1. Optimizing variant-specific therapeutic SARS-CoV-2 decoys using deep-learning-guided molecular dynamics simulations.
- Author
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Köchl, Katharina, Schopper, Tobias, Durmaz, Vedat, Parigger, Lena, Singh, Amit, Krassnigg, Andreas, Cespugli, Marco, Wu, Wei, Yang, Xiaoli, Zhang, Yanchong, Wang, Welson Wen-Shang, Selluski, Crystal, Zhao, Tiehan, Zhang, Xin, Bai, Caihong, Lin, Leon, Hu, Yuxiang, Xie, Zhiwei, Zhang, Zaihui, and Yan, Jun
- Subjects
MOLECULAR dynamics ,SARS-CoV-2 ,SARS-CoV-2 Omicron variant ,CONVOLUTIONAL neural networks ,COVID-19 treatment ,CHO cell - 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 (IC
50 ) compared with the same variant produced in CHO cells and an almost six-fold IC50 reduction compared with wild-type hACE2-Fc. [ABSTRACT FROM AUTHOR]- Published
- 2023
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