Frank Aaron T, Law Sean M, Ahlstrom Logan S, Brooks Charles L
Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
J Chem Theory Comput. 2015 Jan 13;11(1):325-31. doi: 10.1021/ct5009125.
Given the demonstrated utility of coarse-grained modeling and simulations approaches in studying protein structure and dynamics, developing methods that allow experimental observables to be directly recovered from coarse-grained models is of great importance. In this work, we develop one such method that enables protein backbone chemical shifts (1HN, 1Hα, 13Cα, 13C, 13Cβ, and 15N) to be predicted from Cα coordinates. We show that our Cα-based method, LARMORCα, predicts backbone chemical shifts with comparable accuracy to some all-atom approaches. More importantly, we demonstrate that LARMORCα predicted chemical shifts are able to resolve native structure from decoy pools that contain both native and non-native models, and so it is sensitive to protein structure. As an application, we use LARMORCα to characterize the transient state of the fast-folding protein gpW using recently published NMR relaxation dispersion derived backbone chemical shifts. The model we obtain is consistent with the previously proposed model based on independent analysis of the chemical shift dispersion pattern of the transient state. We anticipate that LARMORCα will find utility as a tool that enables important protein conformational substates to be identified by “parsing” trajectories and ensembles generated using coarse-grained modeling and simulations.
鉴于粗粒度建模和模拟方法在研究蛋白质结构和动力学方面已展现出的效用，开发能让实验可观测量直接从粗粒度模型中恢复的方法至关重要。在这项工作中，我们开发了这样一种方法，它能根据Cα坐标预测蛋白质主链化学位移（1HN、1Hα、13Cα、13C、13Cβ和15N）。我们表明，我们基于Cα的方法LARMORCα预测主链化学位移的准确性与一些全原子方法相当。更重要的是，我们证明LARMORCα预测的化学位移能够从包含天然和非天然模型的诱饵库中分辨出天然结构，因此它对蛋白质结构敏感。作为应用，我们使用LARMORCα，利用最近发表的基于NMR弛豫色散得到的主链化学位移来表征快速折叠蛋白gpW的瞬态。我们获得的模型与先前基于对瞬态化学位移色散模式的独立分析提出的模型一致。我们预计LARMORCα将作为一种工具发挥作用，通过“解析”使用粗粒度建模和模拟生成的轨迹和系综来识别重要的蛋白质构象亚状态。
