Ford Colby T, Yasa Shirish, Obeid Khaled, Jaimes Rafael, Tomezsko Phillip J, Guirales-Medrano Sayal, White Richard Allen, Janies Daniel
Center for Computational Intelligence to Predict Health and Environmental Risks (CIPHER), University of North Carolina at Charlotte, Charlotte, NC, USA; Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC, USA; School of Data Science, University of North Carolina at Charlotte, Charlotte, NC, USA; Tuple LLC, Charlotte, NC, USA.
Center for Computational Intelligence to Predict Health and Environmental Risks (CIPHER), University of North Carolina at Charlotte, Charlotte, NC, USA; Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC, USA.
EBioMedicine. 2025 Apr;114:105632. doi: 10.1016/j.ebiom.2025.105632. Epub 2025 Mar 17.
The United States Department of Agriculture has recently released reports that show samples collected from 2022 to 2025 of highly pathogenic avian influenza (H5N1) have been detected in mammals and birds. Up to February 2025, the United States Centres for Disease Control and Prevention reports that there have been 67 humans infected with H5N1 since 2024 with 1 death. The broader potential impact on human health remains unclear.
In this study, we computationally model 1804 protein complexes consisting of various H5 isolates from 1959 to 2024 against 11 haemagglutinin domain 1 (HA1)-neutralising antibodies. This was performed using AI-based protein folding and physics-based simulations of the antibody-antigen interactions. We analysed binding affinity changes over time and across various antibodies using multiple biochemical and biophysical binding metrics.
This study shows a trend of weakening binding affinity of existing antibodies against H5 isolates over time, indicating that the H5N1 virus is evolving immune escape from our therapeutic and immunological defences. We also found that based on the wide variety of host species and geographic locations in which H5N1 was observed to have been transmitted from birds to mammals, there is not a single central reservoir host species or location associated with H5N1's spread.
These results indicate that the virus has potential to move from epidemic to pandemic status. This study illustrates the value of high-performance computing to rapidly model protein-protein interactions and viral genomic sequence data at-scale for functional insights into medical preparedness.
No external funding was used in this study.
美国农业部最近发布的报告显示，在2022年至2025年采集的样本中，已在哺乳动物和鸟类体内检测到高致病性禽流感（H5N1）。截至2025年2月，美国疾病控制与预防中心报告称，自2024年以来已有67人感染H5N1，其中1人死亡。对人类健康更广泛的潜在影响仍不明确。
在本研究中，我们利用基于人工智能的蛋白质折叠以及抗体 - 抗原相互作用的基于物理的模拟，对1959年至2024年的各种H5分离株组成的1804个蛋白质复合物与11种血凝素结构域1（HA1）中和抗体进行了计算建模。我们使用多种生化和生物物理结合指标分析了随时间以及不同抗体之间结合亲和力的变化。
本研究表明，现有抗体对H5分离株的结合亲和力随时间呈减弱趋势，这表明H5N1病毒正在从我们的治疗和免疫防御中进化出免疫逃逸能力。我们还发现，基于观察到H5N1从鸟类传播到哺乳动物的多种宿主物种和地理位置，不存在与H5N1传播相关的单一核心宿主物种或地点。
这些结果表明该病毒有可能从流行状态转变为大流行状态。本研究说明了高性能计算对于大规模快速建模蛋白质 - 蛋白质相互作用和病毒基因组序列数据以获得医学防范功能见解的价值。
本研究未使用外部资金。
