In Silico Design of miniACE2 Decoys with In Vitro Enhanced Neutralization Activity against SARS-CoV-2, Encompassing Omicron Subvariants

Abstract

The COVID-19 pandemic has overwhelmed healthcare systems and triggered global economic downturns. While vaccines have reduced the lethality rate of SARS-CoV-2 to 0.9% as of October 2024, the continuous evolution of variants remains a significant public health challenge. Next-generation medical therapies offer hope in addressing this threat, especially for immunocompromised individuals who experience prolonged infections and severe illnesses, contributing to viral evolution. These cases increase the risk of new variants emerging. This study explores miniACE2 decoys as a novel strategy to counteract SARS-CoV-2 variants. Using in silico design and molecular dynamics, blocking proteins (BPs) were developed with stronger binding affinity for the receptor-binding domain of multiple variants than naturally soluble human ACE2. The BPs were expressed in E. coli and tested in vitro, showing promising neutralizing effects. Notably, miniACE2 BP9 exhibited an average IC₅₀ of 4.9 µg/mL across several variants, including the Wuhan strain, Mu, Omicron BA.1, and BA.2 This low IC50 demonstrates the potent neutralizing ability of BP9, indicating its efficacy at low concentrations.Based on these findings, BP9 has emerged as a promising therapeutic candidate for combating SARS-CoV-2 and its evolving variants, thereby positioning it as a potential emergency biopharmaceutical.

Keywords: ACE2-decoys; SARS-CoV-2 variants evading monoclonal antibodies; basic science for translational medicine; in silico design of next-generation therapies for infectious diseases; miniACE2; next-generation treatments for SARS-CoV-2.

Figure 1

After 200 ns of MD simulation, the ΔG (A) and K_d (B) values were calculated using the PRODIGY web server [30] for the interaction of each BP with a broad range of RBDs from SARS-CoV-2 viral variants: (Wuhan, PDB: 6m0j [26]; Alpha B.1.1.7, PDB: 8DLK [31]; Beta, PDB: 7VX4 [32]; Delta, PDB: 7W9I [33]; Epsilon, PDB: 8DLV [31]; Gamma, PDB: 8DLQ [31]; Kappa, PDB: 7V86 [34]; Omicron BA.2, PDB: 7ZF7 [35]; Omicron BA.3, PDB: 7XB1 [36]; Omicron BA.1, PDB: 7U0N [37]; Omicron BA.1.1, PDB: 7XAZ [36]; Omicron XBB.1, PDB: 8IOV [38]; Omicron BA.2.75, PDB: 8ASY [39]; Omicron BQ.1.1, PDB: 8IF2 [40]; Omicron BA.4/5, PDB: 8AQS [41]).

Figure 2

In silico designs of BPs expressed in E. coli broadly neutralize SARS-CoV-2 variants through ELISA assays. (A) Neutralization efficacy of BP9 and BP11 ACE2 decoys against Wuhan (wild type), Mu, BA.1, and BA.2 variants compared to ACE2 protein, showing enhanced efficacy and potential for novel BP designs (n = 3 technical replicates shown). (B) IC₅₀ comparative panel between novel BPs and ACE2, suggesting their potential therapeutic use at low doses. (C) Statistical analyses (Tukey’s multiple comparison test performed using GraphPad Prism V10) were performed to calculate the statistical significance of the BPs (BP9 and BP11) compared with the parental hACE2. **** Represent the level of significance.

Figure 3

Stability of BP9 and BP11 at 37 °C and the Serum Matrix. Stability was evaluated by measuring neutralization activity using the cPass SARS-CoV-2 Neutralization Antibody Detection Kit for the BA.1 virus variant, with the ACE2 IC₅₀ value (92.92 µg/mL) as a standard protein concentration under the effects of temperature and serum matrix. (A): Proteins were incubated at room temperature and at 37 °C for 2 h to assess their stability. (C) Proteins were incubated at 37 °C for 2 h in the presence or absence of human serum samples, which were confirmed to lack neutralizing antibodies against SARS-CoV-2 (Supplementary Table S2). Each experiment was performed in three technical replicates. (B,D): The stability coefficient was calculated to identify significant differences in the stability of BP9 and BP11 compared to that of hACE2. The dotted line represents the neutralization cutoff as specified by the kit’s manufacturer. Data are presented as the mean ± standard deviation (SD). p-values were determined using one-way ANOVA (B,D).