Predicting protein backbone chemical shifts from Cα coordinates: extracting high resolution experimental observables from low resolution models.
Predicting protein backbone chemical shifts from Cα coordinates: extracting high resolution experimental observables from low resolution models.
复制标题
从 Cα 坐标预测蛋白质主链化学位移：从低分辨率模型中提取高分辨率实验观测值。
DOI：
10.1021/ct5009125
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发表时间：
2015
影响因子：
5.5
通讯作者：
Brooks3rd,CharlesL
中科院分区：
文献类型：
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作者：
Frank,AaronT;Law,SeanM;Ahlstrom,LoganS;Brooks3rd,CharlesL
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的实用性，开发允许实验观测值直接从粗粒度模型中恢复的方法是非常重要的。在这项工作中，我们开发了一种这样的方法，使蛋白质骨架化学位移（1HN，1Hα，13 C α，13 C，13 C β和15 N）预测从Cα坐标。我们表明，我们的Cα为基础的方法，LARMORCα，预测骨干化学位移与一些全原子的方法相当的准确性。更重要的是，我们证明了LARMORCα预测的化学位移能够从包含天然和非天然模型的诱饵池中分辨天然结构，因此它对蛋白质结构敏感。作为一个应用，我们使用LARMORCα来表征的快速折叠蛋白质gpW的瞬态使用最近发表的NMR弛豫色散衍生的骨干化学位移。我们得到的模型是一致的与先前提出的模型的基础上独立分析的化学位移的瞬态色散模式。我们预计，LARMORCα将发现效用作为一种工具，使重要的蛋白质构象亚态被识别的“解析”轨迹和合奏使用粗粒度的建模和模拟。
Given the demonstrated utility of coarse-grained modeling and simulations approaches in studying protein structure and dynamics, developing methods that allow experimental observables to bedirectlyrecovered 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, and15N) 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 LARMORCαpredicted 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 LARMORCαto 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 LARMORCαwill 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.