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对内在无序蛋白质的集体的实验推断结构确定

David H Brookes¹, Teresa Head-Gordon¹

¹Department of Chemistry, ‡Department of Bioengineering, §Department of Chemical and Biomolecular Engineering, ∥Chemical Sciences Division, Lawrence Berkeley National Laboratory, University of California , Berkeley, California 94720, United States.

Journal of the American Chemical Society

|March 12, 2016

概括

我们开发了一种新的贝叶斯方法,即实验推断结构确定 (EISD),使用NMR数据快速排列蛋白质结构组合. 这种方法优化了参数,提高了内在无序蛋白质 (IDP) 的精度.

科学领域:

生物物理
结构生物学
计算化学

背景情况:

内在无序的蛋白质 (IDP) 缺乏稳定的结构,对传统的结构确定构成挑战.
核磁共振 (NMR) 光谱为特征蛋白质动态提供了有价值的数据 (化学转移,J合).
从结构模型到实验观测值的准确反向计算对于整体精细化至关重要.

研究的目的:

开发一个强大的贝叶斯框架来确定最可能的IDP结构组合.
通过优化干扰参数和改进数据集成来解决现有方法的局限性.
为了快速排列大型结构组合的IDP.

主要方法:

贝叶斯式方法整合了NMR化学转移和J合数据及其相关错误.
用随机变量优化实验和反向计算的麻烦参数.
实验推断结构确定 (EISD) 方法的开发,用于快速组合排名.

主要成果:

通过EISD方法,可以高效地对大型结构组合 (数以万计的构造) 进行排序.
对折叠和错误折叠的蛋白质的应用凸显了波兹曼加权先验的问题.
作为当前IDP组合确定的主要局限性,确定了后期计算的准确性较差.
通过使用β-粉样蛋白作为一个例子,证明了减少逆向计算错误的不确定性可以改善IDP组的区别.

结论:

EISD方法为分析NMR数据和排列IDP的结构集提供了强大的工具.
提高结构对可观测的逆向计算的准确性对于推进IDP结构研究至关重要.
这项工作有助于更准确地描述动态蛋白质结构.

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