Advances in microscopy instruments and image processing algorithms have led to an increasing number of cryo-electron microscopy (cryo-EM) maps. However, building accurate models into intermediate-resolution EM maps remains challenging and labor-intensive. Here, we propose an automatic model building method of multi-chain protein complexes from intermediate-resolution cryo-EM maps, named EMBuild, by integrating AlphaFold structure prediction, FFT-based global fitting, domain-based semi-flexible refinement, and graph-based iterative assembling on the main-chain probability map predicted by a deep convolutional network. EMBuild is extensively evaluated on diverse test sets of 47 single-particle EM maps at 4.0–8.0 Å resolution and 16 subtomogram averaging maps of cryo-ET data at 3.7–9.3 Å resolution, and compared with state-of-the-art approaches. We demonstrate that EMBuild is able to build high-quality complex structures that are comparably accurate to the manually built PDB structures from the cryo-EM maps. These results demonstrate the accuracy and reliability of EMBuild in automatic model building.

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Entry of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) into host cells depends on refolding of the virus-encoded spike protein from a prefusion conformation, metastable after cleavage, to a lower energy, stable postfusion conformation. This transition overcomes kinetic barriers for fusion of viral and target cell membranes. We report here a cryo-EM structure of the intact postfusion spike in a lipid bilayer that represents single-membrane product of the fusion reaction. The structure provides structural definition of the functionally critical membrane-interacting segments, including the fusion peptide and transmembrane anchor. The internal fusion peptide forms a hairpin-like wedge that spans almost the entire lipid bilayer and the transmembrane segment wraps around the fusion peptide at the last stage of membrane fusion. These results advance our understanding of the spike protein in a membrane environment and may guide development of intervention strategies.

Aims and Objective: This study aimed to unveil the specific function of lncRNA BBOX1 antisense RNA 1 (BBOX1-AS1) in ESCC cells and the underlying regulatory mechanism. Background: Esophageal squamous cell carcinoma (ESCC) is a deadly disease. Molecular mechanisms essential to ESCC development and progression require in-depth investigation. Long non-coding RNAs (lncRNAs) have been suggested as crucial effectors in modulating tumor growth. Methods: RT-qPCR and western blot examined the expression of genes and proteins of concern, respectively. Colony formation and EdU assays assessed the changes in cell proliferation. Sphere formation assay also detected the stemness of ESCC cells. Bioinformatics prediction, along with mechanistic assays (FISH, Subcellular fractionation, RNA pull-down, RIP, and luciferase reporter), was conducted to explore the gene interactions and regulatory relationship. Results: BBOX1-AS1 was observed to be aberrantly up-regulated in ESCC tissues and cell lines. BBOX1-AS1 depletion exerted suppressive impacts on ESCC cell proliferation and stemness, while BBOX1-AS1 overexpression led to the opposite consequences. Moreover, BBOX1-AS1 was verified to activate Hedgehog signaling pathway via up-regulating PTCH1, and BBOX1-AS1 could sponge miR-506-5p to up-regulate EIF5A, thus stabilizing PTCH1 mRNA. Rescue experiments indicated that BBOX1-AS1 could affect ESCC cell proliferation and stemness via modulation on PTCH1. Conclusion: To conclude, BBOX1-AS1 activates Hedgehog signaling pathway to facilitate the proliferation and stemness of ESCC cells via miR-506-5p/EIF5A/PTCH1 axis.