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R01NIH · NATIONAL INSTITUTE OF ALLERGY AND INFECTIOUS DISEASESNIH

Structure-guided and epitope-based design of potent and broadly neutralizing nanobodies for COVID-19 mucosal immunotherapy

Cavacini, Lisa A (Contact)·UNIV OF MASSACHUSETTS MED SCH WORCESTER, MA·2021–2027·ACTIVE
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INSTITUTION

UNIV OF MASSACHUSETTS MED SCH WORCESTER, MA

PRINCIPAL INVESTIGATOR

Cavacini, Lisa A (Contact)

FUNDING

$678K

YEAR

2021

MOONBASE SCORE

40/100

LOADING MOONBASE SCORE

Abstract

Project Summary The coronavirus disease 2019 (COVID-19) rapidly disseminated through the human population and became a global pandemic. Significant efforts have been put into developing vaccines or antibody therapies based on the spike glycoprotein of SARS-CoV-2. One challenge of such strategies is to identify conserved epitopes on the Spike and predict viral mutations that could diminish the effectiveness of the vaccine and immunotherapy. To date, over 20 variant of SARS- CoV-2 genome sequences have been reported. Therefore, structure guided and epitope based design are crucial to generate effective medicines for current and future outbreaks of SARS-CoV- 2 or related coronavirus. Nanobodies can recognize conserved epitopes on hypervariable pathogens. Here, we propose that anti-spike nanobodies can be utilized for rapid identification of protective epitopes to inform design of vaccine and therapeutics. Further, we hypothesize that potent and broadly protective nanobodies against SARS-CoV-2 can be developed as an inhaled prophylactic or therapeutic medicine. In this proposal, we will leverage our complementary strengths through a multi-disciplinary approach combining mucosal immunology, structural biology, and virology, to characterize the molecular interactions and differential specificities of a diverse panel of nanobodies against spike of SARS-CoV-2 and other members of the Betacoronaviruese family (Aim 1). A comprehensive list of conserved and non-conserved epitopes (Aim 2) will be used for structure-based design of potent nanobody multimers for in vivo characterization (Aim 3).

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