SARS-CoV-2 Delta Variant Decreases Nanobody Binding and ACE2 Blocking Effectivity

Mert Golcuk, Aysima Hacisuleyman, Sema Zeynep Yilmaz, Elhan Taka, Ahmet Yildiz, Mert Gur*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

The Delta variant spreads more rapidly than previous variants of SARS-CoV-2. This variant comprises several mutations on the receptor-binding domain (RBDDelta) of its spike glycoprotein, which binds to the peptidase domain (PD) of angiotensin-converting enzyme 2 (ACE2) receptors in host cells. The RBD-PD interaction has been targeted by antibodies and nanobodies to prevent viral infection, but their effectiveness against the Delta variant remains unclear. Here, we investigated RBDDelta-PD interactions in the presence and absence of nanobodies H11-H4, H11-D4, and Ty1 by performing 21.8 μs of all-atom molecular dynamics simulations. Unbiased simulations revealed that Delta variant mutations strengthen RBD binding to ACE2 by increasing the hydrophobic interactions and salt bridge formation, but weaken interactions with H11-H4, H11-D4, and Ty1. Among these nanobodies H11-H4 and H11-D4 bind RBD without overlapping ACE2. They were unable to dislocate ACE2 from RBDDelta when bound side by side with ACE2 on RBD. Steered molecular dynamics simulations at comparable loading rates to high-speed atomic force microscopy (AFM) experiments estimated lower rupture forces of the nanobodies from RBDDelta compared to ACE2. Our results suggest that existing nanobodies are less effective to inhibit RBDDelta-PD interactions and a new generation of nanobodies is needed to neutralize the Delta variant.

Original languageEnglish
JournalJournal of Chemical Information and Modeling
DOIs
Publication statusAccepted/In press - 2021

Bibliographical note

Publisher Copyright:
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Funding

This work used resources services and support provided via the COVID-19 HPC Consortium ( https://covid19-hpc-consortium.org/ ) and the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1548562.

FundersFunder number
National Science FoundationACI-1548562

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