tetano
Editor, Senior Moderator
Structure
. 2025 Jan 21:S0969-2126(25)00002-4.
doi: 10.1016/j.str.2025.01.002. Online ahead of print. Computational design and improvement of a broad influenza virus HA stem targeting antibody
Huarui Duan[SUP] 1 [/SUP], Xiaojing Chi[SUP] 2 [/SUP], Xuehua Yang[SUP] 1 [/SUP], Shengnan Pan[SUP] 1 [/SUP], Xiuying Liu[SUP] 1 [/SUP], Peixiang Gao[SUP] 1 [/SUP], Fangyuan Zhang[SUP] 1 [/SUP], Xinhui Zhang[SUP] 1 [/SUP], Xuemeng Dong[SUP] 1 [/SUP], Yi Liao[SUP] 1 [/SUP], Wei Yang[SUP] 3 [/SUP]
Affiliations
Broadly neutralizing antibodies (nAbs) are vital therapeutic tools to counteract both pandemic and seasonal influenza threats. Traditional strategies for optimizing nAbs generally rely on labor-intensive, high-throughput mutagenesis screens. Here, we present an innovative structure-based design framework for the optimization of nAbs, which integrates epitope-paratope analysis, computational modeling, and rational design approaches, complemented by comprehensive experimental assessment. This approach was applied to optimize MEDI8852, a nAb targeting the stalk region of influenza A virus hemagglutinin (HA). The resulting variant, M18.1.2.2, shows a marked enhancement in both affinity and neutralizing efficacy, as demonstrated both in vitro and in vivo. Computational modeling reveals that this improvement can be attributed to the fine-tuning of interactions between the antibody's side-chains and the epitope residues that are highly conserved across the influenza A virus HA stalk. Our dry-wet iterative protocol for nAb optimization presented here yielded a promising candidate for influenza intervention.
Keywords: RosettaAntibodyDesign; computational biology; hemagglutinin; influenza A virus; neutralizing antibody.
. 2025 Jan 21:S0969-2126(25)00002-4.
doi: 10.1016/j.str.2025.01.002. Online ahead of print. Computational design and improvement of a broad influenza virus HA stem targeting antibody
Huarui Duan[SUP] 1 [/SUP], Xiaojing Chi[SUP] 2 [/SUP], Xuehua Yang[SUP] 1 [/SUP], Shengnan Pan[SUP] 1 [/SUP], Xiuying Liu[SUP] 1 [/SUP], Peixiang Gao[SUP] 1 [/SUP], Fangyuan Zhang[SUP] 1 [/SUP], Xinhui Zhang[SUP] 1 [/SUP], Xuemeng Dong[SUP] 1 [/SUP], Yi Liao[SUP] 1 [/SUP], Wei Yang[SUP] 3 [/SUP]
Affiliations
- PMID: 39884272
- DOI: 10.1016/j.str.2025.01.002
Broadly neutralizing antibodies (nAbs) are vital therapeutic tools to counteract both pandemic and seasonal influenza threats. Traditional strategies for optimizing nAbs generally rely on labor-intensive, high-throughput mutagenesis screens. Here, we present an innovative structure-based design framework for the optimization of nAbs, which integrates epitope-paratope analysis, computational modeling, and rational design approaches, complemented by comprehensive experimental assessment. This approach was applied to optimize MEDI8852, a nAb targeting the stalk region of influenza A virus hemagglutinin (HA). The resulting variant, M18.1.2.2, shows a marked enhancement in both affinity and neutralizing efficacy, as demonstrated both in vitro and in vivo. Computational modeling reveals that this improvement can be attributed to the fine-tuning of interactions between the antibody's side-chains and the epitope residues that are highly conserved across the influenza A virus HA stalk. Our dry-wet iterative protocol for nAb optimization presented here yielded a promising candidate for influenza intervention.
Keywords: RosettaAntibodyDesign; computational biology; hemagglutinin; influenza A virus; neutralizing antibody.