A Panel of Multivalent Nanobodies Broadly Neutralizing Omicron Subvariants and Recombinant.

Liuhai Zheng,Huifang Wang,Xueyan Liu,Chengchao Xu,Mingxiong Tian,Guangwei Shi,Chongzhi Bai,Zhijie Li,Jigang Wang,Shuwen Liu

Journal Of Medical Virology（2024）SCI 4区

Department of Critical Medicine | Southeast Univ | NMPA Key Laboratory for Research and Evaluation of Drug Metabolism & Guangdong Provincial Key Laboratory of New Drug Screening | Shanxi Prov Hosp Tradit Chinese Med

Cited 1|Views22

Abstract

The emerging Omicron subvariants have a remarkable ability to spread and escape nearly all current monoclonal antibody (mAb) treatments. Although the virulence of SARS-CoV-2 has now diminished, it remains a significant threat to public health due to its high transmissibility and susceptibility to mutation. Therefore, it is urgent to develop broad-acting and potent therapeutics targeting current and emerging Omicron variants. Here, we identified a panel of Omicron BA.1 spike receptor-binding domain (RBD)-targeted nanobodies (Nbs) from a naive alpaca VHH library. This panel of Nbs exhibited high binding affinity to the spike RBD of wild-type, Alpha B.1.1.7, Beta B.1.351, Delta plus, Omicron BA.1, and BA.2. Through multivalent Nb construction, we obtained a subpanel of ultrapotent neutralizing Nbs against Omicron BA.1, BA.2, BF.7 and even emerging XBB.1.5, and XBB.1.16 pseudoviruses. Protein structure prediction and docking analysis showed that Nb trimer 2F2E5 targets two independent RBD epitopes, thus minimizing viral escape. Taken together, we obtained a panel of broad and ultrapotent neutralizing Nbs against Omicron BA.1, Omicron BA.2, BF.7, XBB.1.5, and XBB.1.16. These multivalent Nbs hold great promise for the treatment against SARS-CoV-2 infection and could possess a superwide neutralizing breadth against novel omicron mutants or recombinants.

Translated text

Key words

multivalent,neutralizing Nbs,Omicron,receptor-binding domain,SARS-CoV-2

求助PDF

上传PDF

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Upload PDF to Generate Summary

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper