针对最新H5N1禽流感毒株的基于人工智能的抗体设计

IF 4.4 2区 生物学 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY
Computational and structural biotechnology journal Pub Date : 2025-06-27 eCollection Date: 2025-01-01 DOI:10.1016/j.csbj.2025.06.026
Nicholas Santolla, Colby T Ford
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引用次数: 0

摘要

仅在2025年,H5N1禽流感就会在各种动物物种中造成数千例感染,包括鸡和牛等禽类和哺乳动物牲畜,并因禽类与哺乳动物之间的传播而对人类健康构成威胁。自2024年4月以来,美国已有70例人感染H5N1流感病例,最近的研究表明,我们目前的抗体防御正在减弱。因此,迫切需要发现新的治疗方法,以对抗最新的病毒毒株。在这项研究中,我们提出了自动抗体扩散和评估的Frankies框架。该管道用于自动生成30个新的抗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,这些抗体与已知的治疗性和诱导性抗体结合相似。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AI-based antibody design targeting recent H5N1 avian influenza strains.

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 Frankies 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.

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来源期刊
Computational and structural biotechnology journal
Computational and structural biotechnology journal Biochemistry, Genetics and Molecular Biology-Biophysics
CiteScore
9.30
自引率
3.30%
发文量
540
审稿时长
6 weeks
期刊介绍: Computational and Structural Biotechnology Journal (CSBJ) is an online gold open access journal publishing research articles and reviews after full peer review. All articles are published, without barriers to access, immediately upon acceptance. The journal places a strong emphasis on functional and mechanistic understanding of how molecular components in a biological process work together through the application of computational methods. Structural data may provide such insights, but they are not a pre-requisite for publication in the journal. Specific areas of interest include, but are not limited to: Structure and function of proteins, nucleic acids and other macromolecules Structure and function of multi-component complexes Protein folding, processing and degradation Enzymology Computational and structural studies of plant systems Microbial Informatics Genomics Proteomics Metabolomics Algorithms and Hypothesis in Bioinformatics Mathematical and Theoretical Biology Computational Chemistry and Drug Discovery Microscopy and Molecular Imaging Nanotechnology Systems and Synthetic Biology
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