Leelananda Sumudu P, Lindert Steffen
Department of Chemistry and Biochemistry, Ohio State University , Columbus, Ohio 43210, United States.
J Chem Theory Comput. 2017 Oct 10;13(10):5131-5145. doi: 10.1021/acs.jctc.7b00464. Epub 2017 Sep 26.
Knowing atomistic details of proteins is essential not only for the understanding of protein function but also for the development of drugs. Experimental methods such as X-ray crystallography, NMR, and cryo-electron microscopy (cryo-EM) are the preferred forms of protein structure determination and have achieved great success over the most recent decades. Computational methods may be an alternative when experimental techniques fail. However, computational methods are severely limited when it comes to predicting larger macromolecule structures with little sequence similarity to known structures. The incorporation of experimental restraints in computational methods is becoming increasingly important to more reliably predict protein structure. One such experimental input used in structure prediction and refinement is cryo-EM densities. Recent advances in cryo-EM have arguably revolutionized the field of structural biology. Our previously developed cryo-EM-guided Rosetta-MD protocol has shown great promise in the refinement of soluble protein structures. In this study, we extended cryo-EM density-guided iterative Rosetta-MD to membrane proteins. We also improved the methodology in general by picking models based on a combination of their score and fit-to-density during the Rosetta model selection. By doing so, we have been able to pick models superior to those with the previous selection based on Rosetta score only and we have been able to further improve our previously refined models of soluble proteins. The method was tested with five membrane spanning protein structures. By applying density-guided Rosetta-MD iteratively we were able to refine the predicted structures of these membrane proteins to atomic resolutions. We also showed that the resolution of the density maps determines the improvement and quality of the refined models. By incorporating high-resolution density maps (∼4 Å), we were able to more significantly improve the quality of the models than when medium-resolution maps (6.9 Å) were used. Beginning from an average starting structure root mean square deviation (RMSD) to native of 4.66 Å, our protocol was able to refine the structures to bring the average refined structure RMSD to 1.66 Å when 4 Å density maps were used. The protocol also successfully refined the HIV-1 CTD guided by an experimental 5 Å density map.
了解蛋白质的原子细节不仅对于理解蛋白质功能至关重要,而且对于药物开发也至关重要。诸如X射线晶体学、核磁共振和冷冻电子显微镜(cryo-EM)等实验方法是确定蛋白质结构的首选形式,并且在最近几十年中取得了巨大成功。当实验技术失败时,计算方法可能是一种替代方法。然而,在预测与已知结构序列相似性很小的较大大分子结构时,计算方法受到严重限制。在计算方法中纳入实验约束对于更可靠地预测蛋白质结构变得越来越重要。结构预测和优化中使用的一种此类实验输入是冷冻电子显微镜密度。冷冻电子显微镜的最新进展可以说是彻底改变了结构生物学领域。我们之前开发的冷冻电子显微镜引导的Rosetta-MD协议在优化可溶性蛋白质结构方面显示出了巨大的前景。在这项研究中,我们将冷冻电子显微镜密度引导的迭代Rosetta-MD扩展到膜蛋白。我们还通过在Rosetta模型选择过程中基于模型得分和与密度的拟合度相结合来选择模型,总体上改进了方法。通过这样做,我们能够选择比仅基于Rosetta得分的先前选择更好的模型,并且能够进一步改进我们之前优化的可溶性蛋白质模型。该方法用五个跨膜蛋白结构进行了测试。通过迭代应用密度引导的Rosetta-MD,我们能够将这些膜蛋白的预测结构优化到原子分辨率。我们还表明,密度图的分辨率决定了优化模型的改进程度和质量。通过纳入高分辨率密度图(约4 Å),我们能够比使用中等分辨率图(6.9 Å)时更显著地提高模型质量。从平均起始结构到天然结构的均方根偏差(RMSD)为4.66 Å开始,当使用4 Å密度图时,我们的协议能够将结构优化到使平均优化结构RMSD达到1.66 Å。该协议还成功地在实验性5 Å密度图的引导下优化了HIV-1 CTD。