Li Changrui, Nguyen Thu, Wriggers Willy, He Jing
Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA.
Department of Mechanical and Aerospace Engineering, Old Dominion University, Norfolk, VA 23529, USA.
Comput Struct Bioinform (2024). 2025;2396:71-83. doi: 10.1007/978-3-031-85435-4_6. Epub 2025 Mar 26.
AlphaFold 3 (AF3) has recently been shown to offer improved accuracy in predicting the structures of protein multimers. Improved models may lead to new opportunities for fitting them to cryo-electron microscopy (cryo-EM) maps with medium resolution (5-10 Å). Deriving atomic models from such cryo-EM maps is still challenging due to the lack of high-resolution features. Our case study involving four AF3 multimer models and corresponding cryo-EM maps with 7-8 Å resolution showed that the predicted multimer models were partially correct. The predicted models contained fairly accurate domains, secondary structures, and individual chains, since 9 of the 17 chains exhibit TM-scores higher than 0.8 and 16 chains had TM-scores above 0.5 compared with the official atomic structures that were deposited with the cryo-EM maps. However, some cases exhibited incorrect relative positions of individual chains or domains. We observed that the order of cross-correlation (CC) scores between the multimers and their corresponding cryo-EM maps aligned with the order of the TM-scores. This shows that if regions are masked correctly, CC scores are sensitive enough to distinguish among the multimer models. A masking of monomeric chains may not always be attainable, so we also explored the level of accuracy in secondary structure segmentation for one of the cases in greater detail. Although molecular details are not fully visible in cryo-EM maps at medium resolution, the location of major secondary structures, such as α-helices and β-sheets, were detectable using our DeepSSETracer tool. Our analysis illustrates the potential for improvements in the accuracy of AF3-predicted multimer models by combining the density map-model similarity (CC scores) and the secondary structure map-model similarity in a future approach.
最近研究表明,AlphaFold 3(AF3)在预测蛋白质多聚体结构方面具有更高的准确性。改进后的模型可能为将其与中等分辨率(5-10埃)的冷冻电子显微镜(cryo-EM)图谱进行拟合带来新机遇。由于缺乏高分辨率特征,从这种冷冻电镜图谱中推导原子模型仍然具有挑战性。我们对四个AF3多聚体模型以及对应的分辨率为7-8埃的冷冻电镜图谱进行的案例研究表明,预测的多聚体模型部分正确。预测模型包含相当准确的结构域、二级结构和单条链,因为与随冷冻电镜图谱一起存入的官方原子结构相比,17条链中有9条的TM分数高于0.8,16条链的TM分数高于0.5。然而,在一些情况下,单条链或结构域的相对位置出现错误。我们观察到多聚体与其对应的冷冻电镜图谱之间的互相关(CC)分数顺序与TM分数顺序一致。这表明,如果区域被正确屏蔽,CC分数足够灵敏,能够区分多聚体模型。单体链的屏蔽并非总是可行,因此我们还更详细地探讨了其中一个案例的二级结构分割的准确程度。尽管在中等分辨率的冷冻电镜图谱中分子细节并不完全可见,但使用我们的DeepSSETracer工具可以检测到主要二级结构(如α螺旋和β折叠)的位置。我们的分析说明了在未来的方法中,通过结合密度图-模型相似性(CC分数)和二级结构图-模型相似性来提高AF3预测的多聚体模型准确性的潜力。