14 research outputs found

    Automatic post-picking improves particle image detection from Cryo-EM micrographs

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    Cryo-electron microscopy (cryo-EM) studies using single particle reconstruction is extensively used to reveal structural information of macromolecular complexes. Aiming at the highest achievable resolution, state of the art electron microscopes acquire thousands of high-quality images. Having collected these data, each single particle must be detected and windowed out. Several fully- or semi-automated approaches have been developed for the selection of particle images from digitized micrographs. However they still require laborious manual post processing, which will become the major bottleneck for next generation of electron microscopes. Instead of focusing on improvements in automated particle selection from micrographs, we propose a post-picking step for classifying small windowed images, which are output by common picking software. A supervised strategy for the classification of windowed micrograph images into particles and non-particles reduces the manual workload by orders of magnitude. The method builds on new powerful image features, and the proper training of an ensemble classifier. A few hundred training samples are enough to achieve a human-like classification performance.Comment: 14 pages, 5 figure

    A structural model of the active ribosome-bound membrane protein insertase YidC

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    A structural model of the active ribosome-bound membrane protein insertase YidC

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    Automatic approaches for microscopy imaging based on machine learning and spatial statistics

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    One of the most frequent ways to interact with the surrounding environment occurs as a visual way. Hence imaging is a very common way in order to gain information and learn from the environment. Particularly in the field of cellular biology, imaging is applied in order to get an insight into the minute world of cellular complexes. As a result, in recent years many researches have focused on developing new suitable image processing approaches which have facilitates the extraction of meaningful quantitative information from image data sets. In spite of recent progress, but due to the huge data set of acquired images and the demand for increasing precision, digital image processing and statistical analysis are gaining more and more importance in this field. There are still limitations in bioimaging techniques that are preventing sophisticated optical methods from reaching their full potential. For instance, in the 3D Electron Microscopy(3DEM) process nearly all acquired images require manual postprocessing to enhance the performance, which should be substitute by an automatic and reliable approach (dealt in Part I). Furthermore, the algorithms to localize individual fluorophores in 3D super-resolution microscopy data are still in their initial phase (discussed in Part II). In general, biologists currently lack automated and high throughput methods for quantitative global analysis of 3D gene structures. This thesis focuses mainly on microscopy imaging approaches based on Machine Learning, statistical analysis and image processing in order to cope and improve the task of quantitative analysis of huge image data. The main task consists of building a novel paradigm for microscopy imaging processes which is able to work in an automatic, accurate and reliable way. The specific contributions of this thesis can be summarized as follows: • Substitution of the time-consuming, subjective and laborious task of manual post-picking in Cryo-EM process by a fully automatic particle post-picking routine based on Machine Learning methods (Part I). • Quality enhancement of the 3D reconstruction image due to the high performance of automatically post-picking steps (Part I). • Developing a full automatic tool for detecting subcellular objects in multichannel 3D Fluorescence images (Part II). • Extension of known colocalization analysis by using spatial statistics in order to investigate the surrounding point distribution and enabling to analyze the colocalization in combination with statistical significance (Part II). All introduced approaches are implemented and provided as toolboxes which are free available for research purposes

    Autocryopicker: An unsupervised learning approach for fully automated single particle picking in cryo-em images

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    Background: An important task of macromolecular structure determination by cryo-electron microscopy (cryo-EM) is the identification of single particles in micrographs (particle picking). Due to the necessity of human involvement in the process, current particle picking techniques are time consuming and often result in many false positives and negatives. Adjusting the parameters to eliminate false positives often excludes true particles in certain orientations. The supervised machine learning (e.g. deep learning) methods for particle picking often need a large training dataset, which requires extensive manual annotation. Other reference-dependent methods rely on low-resolution templates for particle detection, matching and picking, and therefore, are not fully automated. These issues motivate us to develop a fully automated, unbiased framework for particle picking. Results: We design a fully automated, unsupervised approach for single particle picking in cryo-EM micrographs. Our approach consists of three stages: image preprocessing, particle clustering, and particle picking. The image preprocessing is based on multiple techniques including: image averaging, normalization, cryo-EM image contrast enhancement correction (CEC), histogram equalization, restoration, adaptive histogram equalization, guided image filtering, and morphological operations. Image preprocessing significantly improves the quality of original cryo-EM images. Our particle clustering method is based on an intensity distribution model which is much faster and more accurate than traditional K-means and Fuzzy C-Means (FCM) algorithms for single particle clustering. Our particle picking method, based on image cleaning and shape detection with a modified Circular Hough Transform algorithm, effectively detects the shape and the center of each particle and creates a bounding box encapsulating the particles. Conclusions: AutoCryoPicker can automatically and effectively recognize particle-like objects from noisy cryo-EM micrographs without the need of labeled training data or human intervention making it a useful tool for cryo-EM protein structure determination

    A structural model of the active ribosome-bound membrane protein insertase YidC

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    The integration of most membrane proteins into the cytoplasmic membrane of bacteria occurs co-translationally. The universally conserved YidC protein mediates this process either individually as a membrane protein insertase, or in concert with the SecY complex. Here, we present a structural model of YidC based on evolutionary co-variation analysis, lipid-versus-protein-exposure and molecular dynamics simulations. The model suggests a distinctive arrangement of the conserved five transmembrane domains and a helical hairpin between transmembrane segment 2 (TM2) and TM3 on the cytoplasmic membrane surface. The model was used for docking into a cryo-electron microscopy reconstruction of a translating YidC-ribosome complex carrying the YidC substrate F(O)c. This structure reveals how a single copy of YidC interacts with the ribosome at the ribosomal tunnel exit and identifies a site for membrane protein insertion at the YidC protein-lipid interface. Together, these data suggest a mechanism for the co-translational mode of YidC-mediated membrane protein insertion

    Structural analysis of membrane protein biogenesis and ribosome stalling by cryo-electron microscopy

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    To study the mechanisms of membrane protein insertion we established a protocol that allows isolation of in vivo assembled ribosome nascent chain complexes (RNCs) from E. coli in high yield and quality. To investigate the interaction of SecY with a translating ribosome, model membrane proteins of different length and topology were over-expressed and the respective RNCs were isolated under mild conditions to allow co-purification of the SecY complex. Analysis of the interaction of RNCs with SecY in vivo suggested that, as expected, a tight engagement of the ribosome and SecY is only established for nascent chains that are translocated co-translationally. We observed that SecY and the RNC do not form a stable complex at the moment of hydrophobic transmembrane segments inserting in the translocon. However, a stable engagement of the RNC with SecY was observed, when inserting a transmembrane segment with a type II topology into SecY followed by a hydrophilic loop of a certain length which allows the isolation of this complex. That suggested a dual binding mode of tight and loose coupling of SecY to the translating ribosome dependent on the nature of the nascent substrate. We present the first three dimensional structure of an in vivo assembled, tightly coupled polytopic RNC-SecYE complex at 7.2 Å solved by cryo-EM and single particle reconstruction. A molecular model based on the cryo-EM structure reveals that SecYE could be trapped in a post-insertion state, with the two substrate helices interacting with the periphery of SecY, while still translocating the hydrophilic loop. The lateral gate of SecY remains in a ‘pre-opened’ conformation during the translocation of the hydrophilic loop. The interaction sites of SecY with the ribosome were found as described. Remarkably, we could also reveal an interaction of helix 59 in the ribosome with nascent membrane protein via positively charged residues in the first cytoplasmic loop of the substrate. It is tempting to speculate that this interaction contributes to the positive inside rule. Though, we provided an unprecedented snapshot of an inserting polytopic membrane protein, the exact path of the nascent chain and the molecular mechanism of the actual insertion could not be solved so far. Expression of the E. coli tryptophanase (TnaA) operon is triggered by ribosome stalling during translation of the upstream TnaC leader peptide. Notably, this stalling is strictly dependent on the presence of tryptophan that acts in a hitherto unknown way. Here, we present a cryo-EM reconstruction of the stalled nascent TnaC leader peptide in the ribosomal exit tunnel. The structure of the TnaC-stalled ribosome was solved to an average resolution of 3.8 Å by cryo-EM and single particle analysis. It reveals the conformation of the silenced peptidyl-transferase center as well as the exact path of the stalled nascent peptide and its contacts in detail. Furthermore, we clearly resolve not a single but two free tryptophan molecules in the ribosomal exit tunnel. The nascent TnaC peptide chain together with distinct rRNA bases in the ribosomal exit tunnel creates two hydrophobic binding pockets for the tryptophan coordination. One tryptophan molecule is coordinated by V20 and I19 of TnaC and interacts with U2586 of the rRNA, the second tryptophan is bound between I19 and I15 in the area of A2058 and A2059 of the rRNA. Interestingly, the latter is also the binding platform for macrolide antibiotics. Engagement of L-Trp in these composite binding pockets leads to subtle conformational changes in residues of the ribosomal tunnel wall that are translated to the PTC eventually resulting in silencing by stabilizing the conformations of the conserved nucleotides A2602 and U2585. These conformations of the two nucleotides in the PTC are incompatible with the correct accommodation of the GGQ motive of release factor 2, thus inhibiting the peptide release
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