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使用超级二次结构代码预测流感病毒血蛋白与氨基酸突变的合规变异性

Hiroshi Izumi¹

¹National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki, Japan. izumi.h@aist.go.jp.

Methods in molecular biology (Clifton, N.J.)

|November 14, 2024

概括

超次结构代码 (SSSC) 预测了蛋白质的灵活性和刚性. 这种方法精确分析流感病毒的血凝素,揭示了与突变和流行病相关的结构变化.

科学领域:

结构生物学是结构生物学.
生物信息学是一种生物信息学.
计算生物学是一种计算生物学.

背景情况:

超次结构代码 (SSSC) 使用构造术语 (H,S,T,D) 来识别来自蛋白质数据库 (PDB) 的蛋白质图案.
深度神经网络为预测蛋白质结构和功能提供了先进的方法.

研究的目的:

描述使用SSSC进行构造变异性预测的协议.
使用这种预测方法分析流感病毒血素及其突变.

主要方法:

使用基于深度神经网络的系统 (SSSCPreds) 来预测蛋白质的灵活性/刚性和区域形状.
应用SSSC来分析流感A (H1N1pdm和H5N1) 血凝素中的形状变异性模式.
将预测的序列灵活性/刚性图与来自突变的序列-表型数据相关联.

主要成果:

SSSCPreds准确预测蛋白质的灵活性和刚性,使得与突变效应的相关性.
H1N1pdm和H5N1血凝素的形态变异性模式显示出强烈的相似性,主要在H5N1furin裂解部位上有所不同.
可视化的形状图突出显示了病毒变体的转变和增加的灵活性,为流感流行提供了洞察力.

结论:

基于SSSC的构造变异性预测方法足够准确,可以与序列到表型数据相关联.
这种方法提供了对病毒变异过渡和灵活性变化的视觉理解,与流感流行相关.
由于有限的测量条件影响预测准确度,pH 5的蛋白质存在限制.

关键词:

合规变性是指合规的变性.深度神经网络是一个神经网络.流感病毒的血凝素是流感病毒的血凝素.动机动机动机超级二级结构的代码.

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