DOI: 10.5061/dryad.ncjsxkt22
title: Data from: Cryoelectron tomography of enveloped human cytomegalovirus particles

description: The compartmentalization of eukaryotic cells presents considerable
 challenges to the herpesvirus life cycle. The herpesvirus tegument, a
 bulky proteinaceous aggregate sandwiched between herpesviruses’ capsid and
 envelope, is uniquely evolved to address these challenges, yet tegument
 structure and organization remain poorly characterized. Here we use
 deep-learning–enhanced cryoEM to investigate the tegument of human
 cytomegalovirus virions and noninfectious enveloped particles (NIEPs; a
 genome packaging-aborted state), revealing a portal-biased tegumentation
 scheme. We resolve atomic structures of portal vertex-associated tegument
 (PVAT) and identify multiple configurations of PVAT arising from layered
 reorganization of pUL77, pUL48 (large tegument protein), and pUL47 (inner
 tegument protein) assemblies. Analyses show pUL77 seals the last-packaged
 viral genome end through electrostatic interactions, pUL77 and pUL48
 harbor a head–linker–capsid-binding motif conducive to PVAT
 reconfiguration, and pUL47/48 dimers form 45-nm–long filaments extending
 from the portal vertex. These results provide a structural framework for
 understanding how herpesvirus tegument facilitates and evolves during
 processes spanning viral genome packaging to delivery.
description: HCMV strain AD169 particles were mixed with 5-nm gold beads and
 applied onto freshly glow-discharged Quantifoil Holey Carbon grids, then
 plunge frozen into liquid ethane. Tilt series were acquired using the
 <em>SerialEM </em>software suite on a ThermoFisher Titan Krios
 electron microscope operated at 300 kV and equipped with a Gatan imaging
 filter, Gatan K2 Summit direct electron detector, and Volta phase plate
 (VPP). Prior to collecting each tilt series, we advanced and
 pre-conditioned the VPP by electron illumination at a dose of 12 nC for
 60s to ensure a phase shift of ~0.3π. Movies were recorded with the K2
 Summit camera operated in counting mode at 5 frames/s with a dose rate of
 ~8-10 e-/pixel/s. Tilt series were collected from -66° to +60° at 2°
 intervals at 53,000x nominal magnification (for a calibrated pixel size of
 2.6 Å) with a cumulative electron dose of approximately 100 e-/Å2. Defocus
 was targeted to -0.6 μm. Collected movies of raw tilt
 series were aligned and averaged using <em>MotionCor2</em> to
 generate a single micrograph for each tilt angle. Tilt series were aligned
 by tracking gold fiducial beads, and tomograms were reconstructed using
 the <em>IMOD 4.11 </em>software package implementing the
 simultaneous iterative reconstruction technique (SIRT) method. We next
 trained <em>IsoNet</em> on our reconstructed tomograms. Full
 usage details of <em>IsoNet</em> and a complete description of
 its workflow have previously been elaborated (Liu et al., <em>Nat
 Commun</em> 2022). Briefly, we generated tomogram STAR files using
 the <em>IsoNet</em> function <em>isonet.py
 prepare_star</em>. Reconstructed tomograms were then downscaled
 using <em>isonet.py rescale</em>, resulting in 6x-binned
 tomograms with a pixel size of 15.6 Å/pixel. Because tomograms were
 acquired using a VPP, we did not perform CTF deconvolution. A
 tomogram-wide mask was created using <em>isonet.py
 make_mask</em>, which automatically targets and identifies
 feature-rich regions of the tomogram for subsequent subtomogram extraction
 and neural-network analysis. 400 subtomograms (96x96x96 pixels) centered
 on feature-containing masked regions were then randomly extracted from the
 11 tomograms using <em>isonet.py extract</em>. Using
 <em>isonet.py refine</em>, we fed these subtomograms as raw
 inputs to generate the initial neural network for dataset-optimized
 missing-wedge correction and denoising. By default, 30 iterations of
 neural network and data refinement occur, with each successive iteration
 implementing both the previous iteration’s network and data output as new
 inputs. Denoising—in short, artificially introducing noise into our
 dataset to promote neural network discernment of noise within signal
 data—was gradually increased from none in the first 10 iterations to 0.2
 in the final 5 iterations. The final trained neural network, specifically
 optimized on our entire set of tomograms, was then applied to binned
 individual whole tomograms using <em>isonet.py predict</em> to
 obtain final denoised and missing-wedge-corrected tomograms.
description: # Cryoelectron tomography of enveloped human cytomegalovirus particles
 Human herpesviruses
 possess a proteinaceous tegument important for survival in the complicated
 intracellular environment. Despite tegument's many functions, its
 structure remains poorly characterized. In this study, we use a
 combination of deep learning-enhanced cryoelectron tomography (cryoET) and
 single-particle cryoelectron microscopy (cryoEM) to investigate human
 cytomegalovirus (HCMV; a betaherpesvirus) tegument structure. The dataset
 here contains raw data images of enveloped HCMV particles from our cryoET
 experiments, which we reconstructed into tomograms and then processed
 using *IsoNet*, our deep learning-based missing-wedge correction
 algorithm. Final *IsoNet*-processed tomograms are included with each image
 stack. ## Description of the data and file structure Each unique file
 string prefix (e.g., HCMV_cryoET_pp###) corresponds to one of eleven
 tomograms. Each individual tomogram has 66 associated files, including: 1.
 Raw image stack (64 files per tomogram), identified by
 HCMV_cryoET_pp*tomogram#_tilt#_tiltAngle*_*timestamp*.mrc. Each .mrc
 consists of the stack of raw frames imaged at that tilt angle. 2.
 *SerialEM* .mdoc file (1 file per tomogram), identified by
 HCMV_cryoET_pp*tomogram#_*full-ts_001-064.mrc. The .mdoc file contains
 important tilt series information obtained during data collection. 3.
 *IsoNet*-processed tomogram (1 file per tomogram), identified by
 HCMV_cryoET_pp*tomogram#_*ISONET_scaled.mrc. These are final corrected,
 rescaled tomograms. Please see our publication for additional details on
 data processing. ## Code/Software Software used in the generation of this
 dataset include *SerialEM*, *MotionCor2*, *IMOD 4.11*, *IsoNet*, and
 *ChimeraX 1.6*.