Abstract
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 language | English (US) |
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Pages (from-to) | 4656-4669 |
Number of pages | 14 |
Journal | Journal of Chemical Information and Modeling |
Volume | 61 |
Issue number | 9 |
DOIs | |
State | Published - Sep 27 2021 |
Keywords
- 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