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Combining NMR ensembles and molecular dynamics simulations provides more realistic models of protein structures in solution and leads to better chemical shift prediction

机译:结合NMR集成和分子动力学模拟可提供溶液中蛋白质结构的更真实模型,并提供更好的化学位移预测

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While chemical shifts are invaluable for obtaining structural information from proteins, they also offer one of the rare ways to obtain information about protein dynamics. A necessary tool in transforming chemical shifts into structural and dynamic information is chemical shift prediction. In our previous work we developed a method for 4D prediction of protein 1H chemical shifts in which molecular motions, the 4th dimension, were modeled using molecular dynamics (MD) simulations. Although the approach clearly improved the prediction, the X-ray structures and single NMR conformers used in the model cannot be considered fully realistic models of protein in solution. In this work, NMR ensembles (NMRE) were used to expand the conformational space of proteins (e.g. side chains, flexible loops, termini), followed by MD simulations for each conformer to map the local fluctuations. Compared with the non-dynamic model, the NMRE+MD model gave 6-17% lower root-mean-square (RMS) errors for different backbone nuclei. The improved prediction indicates that NMR ensembles with MD simulations can be used to obtain a more realistic picture of protein structures in solutions and moreover underlines the importance of short and long time-scale dynamics for the prediction. The RMS errors of the NMRE+MD model were 0.24, 0.43, 0.98, 1.03, 1.16 and 2.39 ppm for 1Hα, 1HN, ~(13)Cα, ~(13)Cβ, ~(13)CO and backbone ~(15)N chemical shifts, respectively. The model is implemented in the prediction program 4DSPOT, available at http://www.uef.fi/4dspot.
机译:虽然化学位移对于从蛋白质获得结构信息非常宝贵,但它们也提供了获得有关蛋白质动力学信息的罕见方法之一。将化学位移转换为结构和动态信息的必要工具是化学位移预测。在我们以前的工作中,我们开发了一种蛋白质1H化学位移的4D预测方法,其中使用分子动力学(MD)模拟对第四维分子运动进行建模。尽管该方法明显改善了预测结果,但不能将模型中使用的X射线结构和单个NMR构象异构体视为溶液中蛋白质的完全真实模型。在这项工作中,使用NMR集成体(NMRE)来扩展蛋白质的构象空间(例如侧链,柔性环,末端),然后通过每个模子的MD模拟来绘制局部波动。与非动态模型相比,NMRE + MD模型对不同主干核的均方根(RMS)误差降低了6-17%。改进的预测结果表明,具有MD模拟的NMR集成可用于获得溶液中蛋白质结构的更真实的图片,而且强调了短期和长期时间尺度动力学对于预测的重要性。对于1Hα,1HN,〜(13)Cα,〜(13)Cβ,〜(13)CO和骨架〜(15),NMRE + MD模型的RMS误差分别为0.24、0.43、0.98、1.03、1.16和2.39 ppm。分别发生N次化学位移。该模型在预测程序4DSPOT中实现,该程序可从http://www.uef.fi/4dspot获得。

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