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biorxiv; 2021.
Preprint Dans Anglais | bioRxiv | ID: ppzbmed-10.1101.2021.12.02.470852


SARS-CoV2 spike glycoprotein is prime target for vaccines and for diagnostics and therapeutic antibodies against the virus. While anchored in the viral envelope, for effective virulence, the spike needs to maintain structural flexibility to recognize the host cell surface receptors and bind to them, a property that can heavily hinge upon the dynamics of the unresolved domains, most prominently the stalk. Construction of the complete, membrane-bound spike model and the description of its dynamics remain critical steps in understanding the inner working of this key element in viral infection. Using a hybrid approach, combining homology modeling, protein-protein docking and MD simulations, guided by biochemical and glycomics data, we have developed a full-length, membrane-bound, palmitoylated and fully-glycosylated spike structure in a native membrane. Multi-microsecond MD simulations of this model, the longest known trajectory of the full-spike, reveals conformational dynamics employed by the protein to explore the crowded surface of the host cell. In agreement with cryoEM, three flexible hinges in stalk allow for global conformational heterogeneity of spike in the fully-glycosyslated system mediated by glycan-glycan and glycan-lipid interactions. Dynamical range of spike is considerably reduced in its non-glycosylated form, confining the area explored by the spike on the host cell surface. Furthermore, palmitoylation of the membrane domain amplify the local curvature that may prime the fusion. We show that the identified hinge regions are highly conserved in SARS coronaviruses, highlighting their functional importance in enhancing viral infection, and thereby provide novel points for discovery of alternative therapeutics against the virus.

medrxiv; 2021.
Preprint Dans Anglais | medRxiv | ID: ppzbmed-10.1101.2021.02.17.21251758


AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSImportanceC_ST_ABSA predictive model to automatically identify the earliest determinants of both hospital discharge and mortality in hospitalized COVID-19 patients could be of great assistance to caregivers if the predictive information is generated and made available in the immediate hours following admission. ObjectiveTo identify the most important predictors of hospital discharge and mortality from measurements at admission for hospitalized COVID-19 patients. DesignObservational cohort study. SettingElectronic records from hospitalized patients. ParticipantsPatients admitted between March 3rd and August 24th with COVID-19 in Johns Hopkins Health System hospitals. Exposures216 phenotypic variables collected within 48 hours of admission. Main OutcomesWe used age-stratified (<60 and >=60 years) random survival forests with competing risks to identify the most important predictors of death and discharge. Fine-Gray competing risk regression (FGR) models were then constructed based on the most important RSF-derived covariates. ResultsOf 2212 patients, 1913 were discharged (age 57{+/-}19, time-to-discharge 9{+/-}11 days) while 279 died (age 75{+/-}14, time to death 14{+/-}15 days). Patients >= 60 years were nearly 10 times as likely to die within 60 days of admission as those <60. As the pandemic evolved, the rate of hospital discharge increased in both older and younger patients. Incident death and hospital discharge were accurately predicted by measures of respiratory distress, inflammation, infection, renal function, red cell turn over and cardiac stress. FGR models for each of hospital discharge and mortality as outcomes based on these variables performed well in the older (AUC 0.80-0.85 at 60-days) and younger populations (AUC >0.90 at 60-days). Conclusions and RelevanceWe identified markers collected within 2 days of admission that predict hospital discharge and mortality in COVID-19 patients and provide prediction models that may be used to guide patient care. Our proposed model suggests that hospital discharge and mortality can be forecasted with high accuracy based on 8-10 variables at this stage of the COVID-19 pandemic. Our findings also point to several specific pathways that could be the focus of future investigations directed at reducing mortality and expediting hospital discharge among COVID-19 patients. Probability of hospital discharge increased over the course of the pandemic. KO_SCPLOWEYC_SCPLOW PO_SCPLOWOINTSC_SCPLOWO_ST_ABSQuestionC_ST_ABSCan we predict the likelihood of hospital discharge as well as mortality from data obtained in the first 48 hours from admission in hospitalized COVID-19 patients? FindingsModels based on extensive phenotyping mined directly from electronic medical records followed by variable selection, accounted for the competing events of hospital death versus discharge, predicted both death and discharge with area under the receiver operating characteristic curves of >0.80. MeaningHospital discharge and mortality can be forecasted with high accuracy based on just 8-10 variables, and the probability of hospital discharge increased over the course of the pandemic.

biorxiv; 2020.
Preprint Dans Anglais | bioRxiv | ID: ppzbmed-10.1101.2020.10.27.357350


Infection of human cells by the SARS-CoV2 relies on its binding to a specific receptor and subsequent fusion of the viral and host cell membranes. The fusion peptide (FP), a short peptide segment in the spike protein, plays a central role in the initial penetration of the virus into the host cell membrane, followed by the fusion of the two membranes. Here, we use an array of molecular dynamics (MD) simulations taking advantage of the Highly Mobile Membrane Mimetic (HMMM) model, to investigate the interaction of the SARS-CoV2 FP with a lipid bilayer representing mammalian cellular membranes at an atomic level, and to characterize the membrane-bound form of the peptide. Six independent systems were generated by changing the initial positioning and orientation of the FP with respect to the membrane, and each system was simulated in five independent replicas. In 60% of the simulations, the FP reaches a stable, membrane-bound configuration where the peptide deeply penetrated into the membrane. Clustering of the results reveals two major membrane binding modes, the helix-binding mode and the loop-binding mode. Taken into account the sequence conservation among the viral FPs and the results of mutagenesis studies establishing the role of specific residues in the helical portion of the FP in membrane association, we propose that the helix-binding mode represents more closely the biologically relevant form. In the helix-binding mode, the helix is stabilized in an oblique angle with respect to the membrane with its N-terminus tilted towards the membrane core. Analysis of the FP-lipid interactions shows the involvement of specific residues of the helix in membrane binding previously described as the fusion active core residues. Taken together, the results shed light on a key step involved in SARS-CoV2 infection with potential implications in designing novel inhibitors.

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