Exploring bioinformatic approaches to assess the binding affinity of COVID-19 therapeutic decoys

In a recent study published in scientific reportresearchers used molecular dynamics (MD) simulations and artificial neural networks (MD) simulations to evaluate the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike (S) protein receptor-binding domain (RBD). ANN) – human angiotensin-converting enzyme 2 (hACE2) binding affinity of SARS-CoV-2 mutants.

Background

Studies have reported that the S-hACE2 binding interaction facilitates SARS-CoV-2 entry and subsequent replication in the host. Therefore, coronavirus disease 2019 (COVID-19) may be prevented by S-ACE2 binding inhibition.

Therefore, human soluble ACE2 (hsACE2) that binds to SARS-CoV-2 virions before SARS-CoV-2 entry may prevent COVID-19. However, this approach requires optimization and adaptation to new SARS-CoV-2 variants.

About research

In the current study, researchers devised a workflow combining conventional methods and point cloud-based techniques to optimize SARS-CoV-2 variant-specific therapeutic decoy development.

MD simulations were performed to identify human angiotensin-converting enzyme 2 amino acid substitutions that enhance the S RBD-hACE2 interaction. For this, an ESF (Empirical Scoring Function), which is closely related to the LIE (Linear Interaction Energy) technique, was used. in vitro A SARS-CoV-2 neutralization assay was performed to demonstrate inhibition of the SARS-CoV-2 wild-type strain and beta-mutant transfer by hACE2 mutants bound to the fragment crystallizable (Fc) region of human immunoglobulin G1. evaluated (hACE2-Fc).

Some variants of hACE2-Fc are also Nicotiana benthamiana A plant investigating the possibility of mass production. Molecular dynamics run data were combined with hACE2 halo and S RBD halo for ANN (Artificial Neural Network) training. A model was used to estimate the binding affinity of SARS-CoV-2 S with hACE2 variants based on the S RBD and hACE2 halo. COVID-19 therapeutic strategies may be targeted for novel SARS-CoV with maximal binding affinity, as hACE2 variants may be rapidly screened by artificial neural networks and validated by MD simulations if new variants emerge. can be tailored based on human soluble ACE2 variants with -2 Strain.

The feasibility of the system to estimate the impact of S RBD variant mutations on the same hACE2 decoy was evaluated using the BA.1 and BA.2 subvariants of the SARS-CoV-2 Omicron variant as examples. All possible hACE2 mutations were screened and the 300 most promising estimates were validated by MD simulations. In addition to wild-type hACE2, a promising pf hACE2 mutant with a C-terminal human IgG Fc tag was expressed in Chinese Hamster Ovary (CHO) cells.

SARS-CoV-2 RNA was quantified by quantitative reverse transcription-polymerase chain reaction (RT-qPCR) and immunohistochemistry (IHC) analysis.SARS-CoV-2 neutralization potential of hACE2 variants expressed in Nicotiana benthamiana Plant leaves (hACE2-Fc K31W_NB) were tested using an enzyme-linked immunosorbent assay (ELISA). in silico Analysis was performed to assess the binding affinity of hACE2 mutants to Omicron BA.3, BA.4/5, and Omicron BA.2.75 RBD proteins.

The crystal structure of wild-type SARS-CoV-2 S RBD bound to hACE2 was downloaded from the Protein Data Bank (PDB) database. Model-estimated ΔG values ​​were calculated based on electrostatic and van der Waals forces. Sequences used for ANN training were S RBD sequences (n=1,165) obtained by visual inspection, literature search, or from the Global Initiative for the Sharing of All Influenza Data (GISAID) database by 4 January 2022 and hACE2 sequences (n=95).

result

hACE2-Fc K31W, hACE2 T27Y_L79T_N330Y_K31W, and hACE2 T27Y_L79T_K31W hACE2 variants were identified as high binding affinity candidates.Candidates created with N. benthamiana showed 5.0 times lower IC and 6.0 times lower IC₅₀ (Half maximal inhibitory concentration) values ​​compared to the same variants produced in CHO cells and wild-type hACE2-Fc, respectively.The findings suggest that hACE2-Fc variants with correct folding are N. benthamiana Also, plant-derived soluble ACE2 variants represent a promising and cost-effective therapeutic option against SARS-CoV-2.

ESF estimate verified in vitro By virus neutralization assay.Experimental data correlated well with the estimated ΔG_Before (Gibbs free energy) model. Compared to wild-type hACE2, the majority of hACE2 mutants are enhanced against SARS-CoV-2 beta mutants, delta mutants, and Omicron BA.1 and BA.2 submutants showed the same binding affinity. hACE2-K31W is the only mutant with a very low Gibbs free energy, indicating that the K31W mutation may contribute to S RBD interactions. The presence of the K31W mutation was observed in most of the high binding affinity mutants.

Variants with mutations from 3.0 to 5.0 showed the greatest S RBD binding. hACE2 T27Y_L79T_K31W and hACE2 T27Y_L79T_N330Y_K31W showed very high binding affinities for BA.2 S RBD (ΔG_Before value -71.0 kJ/mol) and wild-type hACE2 (-52.0 kJ/mol). With estimated binding affinities of -62.0 and -67.0 kJ/mol, the hACE2 T27Y_L79T_K31W and hACE2 T27Y_L79T_N330Y_K31W variants are the top high-affinity variants of BA.3 and bind Omicron BA.4/5 and Omicron BA.2.75 Affinity is low. The highest outliers (MD ΔG values ​​of <−70 kJ/mol) were mapped by the model to the highest observed binding affinity values.

Our findings indicate that not only can ANN better estimate values ​​close to the majority of the binding affinity distribution than extrapolating from closely related variants, but the high-affinity variant has a peak of -68.0 kJ/mol. showed that it reliably mapped to the affinity bracket of . Artificial neural networks have learned meaningful physical insights from Halos and performed much better than simply learning regression to the mean or copy functions, and the model has shown relatively different inputs (distant SARS-CoV -2 sequences) can be combined. ). The model identified a single mutant comparable to the best hACE2 mutant found in the first MD run.

Overall, the study results highlighted a bioinformatics approach that combines MD simulations. in vitro Competitive inhibition assays, live virus infection assays, and ANNs to quickly, cost-effectively and efficiently assess the binding affinity of hACE2 decoys to novel SARS-CoV-2 strains in early stages.Sample requirements in vitro Selection.