SwarmPS: rapid, semi-automated single particle selection software

Machine Learning for Structure Determination in Single-Particle Cryo-Electron Microscopy: A Systematic Review

Recently, single-particle cryo-electron microscopy (cryo-EM) has become an indispensable method for determining macromolecular structures at high resolution to deeply explore the relevant molecular mechanism. Its recent breakthrough is mainly because of the rapid advances in hardware and image processing algorithms, especially machine learning. As an essential support of single-particle cryo-EM, machine learning has powered many aspects of structure determination and greatly promoted its development. In this article, we provide a systematic review of the applications of machine learning in this field. Our review begins with a brief introduction of single-particle cryo-EM, followed by the specific tasks and challenges of its image processing. Then, focusing on the workflow of structure determination, we describe relevant machine learning algorithms and applications at different steps, including particle picking, 2-D clustering, 3-D reconstruction, and other steps. As different tasks exhibit distinct characteristics, we introduce the evaluation metrics for each task and summarize their dynamics of technology development. Finally, we discuss the open issues and potential trends in this promising field.

DeepCryoPicker: fully automated deep neural network for single protein particle picking in cryo-EM

BACKGROUND

Cryo-electron microscopy (Cryo-EM) is widely used in the determination of the three-dimensional (3D) structures of macromolecules. Particle picking from 2D micrographs remains a challenging early step in the Cryo-EM pipeline due to the diversity of particle shapes and the extremely low signal-to-noise ratio of micrographs. Because of these issues, significant human intervention is often required to generate a high-quality set of particles for input to the downstream structure determination steps.

RESULTS

Here we propose a fully automated approach (DeepCryoPicker) for single particle picking based on deep learning. It first uses automated unsupervised learning to generate particle training datasets. Then it trains a deep neural network to classify particles automatically. Results indicate that the DeepCryoPicker compares favorably with semi-automated methods such as DeepEM, DeepPicker, and RELION, with the significant advantage of not requiring human intervention.

CONCLUSIONS

Our framework combing supervised deep learning classification with automated un-supervised clustering for generating training data provides an effective approach to pick particles in cryo-EM images automatically and accurately.

Macromolecule Particle Picking and Segmentation of a KLH Database by Unsupervised Cryo-EM Image Processing

Segmentation is one of the most important stages in the 3D reconstruction of macromolecule structures in cryo-electron microscopy. Due to the variability of macromolecules and the low signal-to-noise ratio of the structures present, there is no generally satisfactory solution to this process. This work proposes a new unsupervised particle picking and segmentation algorithm based on the composition of two well-known image filters: Anisotropic (Perona-Malik) diffusion and non-negative matrix factorization. This study focused on keyhole limpet hemocyanin (KLH) macromolecules which offer both a top view and a side view. Our proposal was able to detect both types of views and separate them automatically. In our experiments, we used 30 images from the KLH dataset of 680 positive classified regions. The true positive rate was 95.1% for top views and 77.8% for side views. The false negative rate was 14.3%. Although the false positive rate was high at 21.8%, it can be lowered with a supervised classification technique.

Flexible workflows for on-the-fly electron-microscopy single-particle image processing using Scipion

Electron microscopy of macromolecular structures is an approach that is in increasing demand in the field of structural biology. The automation of image acquisition has greatly increased the potential throughput of electron microscopy. Here, the focus is on the possibilities in Scipion to implement flexible and robust image-processing workflows that allow the electron-microscope operator and the user to monitor the quality of image acquisition, assessing very simple acquisition measures or obtaining a first estimate of the initial volume, or the data resolution and heterogeneity, without any need for programming skills. These workflows can implement intelligent automatic decisions and they can warn the user of possible acquisition failures. These concepts are illustrated by analysis of the well known 2.2 Å resolution β-galactosidase data set.

Cryo-EM structures of the pore-forming A subunit from the Yersinia entomophaga ABC toxin

ABC toxins are pore-forming virulence factors produced by pathogenic bacteria. YenTcA is the pore-forming and membrane binding A subunit of the ABC toxin YenTc, produced by the insect pathogen Yersinia entomophaga. Here we present cryo-EM structures of YenTcA, purified from the native source. The soluble pre-pore structure, determined at an average resolution of 4.4 Å, reveals a pentameric assembly that in contrast to other characterised ABC toxins is formed by two TcA-like proteins (YenA1 and YenA2) and decorated by two endochitinases (Chi1 and Chi2). We also identify conformational changes that accompany membrane pore formation by visualising YenTcA inserted into liposomes. A clear outward rotation of the Chi1 subunits allows for access of the protruding translocation pore to the membrane. Our results highlight structural and functional diversity within the ABC toxin subfamily, explaining how different ABC toxins are capable of recognising diverse hosts.

New software tools in EMAN2 inspired by EMDatabank map challenge

EMAN2 is an extensible software suite with complete workflows for performing high-resolution single particle analysis, 2-D and 3-D heterogeneity analysis, and subtomogram averaging, among other tasks. Participation in the recent CryoEM Map Challenge sponsored by the EMDatabank led to a number of significant improvements to the single particle analysis process in EMAN2. A new convolutional neural network particle picker was developed, which dramatically improves particle picking accuracy for difficult data sets. A new particle quality metric capable of accurately identifying "bad" particles with a high degree of accuracy, no human input, and a negligible amount of additional computation, has been introduced, and this now serves as a replacement for earlier human-biased methods. The way 3-D single particle reconstructions are filtered has been altered to be more comparable to the filter applied in several other popular software packages, dramatically improving the appearance of sidechains in high-resolution structures. Finally, an option has been added to perform local resolution-based iterative filtration, resulting in local resolution improvements in many maps.

Opportunities and obstacles for deep learning in biology and medicine

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.

A deep convolutional neural network approach to single-particle recognition in cryo-electron microscopy

BACKGROUND

Single-particle cryo-electron microscopy (cryo-EM) has become a mainstream tool for the structural determination of biological macromolecular complexes. However, high-resolution cryo-EM reconstruction often requires hundreds of thousands of single-particle images. Particle extraction from experimental micrographs thus can be laborious and presents a major practical bottleneck in cryo-EM structural determination. Existing computational methods for particle picking often use low-resolution templates for particle matching, making them susceptible to reference-dependent bias. It is critical to develop a highly efficient template-free method for the automatic recognition of particle images from cryo-EM micrographs.

RESULTS

We developed a deep learning-based algorithmic framework, DeepEM, for single-particle recognition from noisy cryo-EM micrographs, enabling automated particle picking, selection and verification in an integrated fashion. The kernel of DeepEM is built upon a convolutional neural network (CNN) composed of eight layers, which can be recursively trained to be highly "knowledgeable". Our approach exhibits an improved performance and accuracy when tested on the standard KLH dataset. Application of DeepEM to several challenging experimental cryo-EM datasets demonstrated its ability to avoid the selection of un-wanted particles and non-particles even when true particles contain fewer features.

CONCLUSIONS

The DeepEM methodology, derived from a deep CNN, allows automated particle extraction from raw cryo-EM micrographs in the absence of a template. It demonstrates an improved performance, objectivity and accuracy. Application of this novel method is expected to free the labor involved in single-particle verification, significantly improving the efficiency of cryo-EM data processing.

High-resolution Single Particle Analysis from Electron Cryo-microscopy Images Using SPHIRE

SPHIRE (SPARX for High-Resolution Electron Microscopy) is a novel open-source, user-friendly software suite for the semi-automated processing of single particle electron cryo-microscopy (cryo-EM) data. The protocol presented here describes in detail how to obtain a near-atomic resolution structure starting from cryo-EM micrograph movies by guiding users through all steps of the single particle structure determination pipeline. These steps are controlled from the new SPHIRE graphical user interface and require minimum user intervention. Using this protocol, a 3.5 Å structure of TcdA1, a Tc toxin complex from Photorhabdus luminescens, was derived from only 9500 single particles. This streamlined approach will help novice users without extensive processing experience and a priori structural information, to obtain noise-free and unbiased atomic models of their purified macromolecular complexes in their native state.

Engineering Recombinant Virus-like Nanoparticles from Plants for Cellular Delivery

Understanding capsid assembly following recombinant expression of viral structural proteins is critical to the design and modification of virus-like nanoparticles for biomedical and nanotechnology applications. Here, we use plant-based transient expression of the Bluetongue virus (BTV) structural proteins, VP3 and VP7, to obtain high yields of empty and green fluorescent protein (GFP)-encapsidating core-like particles (CLPs) from leaves. Single-particle cryo-electron microscopy of both types of particles revealed considerable differences in CLP structure compared to the crystal structure of infection-derived CLPs; in contrast, the two recombinant CLPs have an identical external structure. Using this insight, we exploited the unencumbered pore at the 5-fold axis of symmetry and the absence of encapsidated RNA to label the interior of empty CLPs with a fluorescent bioconjugate. CLPs containing 120 GFP molecules and those containing approximately 150 dye molecules were both shown to bind human integrin via a naturally occurring Arg-Gly-Asp motif found on an exposed loop of the VP7 trimeric spike. Furthermore, fluorescently labeled CLPs were shown to interact with a cell line overexpressing the surface receptor. Thus, BTV CLPs present themselves as a useful tool in targeted cargo delivery. These results highlight the importance of detailed structural analysis of VNPs in validating their molecular organization and the value of such analyses in aiding their design and further modification.

A Two-Phase Improved Correlation Method for Automatic Particle Selection in Cryo-EM

Particle selection from cryo-electron microscopy (Cryo-EM) images is very important for high-resolution reconstruction of macromolecular structure. The methods of particle selection can be roughly grouped into two classes, template-matching methods and feature-based methods. In general, template-matching methods usually generate better results than feature-based methods. However, the accuracy of template-matching methods is restricted by the noise and low contrast of Cryo-EM images. Moreover, the processing speed of template-matching methods, restricted by the random orientation of particles, further limits their practical applications. In this paper, combining the advantages of feature-based methods and template-matching methods, we present a two-phase improved correlation method for automatic, fast particle selection. In Phase I, we generate a preliminary particle set using rotation-invariant features of particles. In Phase II, we filter the preliminary particle set using a correlation method to reduce the interference of the high noise background and improve the precision of particle selection. We apply several optimization strategies, including a modified adaboost algorithm, Divide and Conquer technique, cascade strategy and graphics processing unit parallel technique, to improve feature recognition ability and reduce processing time. In addition, we developed two correlation score functions for different correlation situations. Experimental results on the benchmark of Cryo-EM images show that our method can improve the accuracy and processing speed of particle selection significantly.

High resolution single particle refinement in EMAN2.1

EMAN2.1 is a complete image processing suite for quantitative analysis of grayscale images, with a primary focus on transmission electron microscopy, with complete workflows for performing high resolution single particle reconstruction, 2-D and 3-D heterogeneity analysis, random conical tilt reconstruction and subtomogram averaging, among other tasks. In this manuscript we provide the first detailed description of the high resolution single particle analysis pipeline and the philosophy behind its approach to the reconstruction problem. High resolution refinement is a fully automated process, and involves an advanced set of heuristics to select optimal algorithms for each specific refinement task. A gold standard FSC is produced automatically as part of refinement, providing a robust resolution estimate for the final map, and this is used to optimally filter the final CTF phase and amplitude corrected structure. Additional methods are in-place to reduce model bias during refinement, and to permit cross-validation using other computational methods.

A 3D image filter for parameter-free segmentation of macromolecular structures from electron tomograms

3D image reconstruction of large cellular volumes by electron tomography (ET) at high (≤ 5 nm) resolution can now routinely resolve organellar and compartmental membrane structures, protein coats, cytoskeletal filaments, and macromolecules. However, current image analysis methods for identifying in situ macromolecular structures within the crowded 3D ultrastructural landscape of a cell remain labor-intensive, time-consuming, and prone to user-bias and/or error. This paper demonstrates the development and application of a parameter-free, 3D implementation of the bilateral edge-detection (BLE) algorithm for the rapid and accurate segmentation of cellular tomograms. The performance of the 3D BLE filter has been tested on a range of synthetic and real biological data sets and validated against current leading filters-the pseudo 3D recursive and Canny filters. The performance of the 3D BLE filter was found to be comparable to or better than that of both the 3D recursive and Canny filters while offering the significant advantage that it requires no parameter input or optimisation. Edge widths as little as 2 pixels are reproducibly detected with signal intensity and grey scale values as low as 0.72% above the mean of the background noise. The 3D BLE thus provides an efficient method for the automated segmentation of complex cellular structures across multiple scales for further downstream processing, such as cellular annotation and sub-tomogram averaging, and provides a valuable tool for the accurate and high-throughput identification and annotation of 3D structural complexity at the subcellular level, as well as for mapping the spatial and temporal rearrangement of macromolecular assemblies in situ within cellular tomograms.

Structure of the dengue virus glycoprotein non-structural protein 1 by electron microscopy and single-particle analysis

The flavivirus non-structural protein 1 (NS1) is a glycoprotein that is secreted as a soluble hexameric complex during the course of natural infection. Growing evidence indicates that this secreted form of NS1 (sNS1) plays a significant role in immune evasion and modulation during infection. Attempts to determine the crystal structure of NS1 have been unsuccessful to date and relatively little is known about the macromolecular organization of the sNS1 hexamer. Here, we have applied single-particle analysis to images of baculovirus-derived recombinant dengue 2 virus NS1 obtained by electron microscopy to determine its 3D structure to a resolution of 23 Å. This structure reveals a barrel-like organization of the three dimeric units that comprise the hexamer and provides further insights into the overall organization of oligomeric sNS1.

3D structure of the Yersinia entomophaga toxin complex and implications for insecticidal activity

Toxin complex (Tc) proteins are a class of bacterial protein toxins that form large, multisubunit complexes. Comprising TcA, B, and C components, they are of great interest because many exhibit potent insecticidal activity. Here we report the structure of a novel Tc, Yen-Tc, isolated from the bacterium Yersinia entomophaga MH96, which differs from the majority of bacterially derived Tcs in that it exhibits oral activity toward a broad range of insect pests, including the diamondback moth (Plutella xylostella). We have determined the structure of the Yen-Tc using single particle electron microscopy and studied its mechanism of toxicity by comparative analyses of two variants of the complex exhibiting different toxicity profiles. We show that the A subunits form the basis of a fivefold symmetric assembly that differs substantially in structure and subunit arrangement from its most well characterized homologue, the Xenorhabdus nematophila toxin XptA1. Histopathological and quantitative dose response analyses identify the B and C subunits, which map to a single, surface-accessible region of the structure, as the sole determinants of toxicity. Finally, we show that the assembled Yen-Tc has endochitinase activity and attribute this to putative chitinase subunits that decorate the surface of the TcA scaffold, an observation that may explain the oral toxicity associated with the complex.

Reference-free particle selection enhanced with semi-supervised machine learning for cryo-electron microscopy

Reference-based methods have dominated the approaches to the particle selection problem, proving fast, and accurate on even the most challenging micrographs. A reference volume, however, is not always available and compiling a set of reference projections from the micrographs themselves requires significant effort to attain the same level of accuracy. We propose a reference-free method to quickly extract particles from the micrograph. The method is augmented with a new semi-supervised machine-learning algorithm to accurately discriminate particles from contaminants and noise.

A clarification of the terms used in comparing semi-automated particle selection algorithms in cryo-EM

Many cyro-EM datasets are heterogeneous stemming from molecules undergoing conformational changes. The need to characterize each of the substrates with sufficient resolution entails a large increase in the data flow and motivates the development of more effective automated particle selection algorithms. Concepts and procedures from the machine-learning field are increasingly employed toward this end. However, a review of recent literature has revealed a discrepancy in terminology of the performance scores used to compare particle selection algorithms, and this has subsequently led to ambiguities in the meaning of claimed performance. In an attempt to curtail the perpetuation of this confusion and to disentangle past mistakes, we review the performance of published particle selection efforts with a set of explicitly defined performance scores using the terminology established and accepted within the field of machine learning.

Proteomic and electron microscopy survey of large assemblies in macrophage cytoplasm

Many cellular processes are carried out by large macromolecular assemblies. We systematically analyzed large macromolecular assemblies in the cytoplasm of mouse macrophages (RAW264.7 cell line), cells with crucial roles in immunity and inflammation. Fractionation of the cytoplasmic fraction was performed using sucrose density gradient centrifugation, and individual fractions were subjected in parallel to (i) identification of constituent proteins by mass spectrometry and (ii) structural visualization by electron microscopy. Macromolecular assemblies present in the fractions were analyzed by integrating available data using bioinformatic approaches. We identified 368 unique proteins in our sample. Among these are components of some well-characterized assemblies involved in diverse cellular processes and structures including translation, proteolysis, protein folding, metabolism, and the cytoskeleton, as well as less characterized proteins that may correspond to additional components of known assemblies or other homo- or hetero-oligomeric structures. Single-particle analysis of electron micrographs of negatively stained samples allowed the identification of clearly distinguishable two-dimensional projections of discrete protein assemblies. Among these, we can identify small ribosomal subunits and preribosomal particles, the 26S proteasome complex and small ringlike structures resembling the molecular chaperone complexes. In addition, a broad range of discrete and different complexes were seen at size ranges between 11 to 38 nm in diameter. Our procedure selects the assemblies on the basis of abundance and ease of isolation, and therefore provides an immediately useful starting point for further study of structure and function of large assemblies. Our results will also contribute toward building a molecular cell atlas.

Single particle analysis at high resolution

Electron cryomicroscopy (cryo-EM) and single particle analysis is emerging as a powerful technique for determining the 3D structure of large biomolecules and biomolecular assemblies in close to their native solution environment. Over the last decade, this technology has improved, first to sub-nanometer resolution, and more recently beyond 0.5 nm resolution. Achieving sub-nanometer resolution is now readily approachable on mid-range microscopes with straightforward data processing, so long as the target specimen meets some basic requirements. Achieving resolutions beyond 0.5 nm currently requires a high-end microscope and careful data acquisition and processing, with much more stringent specimen requirements. This chapter will review and discuss the methodologies for determining high-resolution cryo-EM structures of nonvirus particles to sub-nanometer resolution and beyond, with a particular focus on the reconstruction strategy implemented in the EMAN software suite.

Structural mechanism of SDS-induced enzyme activity of scorpion hemocyanin revealed by electron cryomicroscopy

Phenoloxidases (POs) occur in all organisms and are involved in skin and hair coloring in mammals, and initiating melanization in wound healing. Mutation or overexpression of PO can cause albinism or melanoma, respectively. SDS can convert inactive PO and the oxygen carrier hemocyanin (Hc) into enzymatically active PO. Here we present single-particle cryo-EM maps at subnanometer resolution and pseudoatomic models of the 24-oligomeric Hc from scorpion Pandinus imperator in resting and SDS-activated states. Our structural analyses led to a plausible mechanism of Hc enzyme PO activation: upon SDS activation, the intrinsically flexible Hc domain I twists away from domains II and III in each subunit, exposing the entrance to the active site; this movement is stabilized by enhanced interhexamer and interdodecamer interactions, particularly in the central linker subunits. This mechanism could be applicable to other type 3 copper proteins, as the active site is highly conserved.

Three-dimensional structure of AAA ATPase Vps4: advancing structural insights into the mechanisms of endosomal sorting and enveloped virus budding

Vps4 is a AAA ATPase that mediates endosomal membrane protein sorting. It is also a host factor hijacked by a diverse set of clinically important viruses, including HIV and Ebola, to facilitate viral budding. Here we present the three-dimensional structure of the hydrolysis-defective Vps4p(E233Q) mutant. Single-particle analysis, multiangle laser light scattering, and the docking of independently determined atomic models of Vps4 monomers reveal a complex with C6 point symmetry, distinguishing between a range of previously suggested oligomeric states (8-14 subunits). The 3D reconstruction also reveals a tail-to-tail subunit organization between the two rings of the complex and identifies the location of domains critical to complex assembly and interaction with partner proteins. Our refined Vps4 structure is better supported by independent lines of evidence than those previously proposed, and provides insights into the mechanism of endosomal membrane protein sorting and viral envelope budding.

Cryoelectron microscopy map of Atadenovirus reveals cross-genus structural differences from human adenovirus

A three-dimensional (3D) cryoelectron microscopy reconstruction of the prototype Atadenovirus (OAdV [an ovine adenovirus isolate]) showing information at a 10.6-A resolution (0.5 Fourier shell correlation) was derived by single-particle analysis. This is the first 3D structure solved for any adenovirus that is not a Mastadenovirus, allowing cross-genus comparisons between structures and the assignment of genus-specific capsid proteins. Viable OAdV mutants that lacked the genus-specific LH3 and p32k proteins in purified virions were also generated. Negatively stained 3D reconstructions of these mutants were used to identify the location of protein LH3 and infer that of p32k within the capsid. The key finding was that LH3 is a critical protein that holds the outer capsid of the virus together. In its absence, the outer viral capsid is unstable. LH3 is located in the same position among the hexon subunits as its protein IX equivalent from mastadenoviruses but sits on top of the hexon trimers, forming prominent "knobs" on the virion surface that visually distinguish OAdV from other known AdVs. Electron density was also assigned to hexon and penton subunits and to proteins IIIa and VIII. There was good correspondence between OAdV density and human AdV hexon structures, which also validated the significant differences that were observed between the penton base protein structures.

Bilateral edge filter: photometrically weighted, discontinuity based edge detection

Edge-detection algorithms have the potential to play an increasingly important role both in single particle analysis (for the detection of randomly oriented particles), and in tomography (for the segmentation of 3D volumes). However, the majority of traditional linear filters are significantly affected by noise as well as artefacts, and offer limited selectivity. The Bilateral edge filter presented here is an adaptation of the Bilateral filter [Jiang, W., Baker, M.L., Wu, Q., Bajaj, C., Chiu, W., 2003. Applications of a bilateral denoising filter in biological electron microscopy. J. Struct. Biol. 144, 114-122] designed for enhanced edge detection. It uses photometric weighting to identify significant discontinuities (representing edges), minimizing artefacts and noise. Compared with common edge-detectors (LoG, Marr-Hildreth) the Bilateral edge filter yielded significantly better results. Indeed data was of a similar quality to that of the Canny edge-detector, which is considered as a leading standard in edge detection [Basu, M., 2002. Gaussian-based edge-detection methods-a survey. IEEE Trans. Syst. Man Cybern. C Appl. Rev. 32, 252-260]. Compared to the Canny edge-detector the Bilateral edge-detector has the advantages that it only requires the adjustment of a single parameter, is theoretically faster for reasonably sized images, and can be used in selective contrast enhancement of images. The simplicity and speed of the filter for single particle and tomographic analysis are discussed.