The rapid spread of the highly-pathogenic, novel SARS-coronavirus 2 (SARS-CoV-2) has caused a global health 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 its soluble form, ACE2 (sACE2) has the potential to be used as a stable and non-immunogenic competitive inhibitor to SARS-CoV-2 and is presently being explored in clinical trials3. Due to the potential negative side effects of anti-spike mAbs18, and the fact that ACE2 exhibits other biological roles4?6 including integrin signaling regulation7,8, spike-specific receptor mimics would yield novel therapeutics for SARS-CoV-2 and potentially other highly infectious diseases. This proposal seeks to use machine learning and directed evolution to develop high affinity, yet endogenously-inactive mimics of sACE2 in order to create rapidly implementable therapeutics to combat SARS-CoV-2 and potential corona-like viruses. This approach would allow for the generation of scalable and translatable biologics, and provide a platform to rapidly course-correct for potential mutations that may arise in the future. Utilizing deep-learning with UniRep49, will design and generate sACE2 variants that tightly bind the 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 that bind endogenous proteins [Aim 2]. Finally, we will identify lead candidates and evaluate the tolerance and immunogenicity of engineered sACE2 variants in mice [Aim 3]. Collectively, this proposal will develop highly-specific ACE2 receptor mimics in order to create novel antivirals with minimal immunogenicity in time to save lives and prevent future outbreaks. 10
There is desperate need for novel, effective, and scalable antiviral medicines to combat the COVID-19 pandemic. The SARS-CoV-2 virus enters human cells by binding to the ACE2 receptor, and a soluble version of that receptor is being explored in clinical trials as a potential ?competitive inhibitor?: the virus will bind to the decoy instead of the real receptor. The goal of this project is to combine computational and laboratory evolution to create superior receptor decoys that more tightly bind to the virus but don't affect any other functions of the human body, enabling them to be safely delivered at high levels with minimal side effects.
