Hybrid methods for combined experimental and computational determination of protein structure
Hybrid methods for combined experimental and computational determination of protein structure
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DOI：
10.1063/5.0026025
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发表时间：
2020-12-28
影响因子：
4.4
通讯作者：
Lindert, Steffen
中科院分区：
文献类型：
--
作者：
Seffernick, Justin T.;Lindert, Steffen
蛋白质结构的知识对于理解生物学功能，开发新的治疗方法和做出详细的机制假设至关重要。因此，对精确阐明蛋白质三维结构的方法有很高的需求。虽然有一些实验技术可以常规地提供高分辨率的结构，例如X射线晶体学，核磁共振（NMR）和冷冻EM，这些技术已经被开发用于确定蛋白质的结构，但这些技术都有缺点，因此不能在所有情况下使用。然而，此外，大量的实验技术，提供了一些结构信息，但不足以分配原子的位置与高的确定性已经开发出来。这些方法提供了稀疏的实验数据，在某些情况下，这些数据也可能是嘈杂和不准确的。在无法通过实验确定蛋白质结构的情况下，可以使用计算结构预测方法作为替代方法。虽然在大量的研究中，计算方法可以在没有任何实验数据的情况下进行，但将稀疏的实验数据纳入这些预测方法中已经取得了显着的改进。在这方面，我们涵盖了许多成功的综合建模，计算建模与实验数据，特别是蛋白质折叠，蛋白质-蛋白质对接，和分子动力学模拟。我们描述的方法，将稀疏的数据从冷冻EM，NMR，质谱，电子顺磁共振，小角度X射线散射，福斯特共振能量转移，遗传序列协变。最后，我们强调了该领域的一些主要挑战以及未来可能的方向。
Knowledge of protein structure is paramount to the understanding of biological function, developing new therapeutics, and making detailed mechanistic hypotheses. Therefore, methods to accurately elucidate three-dimensional structures of proteins are in high demand. While there are a few experimental techniques that can routinely provide high-resolution structures, such as x-ray crystallography, nuclear magnetic resonance (NMR), and cryo-EM, which have been developed to determine the structures of proteins, these techniques each have shortcomings and thus cannot be used in all cases. However, additionally, a large number of experimental techniques that provide some structural information, but not enough to assign atomic positions with high certainty have been developed. These methods offer sparse experimental data, which can also be noisy and inaccurate in some instances. In cases where it is not possible to determine the structure of a protein experimentally, computational structure prediction methods can be used as an alternative. Although computational methods can be performed without any experimental data in a large number of studies, inclusion of sparse experimental data into these prediction methods has yielded significant improvement. In this Perspective, we cover many of the successes of integrative modeling, computational modeling with experimental data, specifically for protein folding, protein-protein docking, and molecular dynamics simulations. We describe methods that incorporate sparse data from cryo-EM, NMR, mass spectrometry, electron paramagnetic resonance, small-angle x-ray scattering, Forster resonance energy transfer, and genetic sequence covariation. Finally, we highlight some of the major challenges in the field as well as possible future directions.