禽流感甲型 H5N1 血凝素蛋白模型在 1959-2023 株中重复出现不同的结构模式。

IF 2 4区 生物学 Q2 BIOLOGY
Marni E. Cueno, Noriaki Kamio, Kenichi Imai
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引用次数: 0

摘要

甲型流感 H5N1 血凝素(HA)在病毒致病过程中起着至关重要的作用,HA 受体结合域(RBD)的变化被认为是病毒致病过程发生改变的原因。HA内部经常发生突变,从而导致HA结构发生变化,进而促进蛋白质的进化。然而,在 1959-2023 年的 H5N1 病毒进化过程中,可能发生的突变导致 HA 蛋白的还原(回到祖先的蛋白构象),进而产生不同的 HA 结构模式,这种情况从未被研究过。在此,我们生成并验证了 HA 模型的质量,确定了相似的 HA 结构模式,并阐明了 HA RBD 结构动态中可能存在的变化。我们的结果表明,1959-2023 年的 H5N1 HA 模型有 7 种不同的结构模式,这表明在病毒进化过程中,HA 蛋白可能发生了还原。同样,我们发现在这 7 种不同的结构模式中,HA RBD 结构动态也各不相同,这可能会影响病毒的致病机理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Avian influenza A H5N1 hemagglutinin protein models have distinct structural patterns re-occurring across the 1959–2023 strains
Influenza A H5N1 hemagglutinin (HA) plays a crucial role in viral pathogenesis and changes in the HA receptor binding domain (RBD) have been attributed to alterations in viral pathogenesis. Mutations often occur within the HA which in-turn results in HA structural changes that consequently contribute to protein evolution. However, the possible occurrence of mutations that results to reversion of the HA protein (going back to an ancestral protein conformation) which in-turn creates distinct HA structural patterns across the 1959–2023 H5N1 viral evolution has never been investigated. Here, we generated and verified the quality of the HA models, identified similar HA structural patterns, and elucidated the possible variations in HA RBD structural dynamics. Our results show that there are 7 distinct structural patterns occurring among the 1959–2023 H5N1 HA models which suggests that reversion of the HA protein putatively occurs during viral evolution. Similarly, we found that the HA RBD structural dynamics vary among the 7 distinct structural patterns possibly affecting viral pathogenesis.
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来源期刊
Biosystems
Biosystems 生物-生物学
CiteScore
3.70
自引率
18.80%
发文量
129
审稿时长
34 days
期刊介绍: BioSystems encourages experimental, computational, and theoretical articles that link biology, evolutionary thinking, and the information processing sciences. The link areas form a circle that encompasses the fundamental nature of biological information processing, computational modeling of complex biological systems, evolutionary models of computation, the application of biological principles to the design of novel computing systems, and the use of biomolecular materials to synthesize artificial systems that capture essential principles of natural biological information processing.
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