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探索微生物fucosidases的序列功能空间

Ana Martínez Gascueña¹, Haiyang Wu^1,2, Rui Wang^3,4,5

¹The Gut Microbes and Health Institute Strategic Programme, Quadram Institute Bioscience, Norwich Research Park, Norwich, NR4 7UQ, UK.

Communications chemistry

|June 18, 2024

概括

微生物α-L-fucosidases (GH29) 用序列相似性网络 (SSN) 和机器学习进行了功能性特征. 这种方法准确地分类了已知的酶,并确定了具有特定功能的新型甘氨酸酸酶.

科学领域:

酶学是一种酶学.
葡萄糖生物学葡萄糖生物学
生物信息学是一种生物信息学.

背景情况:

微生物alpha-L-fucosidases是裂开alpha-L-fucosidic链接的酶,可以促进转甘油化.
这些酶属于甘氨酸酸酶 (GHs) 超级家族,在碳水化合物活性酶数据库中具体指GH29.
了解GH29 fucosidases中的序列功能关系对于酶的发现和应用至关重要.

研究的目的:

为了探索GH29fucosidases的序列功能格局.
为了功能性地表征选定的GH29 fucosidases,并确定它们的基质特异性.
开发和应用计算模型来预测酶功能和分类新型GH29序列.

主要方法:

序列相似性网络 (SSN) 分析以选择多样化的GH29 fucosidases进行表征.
高性能离子交换色谱与脉冲电比检测 (HPAEC-PAD) 和液体色谱-光检测-并联质谱 (LC-FD-MS/MS) 用于基质特异性分析.
射线晶体学,STD NMR,TLC,ESI-MS和NMR用于结构确定和转化活性评估.
机器学习模型,包括轻量级蛋白质语言模型,用于基于序列的集群和GH29序列的分类.

主要成果:

十五个GH29的fucosidases被功能性地表征,揭示了各种alpha-linked fucosylated oligosaccharides和glycoconjugates的特异性,与SSN集群保持一致.
阐明了Bifidobacterium小行星GH29fucosidase对alpha1,6链接和FA2G2N-glycan的特异性的结构基础.
通过使用 GlcNAc 和 3FN 作为受体证实了转化活性.
机器学习模型准确地将34,258个非冗余的GH29序列分配到SSN集群中,证明了计算方法的力量.

结论:

SSN 分析是探索 GH29 fucosidases 的序列功能空间的一个有价值的工具.
实验性表征与机器学习模型的整合使得可以准确地预测和分类酶功能.
这些结合的计算和实验策略对发现具有量身定制特性的新型葡萄糖酸酶具有重大前景.

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