Alshammari Maytha, Wriggers Willy, Sun Jiangwen, He Jing
Department of Computer Science, Old Dominion University, Norfolk, VA, USA.
Department of Mechanical and Aerospace Engineering, Old Dominion University, Norfolk, VA, USA.
QRB Discov. 2022;3. doi: 10.1017/qrd.2022.13. Epub 2022 Sep 20.
Recent breakthroughs in deep learning-based protein structure prediction show that it is possible to obtain highly accurate models for a wide range of difficult protein targets for which only the amino acid sequence is known. The availability of accurately predicted models from sequences can potentially revolutionise many modelling approaches in structural biology, including the interpretation of cryo-EM density maps. Although atomic structures can be readily solved from cryo-EM maps of better than 4 Å resolution, it is still challenging to determine accurate models from lower-resolution density maps. Here, we report on the benefits of models predicted by AlphaFold2 (the best-performing structure prediction method at CASP14) on cryo-EM refinement using the Phenix refinement suite for AlphaFold2 models. To study the robustness of model refinement at a lower resolution of interest, we introduced hybrid maps (i.e. experimental cryo-EM maps) filtered to lower resolutions by real-space convolution. The AlphaFold2 models were refined to attain good accuracies above 0.8 TM scores for 9 of the 13 cryo-EM maps. TM scores improved for AlphaFold2 models refined against all 13 cryo-EM maps of better than 4.5 Å resolution, 8 hybrid maps of 6 Å resolution, and 3 hybrid maps of 8 Å resolution. The results show that it is possible (at least with the Phenix protocol) to extend the refinement success below 4.5 Å resolution. We even found isolated cases in which resolution lowering was slightly beneficial for refinement, suggesting that high-resolution cryo-EM maps might sometimes trap AlphaFold2 models in local optima.
基于深度学习的蛋白质结构预测的最新突破表明，对于仅知道氨基酸序列的广泛困难蛋白质靶点，有可能获得高度准确的模型。从序列中获得准确预测模型的可能性可能会彻底改变结构生物学中的许多建模方法，包括对冷冻电镜密度图的解释。尽管原子结构可以从分辨率优于4 Å的冷冻电镜图中轻松解析出来，但从较低分辨率的密度图中确定准确模型仍然具有挑战性。在这里，我们报告了AlphaFold2（CASP14中表现最佳的结构预测方法）预测的模型在使用Phenix精修套件对AlphaFold2模型进行冷冻电镜精修方面的优势。为了研究在较低感兴趣分辨率下模型精修的稳健性，我们引入了通过实空间卷积过滤到较低分辨率的混合图（即实验冷冻电镜图）。对于13个冷冻电镜图中的9个，AlphaFold2模型经过精修后获得了高于0.8 TM分数的良好准确性。针对所有分辨率优于4.5 Å的13个冷冻电镜图、6 Å分辨率的8个混合图和8 Å分辨率的3个混合图进行精修的AlphaFold2模型，其TM分数有所提高。结果表明，（至少使用Phenix协议）有可能将精修成功扩展到分辨率低于4.5 Å的情况。我们甚至发现了个别案例，其中分辨率降低对精修略有好处，这表明高分辨率冷冻电镜图有时可能会将AlphaFold2模型困在局部最优解中。
