Optimizing variant-specific therapeutic SARS-CoV-2 decoys using deep-learning-guided molecular dynamics simulations.
Sci Rep
; 13(1): 774, 2023 01 14.
Article
in English
| MEDLINE | ID: covidwho-2186078
ABSTRACT
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 (IC50) compared with the same variant produced in CHO cells and an almost six-fold IC50 reduction compared with wild-type hACE2-Fc.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Deep Learning
/
COVID-19
Type of study:
Experimental Studies
/
Prognostic study
Topics:
Variants
Limits:
Animals
Language:
English
Journal:
Sci Rep
Year:
2023
Document Type:
Article
Affiliation country:
S41598-023-27636-x
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