Automated segmentation of molecular subunits in electron cryomicroscopy density maps

EvoSeg: Automated Electron Microscopy Segmentation through Random Forests and Evolutionary Optimization

Electron Microscopy Maps are key in the study of bio-molecular structures, ranging from borderline atomic level to the sub-cellular range. These maps describe the envelopes that cover possibly a very large number of proteins that form molecular machines within the cell. Within those envelopes, we are interested to find what regions correspond to specific proteins so that we can understand how they function, and design drugs that can enhance or suppress a process that they are involved in, along with other experimental purposes. A classic approach by which we can begin the exploration of map regions is to apply a segmentation algorithm. This yields a mask where each voxel in 3D space is assigned an identifier that maps it to a segment; an ideal segmentation would map each segment to one protein unit, which is rarely the case. In this work, we present a method that uses bio-inspired optimization, through an Evolutionary-Optimized Segmentation algorithm, to iteratively improve upon baseline segments obtained from a classical approach, called watershed segmentation. The cost function used by the evolutionary optimization is based on an ideal segmentation classifier trained as part of this development, which uses basic structural information available to scientists, such as the number of expected units, volume and topology. We show that a basic initial segmentation with the additional information allows our evolutionary method to find better segmentation results, compared to the baseline generated by the watershed.

Map segmentation, automated model-building and their application to the Cryo-EM Model Challenge

A recently-developed method for identifying a compact, contiguous region representing the unique part of a density map was applied to 218 Cryo-EM maps with resolutions of 4.5 Å or better. The key elements of the segmentation procedure are (1) identification of all regions of density above a threshold and (2) choice of a unique set of these regions, taking symmetry into consideration, that maximize connectivity and compactness. This segmentation approach was then combined with tools for automated map sharpening and model-building to generate models for the 12 maps in the 2016 Cryo-EM Model Challenge in a fully automated manner. The resulting models have completeness from 24% to 82% and RMS distances from reference interpretations of 0.6 Å-2.1 Å.

In Situ Imaging of Bacterial Secretion Systems by Electron Cryotomography

The unique property of electron cryotomography (ECT) is its capability to resolve the structure of macromolecular machines in their cellular context. The integration of ECT data with high-resolution structures of purified subcomplexes and live-cell fluorescence light microscopy can generate pseudo-atomic models that lead to a mechanistic understanding across size and time scales. Recent advances in electron detection, sample thinning, data acquisition, and data processing have significantly enhanced the applicability and performance of ECT. Here we describe a detailed workflow for an ECT experiment, including cell culture, vitrification, data acquisition, data reconstruction, tomogram analysis, and subtomogram averaging. This protocol provides an entry point to the technique for students and researchers and indicates the many possible variations arising from specific target properties and the available instrumentation.

Finding the needle in the haystack: towards solving the protein-folding problem computationally

Prediction of protein tertiary structures from amino acid sequence and understanding the mechanisms of how proteins fold, collectively known as "the protein folding problem," has been a grand challenge in molecular biology for over half a century. Theories have been developed that provide us with an unprecedented understanding of protein folding mechanisms. However, computational simulation of protein folding is still difficult, and prediction of protein tertiary structure from amino acid sequence is an unsolved problem. Progress toward a satisfying solution has been slow due to challenges in sampling the vast conformational space and deriving sufficiently accurate energy functions. Nevertheless, several techniques and algorithms have been adopted to overcome these challenges, and the last two decades have seen exciting advances in enhanced sampling algorithms, computational power and tertiary structure prediction methodologies. This review aims at summarizing these computational techniques, specifically conformational sampling algorithms and energy approximations that have been frequently used to study protein-folding mechanisms or to de novo predict protein tertiary structures. We hope that this review can serve as an overview on how the protein-folding problem can be studied computationally and, in cases where experimental approaches are prohibitive, help the researcher choose the most relevant computational approach for the problem at hand. We conclude with a summary of current challenges faced and an outlook on potential future directions.

Big data in cryoEM: automated collection, processing and accessibility of EM data

The scope and complexity of cryogenic electron microscopy (cryoEM) data has greatly increased, and will continue to do so, due to recent and ongoing technical breakthroughs that have led to much improved resolutions for macromolecular structures solved using this method. This big data explosion includes single particle data as well as tomographic tilt series, both generally acquired as direct detector movies of ∼10-100 frames per image or per tilt-series. We provide a brief survey of the developments leading to the current status, and describe existing cryoEM pipelines, with an emphasis on the scope of data acquisition, methods for automation, and use of cloud storage and computing.

RAZA: A Rapid 3D z-crossings algorithm to segment electron tomograms and extract organelles and macromolecules

Resolving the 3D architecture of cells to atomic resolution is one of the most ambitious challenges of cellular and structural biology. Central to this process is the ability to automate tomogram segmentation to identify sub-cellular components, facilitate molecular docking and annotate detected objects with associated metadata. Here we demonstrate that RAZA (Rapid 3D z-crossings algorithm) provides a robust, accurate, intuitive, fast, and generally applicable segmentation algorithm capable of detecting organelles, membranes, macromolecular assemblies and extrinsic membrane protein domains. RAZA defines each continuous contour within a tomogram as a discrete object and extracts a set of 3D structural fingerprints (major, middle and minor axes, surface area and volume), enabling selective, semi-automated segmentation and object extraction. RAZA takes advantage of the fact that the underlying algorithm is a true 3D edge detector, allowing the axes of a detected object to be defined, independent of its random orientation within a cellular tomogram. The selectivity of object segmentation and extraction can be controlled by specifying a user-defined detection tolerance threshold for each fingerprint parameter, within which segmented objects must fall and/or by altering the number of search parameters, to define morphologically similar structures. We demonstrate the capability of RAZA to selectively extract subgroups of organelles (mitochondria) and macromolecular assemblies (ribosomes) from cellular tomograms. Furthermore, the ability of RAZA to define objects and their contours, provides a basis for molecular docking and rapid tomogram annotation.

PRISM-EM: template interface-based modelling of multi-protein complexes guided by cryo-electron microscopy density maps

The structures of protein assemblies are important for elucidating cellular processes at the molecular level. Three-dimensional electron microscopy (3DEM) is a powerful method to identify the structures of assemblies, especially those that are challenging to study by crystallography. Here, a new approach, PRISM-EM, is reported to computationally generate plausible structural models using a procedure that combines crystallographic structures and density maps obtained from 3DEM. The predictions are validated against seven available structurally different crystallographic complexes. The models display mean deviations in the backbone of <5 Å. PRISM-EM was further tested on different benchmark sets; the accuracy was evaluated with respect to the structure of the complex, and the correlation with EM density maps and interface predictions were evaluated and compared with those obtained using other methods. PRISM-EM was then used to predict the structure of the ternary complex of the HIV-1 envelope glycoprotein trimer, the ligand CD4 and the neutralizing protein m36.

De Novo modeling in cryo-EM density maps with Pathwalking

As electron cryo-microscopy (cryo-EM) can now frequently achieve near atomic resolution, accurate interpretation of these density maps in terms of atomistic detail has become paramount in deciphering macromolecular structure and function. However, there are few software tools for modeling protein structure from cryo-EM density maps in this resolution range. Here, we present an extension of our original Pathwalking protocol, which can automatically trace a protein backbone directly from a near-atomic resolution (3-6Å) density map. The original Pathwalking approach utilized a Traveling Salesman Problem solver for backbone tracing, but manual adjustment was still required during modeling. In the new version, human intervention is minimized and we provide a more robust approach for backbone modeling. This includes iterative secondary structure identification, termini detection and the ability to model multiple subunits without prior segmentation. Overall, the new Pathwalking procedure provides a more complete and robust tool for annotating protein structure function in near-atomic resolution density maps.

PF2fit: Polar Fast Fourier Matched Alignment of Atomistic Structures with 3D Electron Microscopy Maps

There continue to be increasing occurrences of both atomistic structure models in the PDB (possibly reconstructed from X-ray diffraction or NMR data), and 3D reconstructed cryo-electron microscopy (3D EM) maps (albeit at coarser resolution) of the same or homologous molecule or molecular assembly, deposited in the EMDB. To obtain the best possible structural model of the molecule at the best achievable resolution, and without any missing gaps, one typically aligns (match and fits) the atomistic structure model with the 3D EM map. We discuss a new algorithm and generalized framework, named PF(2) fit (Polar Fast Fourier Fitting) for the best possible structural alignment of atomistic structures with 3D EM. While PF(2) fit enables only a rigid, six dimensional (6D) alignment method, it augments prior work on 6D X-ray structure and 3D EM alignment in multiple ways: Scoring. PF(2) fit includes a new scoring scheme that, in addition to rewarding overlaps between the volumes occupied by the atomistic structure and 3D EM map, rewards overlaps between the volumes complementary to them. We quantitatively demonstrate how this new complementary scoring scheme improves upon existing approaches. PF(2) fit also includes two scoring functions, the non-uniform exterior penalty and the skeleton-secondary structure score, and implements the scattering potential score as an alternative to traditional Gaussian blurring. Search. PF(2) fit utilizes a fast polar Fourier search scheme, whose main advantage is the ability to search over uniformly and adaptively sampled subsets of the space of rigid-body motions. PF(2) fit also implements a new reranking search and scoring methodology that considerably improves alignment metrics in results obtained from the initial search.

High-resolution cryo-electron microscopy structure of the Trypanosoma brucei ribosome

Ribosomes, the protein factories of living cells, translate genetic information carried by messenger RNAs into proteins, and are thus involved in virtually all aspects of cellular development and maintenance. The few available structures of the eukaryotic ribosome reveal that it is more complex than its prokaryotic counterpart, owing mainly to the presence of eukaryote-specific ribosomal proteins and additional ribosomal RNA insertions, called expansion segments. The structures also differ among species, partly in the size and arrangement of these expansion segments. Such differences are extreme in kinetoplastids, unicellular eukaryotic parasites often infectious to humans. Here we present a high-resolution cryo-electron microscopy structure of the ribosome of Trypanosoma brucei, the parasite that is transmitted by the tsetse fly and that causes African sleeping sickness. The atomic model reveals the unique features of this ribosome, characterized mainly by the presence of unusually large expansion segments and ribosomal-protein extensions leading to the formation of four additional inter-subunit bridges. We also find additional rRNA insertions, including one large rRNA domain that is not found in other eukaryotes. Furthermore, the structure reveals the five cleavage sites of the kinetoplastid large ribosomal subunit (LSU) rRNA chain, which is known to be cleaved uniquely into six pieces, and suggests that the cleavage is important for the maintenance of the T. brucei ribosome in the observed structure. We discuss several possible implications of the large rRNA expansion segments for the translation-regulation process. The structure could serve as a basis for future experiments aimed at understanding the functional importance of these kinetoplastid-specific ribosomal features in protein-translation regulation, an essential step towards finding effective and safe kinetoplastid-specific drugs.

Nhs: network-based hierarchical segmentation for cryo-electron microscopy density maps

Cryo-electron microscopy (cryo-EM) experiments yield low-resolution (3-30 Å) 3D-density maps of macromolecules. These density maps are segmented to identify structurally distinct proteins, protein domains, and subunits. Such partitioning aids the inference of protein motions and guides fitting of high-resolution atomistic structures. Cryo-EM density map segmentation has traditionally required tedious and subjective manual partitioning or semisupervised computational methods, whereas validation of resulting segmentations has remained an open problem in this field. We introduce a network-based hierarchical segmentation (Nhs) method, that provides a multi-scale partitioning, reflecting local and global clustering, while requiring no user input. This approach models each map as a graph, where map voxels constitute nodes and weighted edges connect neighboring voxels. Nhs initiates Markov diffusion (or random walk) on the weighted graph. As Markov probabilities homogenize through diffusion, an intrinsic segmentation emerges. We validate the segmentations with ground-truth maps based on atomistic models. When implemented on density maps in the 2010 Cryo-EM Modeling Challenge, Nhs efficiently and objectively partitions macromolecules into structurally and functionally relevant subregions at multiple scales.

Macromolecular structure modeling from 3D EM using VolRover 2.0

We review tools for structure identification and model-based refinement from three-dimensional electron microscopy implemented in our in-house software package, VOLROVER 2.0. For viral density maps with icosahedral symmetry, we segment the capsid, polymeric, and monomeric subunits using techniques based on automatic symmetry detection and multidomain fast marching. For large biomolecules without symmetry information, we again use our multidomain fast-marching method with manual or fit-based multiseeding to segment meaningful substructures. In either case, we subject the resulting segmented subunit to secondary structure detection when the EM resolution is sufficiently high, and rigid-body structure fitting when the corresponding X-ray structure is available. Secondary structure elements are identified by three techniques: our earlier volume-based and boundary-based skeletonization methods as well as a new method, currently in development, based on solving the grassfire flow equation. For rigid-body fitting, we adapt our earlier fast Fourier-based correlation scheme F2Dock. Our reported segmentation, secondary structure elements identification, and rigid-body fitting techniques, implemented in VOLROVER 2.0 are applied to the PSB 2011 cryo-EM modeling challenge data, and our results are briefly compared to similar results submitted from other research groups. The comparisons show that our techniques are equally capable of segmenting relatively accurate subunits from a viral or protein assembly, and that high segmentation quality leads in turn to higher-quality results of secondary structure elements identification and correlation-based rigid-body fitting. © 2012 Wiley Periodicals, Inc. Biopolymers 97: 709-731, 2012.

Comparison of Segger and other methods for segmentation and rigid-body docking of molecular components in cryo-EM density maps

Segmentation and docking are useful methods for the discovery of molecular components in electron cryo-microscopy (cryo-EM) density maps of macromolecular complexes. In this article, we describe the segmentation and docking methods implemented in Segger. For 11 targets posted in the 2010 cryo-EM challenge, we segmented the regions corresponding to individual molecular components using Segger. We then used the segmented regions to guide rigid-body docking of individual components. Docking results were evaluated by comparing the docked components with published structures, and by calculation of several scores, such as atom inclusion, density occupancy, and geometry clash. The accuracy of the component segmentation using Segger and other methods was assessed by comparing segmented regions with docked components.

Visualization and analysis of 3D microscopic images

In a wide range of biological studies, it is highly desirable to visualize and analyze three-dimensional (3D) microscopic images. In this primer, we first introduce several major methods for visualizing typical 3D images and related multi-scale, multi-time-point, multi-color data sets. Then, we discuss three key categories of image analysis tasks, namely segmentation, registration, and annotation. We demonstrate how to pipeline these visualization and analysis modules using examples of profiling the single-cell gene-expression of C. elegans and constructing a map of stereotyped neurite tracts in a fruit fly brain.

Reconstructing virus structures from nanometer to near-atomic resolutions with cryo-electron microscopy and tomography

The past few decades have seen tremendous advances in single-particle electron -cryo-microscopy (cryo-EM). The field has matured to the point that near-atomic resolution density maps can be generated for icosahedral viruses without the need for crystallization. In parallel, substantial progress has been made in determining the structures of nonicosahedrally arranged proteins in viruses by employing either single-particle cryo-EM or cryo-electron tomography (cryo-ET). Implicit in this course have been the availability of a new generation of electron cryo-microscopes and the development of the computational tools that are essential for generating these maps and models. This methodology has enabled structural biologists to analyze structures in increasing detail for virus particles that are in different morphogenetic states. Furthermore, electron imaging of frozen, hydrated cells, in the process of being infected by viruses, has also opened up a new avenue for studying virus structures "in situ". Here we present the common techniques used to acquire and process cryo-EM and cryo-ET data and discuss their implications for structural virology both now and in the future.

Three-dimensional asymmetric reconstruction of tailed bacteriophage

A universal goal in studying the structures of macromolecules and macromolecular complexes by means of electron cryo-microscopy (cryo-TEM) and three-dimensional (3D) image reconstruction is the derivation of a reliable atomic or pseudoatomic model. Such a model provides the foundation for exploring in detail the mechanisms by which biomolecules function. Though a variety of highly ordered, symmetric specimens such as 2D crystals, helices, and icosahedral virus capsids have been studied by these methods at near-atomic resolution, until recently, numerous challenges have made it difficult to achieve sub-nanometer resolution with large (≥~500Å), asymmetric molecules such as the tailed bacteriophages. After briefly reviewing some of the history behind the development of asymmetric virus reconstructions, we use recent structural studies of the prolate phage ϕ29 as an example to illustrate the step-by-step procedures used to compute an asymmetric reconstruction at sub-nanometer resolution. In contrast to methods that have been employed to study other asymmetric complexes, we demonstrate how symmetries in the head and tail components of the phage can be exploited to obtain the structure of the entire phage in an expedited, stepwise process. Prospects for future enhancements to the procedures currently employed are noted in the concluding section.

Unified data resource for cryo-EM

Three-dimensional (3D) cryoelectron microscopy reconstruction methods are uniquely able to reveal structures of many important macromolecules and macromolecular complexes. EMDataBank.org, a joint effort of the Protein Databank in Europe (PDBe), the Research Collaboratory for Structural Bioinformatics (RCSB), and the National Center for Macromolecular Imaging (NCMI), is a "one-stop shop" resource for global deposition and retrieval of cryo-EM map, model, and associated metadata. The resource unifies public access to the two major EM Structural Data archives: EM Data Bank (EMDB) and Protein Data Bank (PDB), and facilitates use of EM structural data of macromolecules and macromolecular complexes by the wider scientific community.

EMDataBank.org: unified data resource for CryoEM

Cryo-electron microscopy reconstruction methods are uniquely able to reveal structures of many important macromolecules and macromolecular complexes. EMDataBank.org, a joint effort of the Protein Data Bank in Europe (PDBe), the Research Collaboratory for Structural Bioinformatics (RCSB) and the National Center for Macromolecular Imaging (NCMI), is a global ‘one-stop shop’ resource for deposition and retrieval of cryoEM maps, models and associated metadata. The resource unifies public access to the two major archives containing EM-based structural data: EM Data Bank (EMDB) and Protein Data Bank (PDB), and facilitates use of EM structural data of macromolecules and macromolecular complexes by the wider scientific community.

Quantitative analysis of cryo-EM density map segmentation by watershed and scale-space filtering, and fitting of structures by alignment to regions

Cryo-electron microscopy produces 3D density maps of molecular machines, which consist of various molecular components such as proteins and RNA. Segmentation of individual components in such maps is a challenging task, and is mostly accomplished interactively. We present an approach based on the immersive watershed method and grouping of the resulting regions using progressively smoothed maps. The method requires only three parameters: the segmentation threshold, a smoothing step size, and the number of smoothing steps. We first apply the method to maps generated from molecular structures and use a quantitative metric to measure the segmentation accuracy. The method does not attain perfect accuracy, however it produces single or small groups of regions that roughly match individual proteins or subunits. We also present two methods for fitting of structures into density maps, based on aligning the structures with single regions or small groups of regions. The first method aligns centers and principal axes, whereas the second aligns centers and then rotates the structure to find the best fit. We describe both interactive and automated ways of using these two methods. Finally, we show segmentation and fitting results for several experimentally-obtained density maps.

Analyses of subnanometer resolution cryo-EM density maps

Today, electron cryomicroscopy (cryo-EM) can routinely achieve subnanometer resolutions of complex macromolecular assemblies. From a density map, one can extract key structural and functional information using a variety of computational analysis tools. At subnanometer resolution, these tools make it possible to isolate individual subunits, identify secondary structures, and accurately fit atomic models. With several cryo-EM studies achieving resolutions beyond 5Å, computational modeling and feature recognition tools have been employed to construct backbone and atomic models of the protein components directly from a density map. In this chapter, we describe several common classes of computational tools that can be used to analyze and model subnanometer resolution reconstructions from cryo-EM. A general protocol for analyzing subnanometer resolution density maps is presented along with a full description of steps used in analyzing the 4.3Å resolution structure of Mm-cpn.

Molecular cryo-electron tomography of vitreous tissue sections: current challenges

Electron tomography of vitreous tissue sections (tissue TOVIS) allows the study of the three-dimensional structure of molecular complexes in a near-native cellular context. Its usage is, however, limited by an unfortunate combination of noisy and incomplete data, by a technically demanding sample preparation procedure, and by a disposition for specimen degradation during data collection. Here we outline some major challenges as experienced from the application of TOVIS to human skin. We further consider a number of practical measures as well as theoretical approaches for its future development.

The advent of near-atomic resolution in single-particle electron microscopy

Single-particle electron microscopy (EM) can provide structural information for a large variety of biological molecules, ranging from small proteins to large macromolecular assemblies, without the need to produce crystals. The year 2008 has become a landmark year for single-particle EM as for the first time density maps have been produced at a resolution that made it possible to trace protein backbones or even to build atomic models. In this review, we highlight some of the recent successes achieved by single-particle EM and describe the individual steps involved in producing a density map by this technique. We also discuss some of the remaining challenges and areas, in which further advances would have a great impact on the results that can be achieved by single-particle EM.

Interpretation of very low resolution X-ray electron-density maps using core objects

A novel approach to obtaining structural information from macromolecular X-ray data extending to resolutions as low as 20 A is presented. Following a simple map-segmentation procedure, the approximate shapes of the domains forming the structure are identified. A pattern-recognition comparative analysis of these shapes and those derived from the structures of domains from the PDB results in candidate structural models that can be used for a fit into the density map. It is shown that the placed candidate models can be employed for subsequent phase extension to higher resolution.

Identifying Components in 3D Density Maps of Protein Nanomachines by Multi-scale Segmentation

Segmentation of density maps obtained using cryo-electron microscopy (cryo-EM) is a challenging task, and is typically accomplished by time-intensive interactive methods. The goal of segmentation is to identify the regions inside the density map that correspond to individual components. We present a multi-scale segmentation method for accomplishing this task that requires very little user interaction. The method uses the concept of scale space, which is created by convolution of the input density map with a Gaussian filter. The latter process smoothes the density map. The standard deviation of the Gaussian filter is varied, with smaller values corresponding to finer scales and larger values to coarser scales. Each of the maps at different scales is segmented using the watershed method, which is very efficient, completely automatic, and does not require the specification of seed points. Some detail is lost in the smoothing process. A sharpening process reintroduces detail into the segmentation at the coarsest scale by using the segmentations at the finer scales. We apply the method to simulated density maps, where the exact segmentation (or ground truth) is known, and rigorously evaluate the accuracy of the resulting segmentations.

Computational approaches for automatic structural analysis of large biomolecular complexes

We present computational solutions to two problems of macromolecular structure interpretation from reconstructed three-dimensional electron microscopy (3D-EM) maps of large bio-molecular complexes at intermediate resolution (5A-15 A). The two problems addressed are: 1) 3D structural alignment (matching) between identified and segmented 3D maps of structure units (e.g. trimeric configuration of proteins), and 2) the secondary structure identification of a segmented protein 3D map (i.e.locations of alpha-helices, beta-sheets). For problem 1, we present an efficient algorithm to correlate spatially (and structurally) two 3D maps of structure units. Besides providing a similarity score between structure units, the algorithm yields an effective technique for resolution refinement of repeated structure units, by 3D alignment and averaging. For problem 2, we present an efficient algorithm to compute eigenvalues and link eigenvectors of a Gaussian convoluted structure tensor derived from the protein 3D Map, thereby identifying and locating secondary structural motifs of proteins. The efficiency and performance of our approach is demonstrated on several experimentally reconstructed 3D maps of virus capsid shells from single-particle cryo-electron microscopy (cryo-EM), as well as computationally simulated protein structure density 3D maps generated from protein model entries in the Protein Data Bank.

Three-dimensional geometric modeling of membrane-bound organelles in ventricular myocytes: bridging the gap between microscopic imaging and mathematical simulation

A general framework of image-based geometric processing is presented to bridge the gap between three-dimensional (3D) imaging that provides structural details of a biological system and mathematical simulation where high-quality surface or volumetric meshes are required. A 3D density map is processed in the order of image pre-processing (contrast enhancement and anisotropic filtering), feature extraction (boundary segmentation and skeletonization), and high-quality and realistic surface (triangular) and volumetric (tetrahedral) mesh generation. While the tool-chain described is applicable to general types of 3D imaging data, the performance is demonstrated specifically on membrane-bound organelles in ventricular myocytes that are imaged and reconstructed with electron microscopic (EM) tomography and two-photon microscopy (T-PM). Of particular interest in this study are two types of membrane-bound Ca(2+)-handling organelles, namely, transverse tubules (T-tubules) and junctional sarcoplasmic reticulum (jSR), both of which play an important role in regulating the excitation-contraction (E-C) coupling through dynamic Ca(2+) mobilization in cardiomyocytes.

Integrating diverse data for structure determination of macromolecular assemblies

To understand the cell, we need to determine the macromolecular assembly structures, which may consist of tens to hundreds of components. First, we review the varied experimental data that characterize the assemblies at several levels of resolution. We then describe computational methods for generating the structures using these data. To maximize completeness, resolution, accuracy, precision, and efficiency of the structure determination, a computational approach is required that uses spatial information from a variety of experimental methods. We propose such an approach, defined by its three main components: a hierarchical representation of the assembly, a scoring function consisting of spatial restraints derived from experimental data, and an optimization method that generates structures consistent with the data. This approach is illustrated by determining the configuration of the 456 proteins in the nuclear pore complex (NPC) from baker's yeast. With these tools, we are poised to integrate structural information gathered at multiple levels of the biological hierarchy--from atoms to cells--into a common framework.

JUST (Java User Segmentation Tool) for semi-automatic segmentation of tomographic maps

We are presenting a program for interactive segmentation of tomographic maps, based on objective criteria so as to yield reproducible results. The strategy starts with the automatic segmentation of the entire volume with the watershed algorithm in 3D. The watershed regions are clustered successively by supervised classification, allowing the segmentation of known organelles, such as membranes, vesicles and microtubules. These organelles are processed with topological models and input parameters manually derived from the tomograms. After known organelles are extracted from the volume, all other watershed regions can be organized into homogeneous assemblies on the basis of their densities. To complete the process, all voxels in the volume are assigned either to the background or individual structures, which can then be extracted for visualization with any rendering technique. The user interface of the program is written in Java, and computational routines are written in C. For some operations, involving the visualization of the tomogram, we refer to existing software, either open or commercial. While the program runs, a history file is created, that allows all parameters and other data to be saved for the purposes of comparison or exchange. Initially, the program was developed for the segmentation of synapses, and organelles belonging to these structures have thus far been the principal targets modeled with JUST. Since each organelle is clustered independently from the rest of the volume, however, the program can accommodate new models of different organelles as well as tomograms of other types of preparations of tissue, such as cytoskeletal components in vitreous ice.

AUTOMATIC STRUCTURE INTERPRETATION OF SINGLE PARTICLE CRYO-ELECTRON MICROSCOPY: FROM IMAGES TO PSUEDO-ATOMIC MODELS

Three dimensional Electron Microscopy (EM) and in particular single particle reconstruction using cryo-EM, has rapidly advanced over recent years, such that increasingly several macromolecular complexes can be resolved at subnanometer resolution (6-10 Å). This paper reviews some of the main volumetric image and geometric post-processing steps once a three dimensional EM map (henceforth a 3D map) has been reconstructed from single particle Cryo-EM, as essential steps in an enhanced and automated computational structure interpretation pipeline. In particular the paper addresses automated filtering, critical point calculations, symmetric and non-symmetric molecular domain segmentation, molecular surface selection, curation, and protein secondary structure (α- helices and β-sheets) elucidation from 3D maps.