysis. We then performed mutagenesis studies in M. smegmatis, a TB model organism, which uncovered the critical amino acid residues in the active site and determined that a bound acyl-carrier-protein was likely involved in allosteric inhibition of AftD’s active site. Another member of the family, EmbB, is the target of a widely used frontline TB drug called ethambutol. We have solved the high resolution structures of the apo and putative drug-bound states of EmbB, allowing us to map out, for the first time, both the active site and drug-resistance mutations of this crucial enzyme. The atomic structures of the functional pockets of Mycobacterial AftD and malarial PfCRT will hopefully enable structure-based drug design to improve existing drugs or potentially even develop new treatments against these infectious maladies. In conclusion, the continual and breathtaking improvements in single-particle cryo-EM methodology has been instrumental in allowing the elucidation of the aforementioned biological macromolecules from ribosome biogenesis intermediates, to AAV2 vehicle, Plasmodium drug resistance transporter to mycobacterial glycosyltransferases – structures of which help explain biological function

Single particle 2D Electron crystallography for membrane protein structure determination

Proteins embedded into or attached to the cellular membrane perform crucial biological functions. Despite such importance, they remain among the most challenging targets of structural biology. Dedicated methods for membrane protein structure determination have been devised since decades, however with only partial success if compared to soluble proteins. One of these methods is 2D electron crystallography, in which the proteins are periodically arranged into a lipid bilayer. Using transmission electron microscopy to acquire projection images of samples containing such 2D crystals, which are embedded into a thin vitreous ice layer for radiation protection (cryo-EM), computer algorithms can be used to generate a 3D reconstruction of the protein. Unfortunately, in nearly every case, the 2D crystals are not flat and ordered enough to yield high-resolution reconstructions. Single particle analysis, on the other hand, is a technique that aligns projections of proteins isolated in solution in order to obtain a 3D reconstruction with a high success rate in terms of high resolution structures. In this thesis, we couple 2D crystal data processing with single particle analysis algorithms in order to perform a local correction of crystal distortions. We show that this approach not only allows reconstructions of much higher resolution than expected from the diffraction patterns obtained, but also reveals the existence of conformational heterogeneity within the 2D crystals. This structural variability can be linked to protein function, providing novel mechanistic insights and an explanation for why 2D crystals do not diffract to high resolution, in general. We present the computational methods that enable this hybrid approach, as well as other tools that aid several steps of cryo-EM data processing, from storage to postprocessing

Vers la classification non-supervisée des complexes macromoléculaires en cryo-tomographie électronique : Défis et opportunités

International audienceBackground and Objectives: Cryo electron tomography visualizes native cells at nanometer resolution, but analysis is challenged by noise and artifacts. Recently, supervised deep learning methods have been applied to decipher the 3D spatial distribution of macromolecules. However, in order to discover unknown objects, unsupervised classification techniques are necessary. In this paper, we provide an overview of unsupervised deep learning techniques, discuss the challenges to analyze cryo-ET data, and provide a proof-of-concept on real data. Methods: We propose a weakly supervised subtomogram classification method based on transfer learning. We use a deep neural network to learn a clustering friendly representation able to capture 3D shapes in the presence of noise and artifacts. This representation is learned here from a synthetic data set. Results: We show that when applying k-means clustering given a learning-based representation, it becomes possible to satisfyingly classify real subtomograms according to structural similarity. It is worth noting that no manual annotation is used for performing classification. Conclusions: We describe the advantages and limitations of our proof-of-concept and raise several perspectives for improving classification performance

A guide to machine learning for biologists

The expanding scale and inherent complexity of biological data have encouraged a growing use of machine learning in biology to build informative and predictive models of the underlying biological processes. All machine learning techniques fit models to data; however, the specific methods are quite varied and can at first glance seem bewildering. In this Review, we aim to provide readers with a gentle introduction to a few key machine learning techniques, including the most recently developed and widely used techniques involving deep neural networks. We describe how different techniques may be suited to specific types of biological data, and also discuss some best practices and points to consider when one is embarking on experiments involving machine learning. Some emerging directions in machine learning methodology are also discussed