Computationally Designed ACE2 Decoy Receptor Binds SARS-CoV-2 Spike (S) Protein with Tight Nanomolar Affinity

Brandon Havranek, Kui K. Chan, Austin Wu, Erik Procko, Shahidul M. Islam

Research output: Contribution to journalArticlepeer-review


Even with the availability of vaccines, therapeutic options for COVID-19 still remain highly desirable, especially in hospitalized patients with moderate or severe disease. Soluble ACE2 (sACE2) is a promising therapeutic candidate that neutralizes SARS CoV-2 infection by acting as a decoy. Using computational mutagenesis, we designed a number of sACE2 derivatives carrying three to four mutations. The top-predicted sACE2 decoy based on the in silico mutagenesis scan was subjected to molecular dynamics and free-energy calculations for further validation. After illuminating the mechanism of increased binding for our designed sACE2 derivative, the design was verified experimentally by flow cytometry and BLI-binding experiments. The computationally designed sACE2 decoy (ACE2-FFWF) bound the receptor-binding domain of SARS-CoV-2 tightly with low nanomolar affinity and ninefold affinity enhancement over the wild type. Furthermore, cell surface expression was slightly greater than wild-type ACE2, suggesting that the design is well-folded and stable. Having an arsenal of high-affinity sACE2 derivatives will help to buffer against the emergence of SARS CoV-2 variants. Here, we show that computational methods have become sufficiently accurate for the design of therapeutics for current and future viral pandemics.
Original languageEnglish (US)
Pages (from-to)4656-4669
Number of pages14
JournalJournal of Chemical Information and Modeling
Issue number9
StatePublished - Sep 27 2021


  • COVID-19
  • severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
  • Free energy
  • Receptors
  • Interaction energies
  • Genetics
  • Interfaces

ASJC Scopus subject areas

  • General Chemical Engineering
  • General Chemistry
  • Library and Information Sciences
  • Computer Science Applications


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