Alignment of 3D structures of macromolecular assemblies

MOTIVATION: A number of macromolecular assemblies are being reconstructed in 3D from electron micrographs. The analysis yields a 3D matrix representing the protein density map. In reconstruction processes and in comparing the results of different experiments, it is often necessary to obtain all models oriented the same way in three dimensions. The problem is not trivial since there exist no 3D counterpart of correlation analysis used for 2D images. It is usually solved by time consuming trial and error algorithms. RESULTS: 3D density distributions can be brought to a 'canonical' orientation. The tensor of inertia of the distribution is determined and its eigenvectors are oriented along the coordinate axes. The method is fast and essentially free of reference. It is suitable for structures whose inertial axes do not completely degenerate as they do in icosahedral viruses or if symmetry is cubic. Applications are presented for asymmetric objects and for molecules possessing symmetry axes higher than twofold. IMPLEMENTATION: The implementation simply requires the accumulation of the inertial tensor and its diagonalisation. Volume data rotation has been already illustrated in this journal by the authors.     

Automated segmentation of electron tomograms for a quantitative description of actin filament networks

Cryo-electron tomography allows to visualize individual actin filaments and to describe the three-dimensional organization of actin networks in the context of unperturbed cellular environments. For a quantitative characterization of actin filament networks, the tomograms must be segmented in a reproducible manner. Here, we describe an automated procedure for the segmentation of actin filaments, which combines template matching with a new tracing algorithm. The result is a set of lines, each one representing the central line of a filament. As demonstrated with cryo-tomograms of cellular actin networks, these line sets can be used to characterize filament networks in terms of filament length, orientation, density, stiffness (persistence length), or the occurrence of branching points.     

Pre-docking filter for protein and ligand 3D structures

Virtual drug screening using protein-ligand docking techniques is a time-consuming process, which requires high computational power for binding affinity calculation. There are millions of chemical compounds available for docking. Eliminating compounds that are unlikely to exhibit high binding affinity from the screening set should speed-up the virtual drug screening procedure. We performed docking of 6353 ligands against twenty-one protein X-ray crystal structures. The docked ligands were ranked according to their calculated binding affinities, from which the top five hundred and the bottom five hundred were selected. We found that the volume and number of rotatable bonds of the top five hundred docked ligands are similar to those found in the crystal structures and corresponded with the volume of the binding sites. In contrast, the bottom five hundred set contains ligands that are either too large to enter the binding site, or too small to bind with high specificity and affinity to the binding site. A pre-docking filter that takes into account shapes and volumes of the binding sites as well as ligand volumes and flexibilities can filter out low binding affinity ligands from the screening sets. Thus, the virtual drug screening procedure speed is increased.     

Mean curvature motion facilitates the segmentation and surface visualization of electron tomograms

Three-dimensional visualization of biological samples is essential for understanding their architecture and function. However, it is often challenging due to the macromolecular crowdedness of the samples and low signal-to-noise ratio of the cryo-electron tomograms. Denoising and segmentation techniques address this challenge by increasing the signal-to-noise ratio and by simplifying the data in images. Here, mean curvature motion is presented as a method that can be applied to segmentation results, created either manually or automatically, to significantly improve both the visual quality and downstream computational handling. Mean curvature motion is a process based on nonlinear anisotropic diffusion that smooths along edges and causes high-curvature features, such as noise, to disappear. In combination with level-set methods for image erosion and dilation, the application of mean curvature motion to electron tomograms and segmentations removes sharp edges or spikes in the visualized surfaces, produces an improved surface quality, and improves overall visualization and interpretation of the three-dimensional images.     

Benchmarking of deep learning algorithms for 3D instance segmentation of confocal image datasets

Segmenting three-dimensional (3D) microscopy images is essential for understanding phenomena like morphogenesis, cell division, cellular growth, and genetic expression patterns. Recently, deep learning (DL) pipelines have been developed, which claim to provide high accuracy segmentation of cellular images and are increasingly considered as the state of the art for image segmentation problems. However, it remains difficult to define their relative performances as the concurrent diversity and lack of uniform evaluation strategies makes it difficult to know how their results compare. In this paper, we first made an inventory of the available DL methods for 3D cell segmentation. We next implemented and quantitatively compared a number of representative DL pipelines, alongside a highly efficient non-DL method named MARS. The DL methods were trained on a common dataset of 3D cellular confocal microscopy images. Their segmentation accuracies were also tested in the presence of different image artifacts. A specific method for segmentation quality evaluation was adopted, which isolates segmentation errors due to under- or oversegmentation. This is complemented with a 3D visualization strategy for interactive exploration of segmentation quality. Our analysis shows that the DL pipelines have different levels of accuracy. Two of them, which are end-to-end 3D and were originally designed for cell boundary detection, show high performance and offer clear advantages in terms of adaptability to new data.     

Protonate3D: assignment of ionization states and hydrogen coordinates to macromolecular structures

A new method, called Protonate3D, is presented for the automated prediction of hydrogen coordinates given the 3D coordinates of the heavy atoms of a macromolecular structure. Protonate3D considers side-chain "flip," rotamer, tautomer, and ionization states of all chemical groups, ligands, and solvent, provided suitable templates are available in a parameter file. The energy model includes van der Waals, Coulomb, solvation, rotamer, tautomer, and titration effects. The results of computational validation experiments suggest that Protonate3D can accurately predict the location of hydrogen atoms in macromolecular structures.     

HYDROMIC: prediction of hydrodynamic properties of rigid macromolecular structures obtained from electron microscopy images

We have developed a procedure for the prediction of hydrodynamic coefficients and other solution properties of macromolecules and macromolecular complexes whose volumes have been generated from electron microscopy images. Starting from the structural files generated in the three-dimensional reconstructions of such molecules, it is possible to construct a hydrodynamic model for which the solution properties can be calculated. We have written a computer program, HYDROMIC, that implements all the stages of the calculation. The use of this procedure is illustrated with a calculation of the solution properties of the volume of the cytosolic chaperonin CCT, obtained from cryoelectron microscopy images.     

Edged watershed segmentation: A semi-interactive algorithm for segmentation of low-resolution maps from electron cryomicroscopy

Electron cryomicroscopy (cryo-EM) allows for the structural analysis of large protein complexes that may be difficult to study by other means. Frequently, maps of complexes from cryo-EM are obtained at resolutions between 10 and 25 Å. To aid in the interpretation of these medium- to low-resolution maps, they may be subdivided into three-dimensional segments representing subunits or subcomplexes. This division is often accomplished using a manual segmentation approach. While extremely useful, manual segmentation is subjective. We have developed a novel semi-interactive segmentation algorithm that can incorporate prior knowledge of subunit composition or structure without biasing the boundaries between subunits or subcomplexes. This algorithm has been characterized with experimental and simulated cryo-EM density maps at resolutions between 10 and 25 Å.     

WatershedCounting3D: a new method for segmenting and counting punctate structures from confocal image data

Current research in cell biology frequently uses light microscopy to study intracellular organelles. To segment and count organelles, most investigators have used a global thresholding method, which relies on homogeneous background intensity values within a cell. Because this is not always the case, we developed WatershedCounting3D, a program that uses a modified watershed algorithm to more accurately identify intracellular structures from confocal image data, even in the presence of an inhomogeneous background. We give examples of segmenting and counting endoplasmic reticulum exit sites and the Golgi apparatus.     

A visualization and segmentation toolbox for electron microscopy

Three-dimensional image analysis includes image processing, segmentation and visualization operations, which facilitate the interpretation of data. We have developed a toolbox for three-dimensional (3D) electron microscopy (EM) in Amira, which is a commercial software package, used by many laboratories. Our toolbox integrates a number of established procedures specifically tailored for 3D EM. These include input-output, filtering, segmentation, visualization and ray-tracing functions, which can be accessed directly from a user-friendly pop-up menu. They allow performing denoising and segmentation tasks directly in Amira, without the need of other programs, and ultimately allow the visualization of the results at photo-realistic quality with ray-tracing. They also allow a direct interaction with the data, such that, e.g., sub-tomograms can be directly extracted, or segmentation areas can be interactively selected. The implemented functions are fast, reliable and intuitive, yielding a comprehensive package for visualization in EM.     

A watershed approach for improving medical image segmentation

In this paper, a novel watershed approach based on seed region growing and image entropy is presented which could improve the medical image segmentation. The proposed algorithm enables the prior information of seed region growing and image entropy in its calculation. The algorithm starts by partitioning the image into several levels of intensity using watershed multi-degree immersion process. The levels of intensity are the input to a computationally efficient seed region segmentation process which produces the initial partitioning of the image regions. These regions are fed to entropy procedure to carry out a suitable merging which produces the final segmentation. The latter process uses a region-based similarity representation of the image regions to decide whether regions can be merged. The region is isolated from the level and the residual pixels are uploaded to the next level and so on, we recall this process as multi-level process and the watershed is called multi-level watershed. The proposed algorithm is applied to challenging applications: grey matter–white matter segmentation in magnetic resonance images (MRIs). The established methods and the proposed approach are experimented by these applications to a variety of simulating immersion, multi-degree, multi-level seed region growing and multi-level seed region growing with entropy. It is shown that the proposed method achieves more accurate results for medical image oversegmentation.     

MOLMOL: A program for display and analysis of macromolecular structures

MOLMOL is a molecular graphics program for display, analysis, and manipulation of three-dimensional structures of biological macromolecules, with special emphasis on nuclear magnetic resonance (NMR) solution structures of proteins and nucleic acids. MOLMOL has a graphical user interface with menus, dialog boxes, and on-line help. The display possibilities include conventional presentation, as well as novel schematic drawings, with the option of combining different presentations in one view of a molecule. Covalent molecular structures can be modified by addition or removal of individual atoms and bonds, and three-dimensional structures can be manipulated by interactive rotation about individual bonds. Special efforts were made to allow for appropriate display and analysis of the sets of typically 20–40 conformers that are conventionally used to represent the result of an NMR structure determination, using functions for superimposing sets of conformers, calculation of root mean square distance (RMSD) values, identification of hydrogen bonds, checking and displaying violations of NMR constraints, and identification and listing of short distances between pairs of hydrogen atoms.     

A novel approach for intracellular 3D immuno-labeling for electron tomography

Electron tomography (ET) is an indispensable high-resolution tool for three dimensional (3D) imaging in cell biology. When applied to immuno-labeled cells, ET can provide essential insights in both the cellular architecture and the dynamics. Current protocols for 3D immuno-labeling of intracellular antigens include permeabilization steps that cause random, extensive cell membrane disruption. This permeabilization results in a poor cell ultrastructure, limiting the usefulness of the specimens for high-resolution studies. Here we describe a novel method, based on a well-controlled permeabilization by targeted laser cell perforation, that allows for the 3D immuno-localization of cytoplasmic antigens in cultured cells. The approach is unique since it is applicable to both chemically and cryo-fixed cells and leads to a superior ultrastructural preservation for electron microscopy and tomography.     

A thresholding method for automatic cell image segmentation.

An algorithm for automatic segmentation of PAP-stained cell images and its digital implementation is described. First, the image is filtered in order to eliminate the granularily and small objects in the image which may upset the segmentation procedure. In a second step, information on gradient and compactness is extracted from the filtered image and stored in three histograms as functions of the extinction. From these histograms, two extinction thresholds are computed. These thresholds are suitable to separate the nucleus from the cytoplasm, and the cytoplasm from the background in the filtered image. Masks are determined in this way, and finally used to analyse the nucleus and the cytoplasm in the original image.     

Reconstruction of 3D structures from protein contact maps

The prediction of the protein tertiary structure from solely its residue sequence (the so called Protein Folding Problem) is one of the most challenging problems in Structural Bioinformatics. We focus on the protein residue contact map. When this map is assigned it is possible to reconstruct the 3D structure of the protein backbone. The general problem of recovering a set of 3D coordinates consistent with some given contact map is known as a unit-disk-graph realization problem and it has been recently proven to be NP-Hard. In this paper we describe a heuristic method (COMAR) that is able to reconstruct with an unprecedented rate (3-15 seconds) a 3D model that exactly matches the target contact map of a protein. Working with a non-redundant set of 1760 proteins, we find that the scoring efficiency of finding a 3D model very close to the protein native structure depends on the threshold value adopted to compute the protein residue contact map. Contact maps whose threshold values range from 10 to 18 Ångstroms allow reconstructing 3D models that are very similar to the proteins native structure.     

A data mining approach for analyzing density maps representing macromolecular structures

Results of electron microscopy-based three-dimensional reconstructions of macromolecules or their complexes are usually stored as density maps. Each point ("voxel") in the map represents a density value and one approach for studying details of the map is to display an isosurface enclosing areas of interest. We have taken a data mining approach not only focusing on the areas of immediate interest but determining all possible separate entities ("blobs") from a density map. After the entire density map is analyzed with our mining program BLOBBER, properties of all detected blobs can be browsed and sets of blobs can be visualized using our VIZBLOB program. Since BLOBBER analyzes density maps using only density information and relates it to spatial relationships, BLOBBER can be used to analyze symmetrical or asymmetrical density maps from any source. To test our program we have analyzed published bacteriophage PRD1 reconstructions. We identified various structural details ranging from individual proteins to major complexes such as the whole capsid shell and more elaborate details of possible connections between membrane interfaces. This approach can also be a useful preprocessing tool for visualizing reconstructions.