Highlights d Cryo-EM reveals the molecular features of the mammalian SRP targeting complexes d The fold of the eukaryotic SRP54 M domain and signal sequence binding is revealed d SRP$SR complex reveals eukaryotic-specific SRP contacts at the distal site d Regulation of the SRP$SR GTPase occurs through conserved protein and RNA contacts
SecA, an ATPase known to posttranslationally translocate secretory proteins across the bacterial plasma membrane, also binds ribosomes, but the role of SecA’s ribosome interaction has been unclear. Here, we used a combination of ribosome profiling methods to investigate the cotranslational actions of SecA. Our data reveal the widespread accumulation of large periplasmic loops of inner membrane proteins in the cytoplasm during their cotranslational translocation, which are specifically recognized and resolved by SecA in coordination with the proton motive force (PMF). Furthermore, SecA associates with 25% of secretory proteins with highly hydrophobic signal sequences at an early stage of translation and mediates their cotranslational transport. In contrast, the chaperone trigger factor (TF) delays SecA engagement on secretory proteins with weakly hydrophobic signal sequences, thus enforcing a posttranslational mode of their translocation. Our results elucidate the principles of SecA-driven cotranslational protein translocation and reveal a hierarchical network of protein export pathways in bacteria.
“Social sensors” refer to those who provide opinions through electronic communication channels such as social networks. There are two major issues in current models of sentiment analysis in social sensor networks. First, most existing models only analyzed the sentiment within the text but did not analyze the users, which led to the experimental results difficult to explain. Second, few studies extract the specific opinions of users. Only analyzing the emotional tendencies or aspect-level emotions of social users brings difficulties to the analysis of the opinion evolution in public emergencies. To resolve these issues, we propose an explainable sentiment prediction model based on the portraits of users sharing representative opinions in social sensors. Our model extracts the specific opinions of the user groups on the topics and fully considers the impacts of their diverse features on sentiment analysis. We conduct experiments on 51,853 tweets about the “COVID-19” collected from 1 May 2020 to 9 July 2020. We build users’ portraits from three aspects: attribute features, interest features, and emotional features. Six machine learning algorithms are used to predict emotional tendency based on users’ portraits. We analyze the influence of users’ features on the sentiment. The prediction accuracy of our model is 64.88%.
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