Department of Chemistry, University of Calgary, Calgary, AB, Canada.
Department of Chemistry, University of Calgary, Calgary, AB, Canada.
Biochim Biophys Acta Proteins Proteom. 2017 Nov;1865(11 Pt B):1654-1663. doi: 10.1016/j.bbapap.2017.06.016. Epub 2017 Jun 22.
The 3D atomic structures of biomolecules and their complexes are key to our understanding of biomolecular function, recognition, and mechanism. However, it is often difficult to obtain structures, particularly for systems that are complex, dynamic, disordered, or exist in environments like cell membranes. In such cases sparse data from a variety of paramagnetic NMR experiments offers one possible source of structural information. These restraints can be incorporated in computer modeling algorithms that can accurately translate the sparse experimental data into full 3D atomic structures. In this review, we discuss various types of paramagnetic NMR/computational hybrid modeling techniques that can be applied to successful modeling of not only the atomic structure of proteins but also their interacting partners. This article is part of a Special Issue entitled: Biophysics in Canada, edited by Lewis Kay, John Baenziger, Albert Berghuis and Peter Tieleman.
生物分子及其复合物的 3D 原子结构是我们理解生物分子功能、识别和机制的关键。然而,通常很难获得结构,特别是对于复杂、动态、无序的系统,或者存在于细胞膜等环境中的系统。在这种情况下,来自各种顺磁 NMR 实验的稀疏数据提供了一种可能的结构信息来源。这些约束条件可以被纳入计算机建模算法中,这些算法可以将稀疏的实验数据准确地转化为完整的 3D 原子结构。在这篇综述中,我们讨论了各种类型的顺磁 NMR/计算混合建模技术,这些技术不仅可以成功地对蛋白质的原子结构进行建模,还可以对其相互作用的伴侣进行建模。本文是由 Lewis Kay、John Baenziger、Albert Berghuis 和 Peter Tieleman 编辑的题为“加拿大的生物物理学”特刊的一部分。