In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.

A new porcine coronavirus SADS-CoV was recently identified from suckling piglets 20 with severe diarrhea in southern China and its genome sequence is most identical (~95% 21 identity) to that of bat α-coronavirus HKU2. It again indicates bats are the natural 22 reservoir of many coronaviruses that have great potential for cross-species transmission 23 to animals and humans by recombination and/or mutation. Here we report the cryo-EM 24 structures of HKU2 and SADS-CoV spike glycoprotein trimers at 2.38 Å and 2.83 Å 25 resolution, respectively. HKU2 and SADS-CoV spikes exhibit very high structural 26 similarity, with subtle differences mainly distributed in the NTD and CTD of the S1 27 subunit responsible for cell attachment and receptor binding. We systematically 28 analyzed and compared the NTD, CTD, SD1 and SD2 domains of the S1 subunit and 29 the S2 subunit of HKU2 spike with those of α-, β-, γ-, and δ-coronavirus spikes. The 30 results show that the NTD and CTD of HKU2/SADS-CoV are probably the most 31 ancestral in the evolution of spike. Although the S2 subunit mediating membrane fusion 32 is highly conserved, the connecting region after fusion peptide in HKU2/SADS-CoV 33 S2 subunit also adopts a conformation distinct from other coronaviruses. These results 34 structurally demonstrate a close evolutionary relationship between HKU2 /SADS-CoV 35 and β-coronavirus spikes and provide new insights into the evolution and cross-species 36 transmission of coronaviruses. : bioRxiv preprint 39 Coronaviruses, categorized into the order Nidovirales family Coronaviridae and 40 subfamily Coronavirinae, are a large group of viral pathogens with a wide host range 1 . 41 Their infections in humans, other mammals and birds can cause respiratory, hepatic, 42 enteric and neurological diseases with varying severity 2 . The severe acute respiratory 43 syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus 44 (MERS-CoV) have posed severe threats to human health in the 21 st century 1,3 . In the 45 meantime, coronaviruses infecting domestic animals also bring substantial economic 46 losses 4 . For example, the swine acute diarrhea syndrome coronavirus (SADS-CoV) 47 (also known as SeACoV and PEAV) isolated in 2017 caused outbreaks of severe watery 48 diarrhea of suckling piglets with a mortality up to 90% in several commercial pig farms 49 in Guangdong Province of China 5-10 . SADS-CoV is an α-coronavirus and other 50 representative members in the α-genus are porcine epidemic diarrhea virus 51 (PEDV), porcine transmissible gastroenteritis coronavirus (TGEV), porcine respiratory 52 coronavirus (PRCV), human NL63 and 229E coronaviruses (HCoV-NL63 and HCoV-53 229E) 1 . Representative members in other three genera include mouse hepatitis 54 coronavirus (MHV), bovine coronavirus (BCoV), SARS-CoV, MERS-CoV, HCoV-55 OC43 and HCoV-HKU1 in the β-genus, avian infectious bronchitis virus (IBV) in the 56 γ-genus and porcine deltacoronavirus (PdCoV) in the δ-genus 1 . 57 Cross-species transmission promoted by genetic recombination and/or mutations 58 underlies the host range expansion of coronaviruses 3,11-13 . Bats are the natural reservoir 59 of more than 30 different α-and β-coronaviruses that have great potential for 60 interspecies transmission by recombination and/or mutation 12,14-16 . Data on genetic 61 evolution, receptor binding and pathogenesis have demonstrated that human SARS-62 CoV and MERS-CoV most likely originate from bats 1 . Palm civets and dromedary 63 camels are the intermediate hosts of SARS-CoV and MERS-CoV from bats to humans 1 , 64 respectively. The newly identified porcine SADS-CoV isolates are also found to share 65 ~95% sequence identity with Rhinolophus bat coronavirus HKU2, and this further 66 stressed the severe results of coronavirus spillover from bats to domestic animals 5-10 .
Cryo-EM structures of HKU2 and SADS-CoV spike 1 glycoproteins and insights into coronavirus evolution 2 3 4

Background: We present here the recent update of AMS algorithm for identification of post-translational modification (PTM) sites in proteins based only on sequence information, using artificial neural network (ANN) method. The query protein sequence is dissected into overlapping short sequence segments. Ten different physicochemical features describe each amino acid; therefore nine residues long segment is represented as a point in a 90 dimensional space. The database of sequence segments with confirmed by experiments post-translational modification sites are used for training a set of ANNs. The efficiency of the classification for each type of modification and the prediction power of the method is estimated here using recall (sensitivity), precision values, the area under receiver operating characteristic (ROC) curves and leave-one-out tests (LOOCV). The significant differences in the performance for differently optimized neural networks are observed, yet the AMS 3.0 tool integrates those heterogeneous classification schemes into the single consensus scheme, and it is able to boost the precision and recall values independent of a PTM type in comparison with the currently available state-of-the art methods. The standalone version of AMS 3.0 presents an efficient way to indentify post-translational modifications for whole proteomes. The training datasets, precompiled binaries for AMS 3.0 tool and the source code are available at Additional file 1 Supplementary materials for the publication. Supplementary.xls contains detailed results of training and testing of AMS 3.0 tool in MS Excel file format. Additional file 2 PTM predictor software with training data. PTM_Archive.tar.gz includes the predictor software binaries together with all trained ANNs in tar.gz compressed form.
AMS 3.0: prediction of post-translational modifications

Highlights d ITZ, an antifungal and anticancer agent, is a broad-spectrum enterovirus inhibitor d OSBP and ORP4 are identified as novel targets of ITZ d ITZ binds OSBP and inhibits OSBP-mediated lipid exchange at membrane contact sites d ITZ also inhibits hepatitis C virus replication SUMMARY Itraconazole (ITZ) is a well-known antifungal agent that also has anticancer activity. In this study, we identify ITZ as a broad-spectrum inhibitor of enteroviruses (e.g., poliovirus, coxsackievirus, enterovirus-71, rhinovirus). We demonstrate that ITZ inhibits viral RNA replication by targeting oxysterol-binding protein (OSBP) and OSBP-related protein 4 (ORP4). Consistently, OSW-1, a specific OSBP/ORP4 antagonist, also inhibits enterovirus replication. Knockdown of OSBP inhibits virus replication, whereas overexpression of OSBP or ORP4 counteracts the antiviral effects of ITZ and OSW-1. ITZ binds OSBP and inhibits its function, i.e., shuttling of cholesterol and phosphatidylinositol-4phosphate between membranes, thereby likely perturbing the virus-induced membrane alterations essential for viral replication organelle formation. ITZ also inhibits hepatitis C virus replication, which also relies on OSBP. Together, these data implicate OSBP/ORP4 as molecular targets of ITZ and point to an essential role of OSBP/ORP4-mediated lipid exchange in virus replication that can be targeted by antiviral drugs.

Itraconazole Inhibits Enterovirus Replication by Targeting the Oxysterol-Binding Protein