Treatment of COVID-19 with a soluble version of ACE2 that binds to SARS-CoV-2 virions before they enter host cells is a promising approach, however it needs to be optimized and adapted to emerging viral variants. The computational workflow presented here consists of molecular dynamics simulations for spike RBD-hACE2 binding affinity assessments of multiple spike RBD/hACE2 variants and a novel convolutional neural network architecture working on pairs of voxelized force-fields for efficient search-space reduction. We identified hACE2-Fc K31W and multi-mutation variants as high-affinity candidates, which we validated in vitro with virus neutralization assays. We evaluated binding affinities of these ACE2 variants with the RBDs of Omicron BA.3, Omicron BA.4/BA.5, and Omicron BA.2.75 in silico. In addition, candidates produced in Nicotiana benthamiana, an expression organism for potential large-scale production, showed a 4.6-fold reduction in half-maximal inhibitory concentration (IC) compared with the same variant produced in CHO cells and an almost six-fold IC reduction compared with wild-type hACE2-Fc.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9840421PMC
http://dx.doi.org/10.1038/s41598-023-27636-xDOI Listing

Publication Analysis

Top Keywords

molecular dynamics
8
dynamics simulations
8
optimizing variant-specific
4
variant-specific therapeutic
4
therapeutic sars-cov-2
4
sars-cov-2 decoys
4
decoys deep-learning-guided
4
deep-learning-guided molecular
4
simulations treatment
4
treatment covid-19
4

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!