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来自DEER的生物聚合物的生物聚合物数据的多模式距离分布的局部重建

Karen Tsay¹, Timothy Keller¹, Yann Fichou²

¹Department of Chemistry and Biochemistry, University of California, Santa Barbara, CA - 93106.

bioRxiv : the preprint server for biology

|January 13, 2025

概括

脉冲双极ESR光谱 (PDS) 现在可以可靠地确定内在无序蛋白 (IDP) 的距离分布. 新的波形无声化和数学反转方法 (WavPDS和SF-SVD) 克服了IDP结构性特征的先前限制.

科学领域:

生物物理学的生物物理.
蛋白质科学蛋白质科学
聚合物科学聚合物科学

背景情况:

脉冲二极电子自旋共振光谱 (PDS) 描述了内在无序蛋白质 (IDP) 和聚合物的结构性质和形状变化.
对于使用PDS的IDP,确定距离分布P(r) 是具有挑战性的,因为它们的结构信息固有的不确定性和广度.
现有的 PDS 方法通常需要先前的知识或用户的解释,限制了对 IDP 结构的客观分析.

研究的目的:

为了证明波束无声化 (WavPDS) 和斯里瓦斯塔瓦-自由单值值分解 (SF-SVD) 的组合可以可靠地确定IDP的距离分布P (r).
证明这些方法可以解决复杂的P(r) 形状,包括广泛和混合的特征,而不需要先前对IDP结构的假设.
用模型系统和内在无序蛋白质验证WavPDS和SF-SVD的性能.

主要方法:

在PDS数据中应用波纹无声化 (WavPDS) 来降低噪声.
使用斯里瓦斯塔瓦-弗里德单数值分解 (SF-SVD) 点向数学反转技术进行距离分布P (r) 分析.
测试WavPDS和SF-SVD结合的方法在模型规则分子,聚乙烯糖醇聚合物和蛋白质的部分上.

主要成果:

与SF-SVD相结合的WavPDS有效地解决了国内流离失所者在距离分布中的广泛和混合特征.
这些方法提供了透明和客观的分析,不需要调节的参数,并产生了独立于用户判断的结果.
为模型系统和IDP生成了可靠的P(r) 解决方案,包括不确定性和错误分析.

结论:

波式PDS和SF-SVD方法显著提高了PDS的能力,以表征内部流离失所者的结构动态.
这种方法克服了分析国内流离失所者固有的广泛和不确定的距离分布的关键局限性.
由于WavPDS和SF-SVD的透明性和无参数性,使得PDS成为IDP结构研究中更强大的工具.

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