Iterative Molecular Dynamics–Rosetta Membrane Protein Structure Refinement Guided by Cryo-EM Densities

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.了解蛋白质的原子级细节，不仅是理解蛋白质功能的核心前提，同时也是药物开发的必要基础。X射线晶体学、核磁共振（NMR）、冷冻电子显微镜（cryo-electron microscopy, cryo-EM）等实验方法是当前蛋白质结构测定的主流手段，近数十年来已取得了卓越成就。当实验技术无法达成结构测定目标时，计算方法可作为替代方案。然而，在预测与已知结构序列相似性极低的大型大分子结构时，现有计算方法存在显著局限。在计算方法中融入实验约束条件，以更可靠地预测蛋白质结构的需求正日益迫切。其中一类用于结构预测与精修的实验输入数据即为冷冻电镜密度图。冷冻电镜领域的近期进展堪称结构生物学领域的革命性突破。我们此前开发的冷冻电镜引导的Rosetta–MD流程，在可溶性蛋白质结构精修方面已展现出良好应用前景。本研究将冷冻电镜密度引导的迭代式Rosetta–MD流程拓展至膜蛋白领域，并对方法本身进行了通用化改进：在Rosetta模型筛选阶段，结合模型得分与密度拟合度进行模型选取。通过该方式，我们筛选得到的模型优于此前仅基于Rosetta得分选取的模型，同时进一步优化了我们此前针对可溶性蛋白质的精修模型。本方法针对五种跨膜蛋白结构开展了测试。通过迭代应用密度引导的Rosetta-MD流程，我们成功将这些膜蛋白的预测结构精修至原子分辨率级别。研究同时表明，密度图的分辨率决定了精修模型的提升幅度与最终质量：相较于使用中等分辨率（6.9 Å）密度图的情况，采用高分辨率（约4 Å）密度图时，模型质量的提升更为显著。以与天然结构的平均初始均方根偏差（root mean square deviation, RMSD）4.66 Å为起点，当使用4 Å分辨率的密度图时，本流程可将精修后结构的平均均方根偏差降至1.66 Å。该流程还在基于5 Å分辨率实验密度图的引导下，成功完成了HIV-1的C端结构域（CTD）的结构精修。