EVOLVING VIRUS-SPECIFIC sACE2 MIMICS FOR COMPETITIVE INHIBITION OF SARS-CoV-2

PROJECT Summary: The rapid spread of the highly-pathogenic, novel SARS-coronavirus 2 (SARS-CoV-2) has caused a globalhealth emergency. Thus, there is a desperate need for effective antiviral therapeutics to counteract this virus.The SARS-CoV-2 virus enters cells using the ACE2 receptor1 which binds the viral spike protein2. In itssoluble form, ACE2 (sACE2) has the potential to be used as a stable and non-immunogenic competitiveinhibitor to SARS-CoV-2 and is presently being explored in clinical trials3. Due to the potential negative sideeffects of anti-spike mAbs18, and the fact that ACE2 exhibits other biological roles4-6 including integrinsignaling regulation7,8, spike-specific receptor mimics would yield novel therapeutics for SARS-CoV-2 andpotentially other highly infectious diseases.This proposal seeks to use machine learning and directed evolution to develop high affinity, yetendogenously-inactive mimics of sACE2 in order to create rapidly implementable therapeutics to combatSARS-CoV-2 and potential corona-like viruses. This approach would allow for the generation of scalable andtranslatable biologics, and provide a platform to rapidly course-correct for potential mutations that may arisein the future. Utilizing deep-learning with UniRep49, will design and generate sACE2 variants that tightly bindthe SARS-CoV2-2 spike protein but do not cross-interact with endogenous targets such as integrins [Aim 1].Simultaneously, we will perform directed evolution to optimize spike-binding and select against variants thatbind endogenous proteins [Aim 2]. Finally, we will identify lead candidates and evaluate the tolerance andimmunogenicity of engineered sACE2 variants in mice [Aim 3]. Collectively, this proposal will develophighly-specific ACE2 receptor mimics in order to create novel antivirals with minimal immunogenicity in timeto save lives and prevent future outbreaks.10