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NMR Based SARS-CoV-2 Antibody Screening.

ABSTRACT: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) entry into cells is a complex process that involves (1) recognition of the host entry receptor, angiotensin-converting enzyme 2 (ACE2), by the SARS-CoV-2 spike protein receptor binding domain (RBD), and (2) the subsequent fusion of the viral and cell membranes. Our long-term immune-defense is the production of antibodies (Abs) that recognize the SARS-CoV-2 RBD and successfully block viral infection. Thus, to understand immunity against SARS-CoV-2, a comprehensive molecular understanding of how human SARS-CoV-2 Abs recognize the RBD is needed. Here, we report the sequence-specific backbone assignment of the SARS-CoV-2 RBD and, furthermore, demonstrate that biomolecular NMR spectroscopy chemical shift perturbation (CSP) mapping successfully and rapidly identifies the molecular epitopes of RBD-specific mAbs. By incorporating NMR-based CSP mapping with other molecular techniques to define RBD-mAb interactions and then correlating these data with neutralization efficacy, structure-based approaches for developing improved vaccines and COVID-19 mAb-based therapies will be greatly accelerated.

SUBMITTER: Schoenle MV

PROVIDER: S-EPMC8171107 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Correlation between SARS-COV-2 antibody screening by immunoassay and neutralizing antibody testing.

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Uncovering a conserved vulnerability site in SARS-CoV-2 by a human antibody

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NMR Spectroscopy of the Main Protease of SARS-CoV-2 and Fragment-Based Screening Identify Three Protein Hotspots and an Antiviral Fragment.

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Voice-based screening for SARS-CoV-2 exposure in cardiovascular clinics.

Project description:AimsArtificial intelligence (A.I) driven voice-based assistants may facilitate data capture in clinical care and trials; however, the feasibility and accuracy of using such devices in a healthcare environment are unknown. We explored the feasibility of using the Amazon Alexa ('Alexa') A.I. voice-assistant to screen for risk factors or symptoms relating to SARS-CoV-2 exposure in quaternary care cardiovascular clinics.Methods and resultsWe enrolled participants to be screened for signs and symptoms of SARS-CoV-2 exposure by a healthcare provider and then subsequently by the Alexa. Our primary outcome was interrater reliability of Alexa to healthcare provider screening using Cohen's Kappa statistic. Participants rated the Alexa in a post-study survey (scale of 1 to 5 with 5 reflecting strongly agree). This study was approved by the McGill University Health Centre ethics board. We prospectively enrolled 215 participants. The mean age was 46 years [17.7 years standard deviation (SD)], 55% were female, and 31% were French speakers (others were English). In total, 645 screening questions were delivered by Alexa. The Alexa mis-identified one response. The simple and weighted Cohen's kappa statistic between Alexa and healthcare provider screening was 0.989 [95% confidence interval (CI) 0.982-0.997] and 0.992 (955 CI 0.985-0.999), respectively. The participants gave an overall mean rating of 4.4 (out of 5, 0.9 SD).ConclusionOur study demonstrates the feasibility of an A.I. driven multilingual voice-based assistant to collect data in the context of SARS-CoV-2 exposure screening. Future studies integrating such devices in cardiovascular healthcare delivery and clinical trials are warranted.Registrationhttps://clinicaltrials.gov/ct2/show/NCT04508972.

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