Cryo electron micrographs of Kaposi’s sarcoma-associated herpesvirus glycoprotein B in post-fusion conformation
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ABSTRACT:
SUBMITTER: Fumiaki Ito
PROVIDER: EMPIAR-12541 | biostudies-other |
REPOSITORIES: biostudies-other
Publications
Structure of the Kaposi's sarcoma-associated herpesvirus gB in post-fusion conformation.
Ito Fumiaki F Zhen James J Xie Guodong G Huang Haigen H Silva Juan C JC Wu Ting-Ting T-T Zhou Z Hong ZH
Journal of virology 20250117 2
Discovered in 1994 in lesions of an AIDS patient, Kaposi's sarcoma-associated herpesvirus (KSHV) is a member of the gammaherpesvirus subfamily of the <i>Herpesviridae</i> family, which contains a total of nine that infect humans. These viruses all contain a large envelope glycoprotein, glycoprotein B (gB), that is required for viral fusion with host cell membrane to initial infection. Although the atomic structures of five other human herpesviruses in their postfusion conformation and one in its ...[more]
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Project description:Discovered in 1994 in lesions of an AIDS patient, Kaposi's sarcoma-associated herpesvirus (KSHV) is a member of the gammaherpesvirus subfamily of the Herpesviridae family, which contains a total of nine that infect humans. These viruses all contain a large envelope glycoprotein, glycoprotein B (gB), that is required for viral fusion with host cell membrane to initial infection. Although the atomic structures of five other human herpesviruses in their postfusion conformation and one in its prefusion conformation are known, the atomic structure of KSHV gB has not been reported. Here, we report the first structure of the KSHV gB ectodomain determined by single-particle cryogenic electron microscopy (cryoEM). Despite a similar global fold between herpesvirus gB, KSHV gB possesses local differences not shared by its relatives in other herpesviruses. The glycosylation sites of gB are arranged in belts down the symmetry axis with distinct localization compared to that of other herpesviruses, which occludes certain antibody binding sites. An extended glycan chain observed in domain I (DI), located proximal to the host membrane, may suggest its possible role in host cell attachment. Local flexibility of domain IV (DIV) governed by molecular hinges at its interdomain junctions identifies a means for enabling conformational change. A mutation in the domain III (DIII) central helix disrupts incorporation of gB into KSHV virions despite adoption of a canonical fold in vitro. Taken together, this study reveals mechanisms of structural variability of herpesvirus fusion protein gB and informs its folding and immunogenicity.IMPORTANCEIn 1994, a cancer-causing virus was discovered in lesions of AIDS patients, which was later named Kaposi's sarcoma-associated herpesvirus (KSHV). As the latest discovered human herpesvirus, KSHV has been classified into the gammaherpesvirus subfamily of the Herpesviridae. In this study, we have expressed KSHV gB and employed cryogenic electron microscopy (cryoEM) to determine its first structure. Importantly, our structure resolves some glycans beyond the first sugar moiety. These glycans are arranged in a pattern unique to KSHV, which impacts the antigenicity of KSHV gB. Our structure also reveals conformational flexibility caused by molecular hinges between domains that provide clues into the mechanism behind the drastic change between prefusion and postfusion states.
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Project description:BackgroundIdentification and selection of protein particles in cryo-electron micrographs is an important step in single particle analysis. In this study, we developed a deep learning-based particle picking network to automatically detect particle centers from cryoEM micrographs. This is a challenging task due to the nature of cryoEM data, having low signal-to-noise ratios with variable particle sizes, shapes, distributions, grayscale variations as well as other undesirable artifacts.ResultsWe propose a double convolutional neural network (CNN) cascade for automated detection of particles in cryo-electron micrographs. This approach, entitled Deep Regression Picker Network or "DRPnet", is simple but very effective in recognizing different particle sizes, shapes, distributions and grayscale patterns corresponding to 2D views of 3D particles. Particles are detected by the first network, a fully convolutional regression network (FCRN), which maps the particle image to a continuous distance map that acts like a probability density function of particle centers. Particles identified by FCRN are further refined to reduce false particle detections by the second classification CNN. DRPnet's first CNN pretrained with only a single cryoEM dataset can be used to detect particles from different datasets without retraining. Compared to RELION template-based autopicking, DRPnet results in better particle picking performance with drastically reduced user interactions and processing time. DRPnet also outperforms the state-of-the-art particle picking networks in terms of the supervised detection evaluation metrics recall, precision, and F-measure. To further highlight quality of the picked particle sets, we compute and present additional performance metrics assessing the resulting 3D reconstructions such as number of 2D class averages, efficiency/angular coverage, Rosenthal-Henderson plots and local/global 3D reconstruction resolution.ConclusionDRPnet shows greatly improved time-savings to generate an initial particle dataset compared to manual picking, followed by template-based autopicking. Compared to other networks, DRPnet has equivalent or better performance. DRPnet excels on cryoEM datasets that have low contrast or clumped particles. Evaluating other performance metrics, DRPnet is useful for higher resolution 3D reconstructions with decreased particle numbers or unknown symmetry, detecting particles with better angular orientation coverage.
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Project description:Single-particle cryo-electron microscopy (cryo-EM) is a powerful imaging modality capable of visualizing proteins and macromolecular complexes at near-atomic resolution. The low electron-doses used to prevent radiation damage to the biological samples, however, result in images where the power of the noise is 100 times greater than the power of the signal. To overcome these low signal-to-noise ratios (SNRs), hundreds of thousands of particle projections are averaged to determine the three-dimensional structure of the molecule of interest. The sampling requirements of high-resolution imaging impose limitations on the pixel sizes that can be used for acquisition, limiting the size of the field of view and requiring data collection sessions of several days to accumulate sufficient numbers of particles. Meanwhile, recent image super-resolution (SR) techniques based on neural networks have shown state-of-the-art performance on natural images. Building on these advances, here, we present a multiple-image SR algorithm based on deep internal learning designed specifically to work under low-SNR conditions. Our approach leverages the internal image statistics of cryo-EM movies and does not require training on ground-truth data. When applied to single-particle datasets of apoferritin and T20S proteasome, we show that the resolution of the 3D structure obtained from SR micrographs can surpass the limits imposed by the imaging system. Our results indicate that the combination of low magnification imaging with in silico image SR has the potential to accelerate cryo-EM data collection by virtue of including more particles in each exposure and doing so without sacrificing resolution.
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