Santolla Nicholas, Ford Colby T
University of North Carolina at Charlotte, School of Data Science, Charlotte, NC, USA.
University of North Carolina at Charlotte, Center for Computational Intelligence to Predict Health and Environmental Risks (CIPHER), Charlotte, NC, USA.
Comput Struct Biotechnol J. 2025 Jun 27;27:2915-2923. doi: 10.1016/j.csbj.2025.06.026. eCollection 2025.
In 2025 alone, H5N1 avian influenza is responsible for thousands of infections across various animal species, including avian and mammalian livestock such as chickens and cows, and poses a threat to human health due to avian-to-mammalian transmission. There have been 70 human cases of H5N1 influenza in the United States since April 2024 and, as shown in recent studies, our current antibody defenses are waning. Thus, it is imperative to discover new therapeutics in the fight against more recent strains of the virus. In this study, we present the framework for automated antibody diffusion and assessment. This pipeline was used to automate the generation of 30 novel anti-HA1 Fv antibody fragment sequences, fold them into 3-dimensional structures, and then dock against a recent H5N1 HA1 antigen structure for binding evaluation. Here we show the utility of artificial intelligence in the discovery of novel antibodies against specific H5N1 strains of interest, which bind similarly to known therapeutic and elicited antibodies.
仅在2025年，H5N1禽流感就在包括鸡和牛等禽类和哺乳动物家畜在内的各种动物物种中引发了数千起感染事件，并且由于从禽类到哺乳动物的传播，对人类健康构成了威胁。自2024年4月以来，美国已有70例H5N1流感人类病例，而且如最近的研究所显示的，我们目前的抗体防御能力正在减弱。因此，在对抗该病毒的最新毒株方面发现新的治疗方法势在必行。在本研究中，我们展示了自动抗体扩散和评估的框架。该流程用于自动生成30个新型抗HA1 Fv抗体片段序列，将它们折叠成三维结构，然后与最近的H5N1 HA1抗原结构对接以进行结合评估。在这里，我们展示了人工智能在发现针对特定H5N1毒株的新型抗体方面的效用，这些抗体的结合方式与已知治疗性抗体和诱导产生的抗体相似。
