用多尺度方法从冷冻电镜图像重建蛋白质原子结构，精度更高。
Multiscale reconstruction of protein conformations from cryo-EM images
- 基于键、扭转角等显式表示蛋白骨架，引入丰富先验信息。
- 在低信噪比数据下，RMSD和TM分数优于现有方法。
- 适合需要高精度结构解析的生物医学研究者使用。
我们提出一种新型多尺度算法，可直接从单颗粒冷冻电镜数据中恢复蛋白质的原子模型结构。该算法在高噪声、低对比度数据下仍能实现当前最优的结构重构精度，并对透射电镜成像模型的误设具有鲁棒性。其优异表现主要源于采用键、扭转角和键角等显式方式表示蛋白骨架，为结构恢复提供了丰富的先验信息。我们在三个由电子显微镜数字孪生生成的蛋白质冷冻电镜数据集上应用该方法，结果表明，相较于真实结构，多尺度方法显著提升了均方根偏差（RMSD）和模板建模（TM）得分。此外，证据显示该算法更倾向于优先优化大尺度结构，从而降低了陷入劣质局部极小值的可能性。
原文摘要 · Abstract (English)
We present a novel multiscale algorithm for directly recovering the atomic model structure of a protein from single-particle cryo-EM data. Our algorithm is able to estimate protein structures to state-of-the-art accuracy for high-noise and low-contrast data. It is also robust to misspecifications in the TEM image formation model. These desirable properties are primarily due to the use of an explicit representation of the protein backbone in terms of bonds, torsion angles and bond angles, which supplies rich prior information to the structure recovery process. We apply our method on three protein cryo-EM datasets, generated using an electron microscope digital twin, and show that using a multiscale approach yields an improvement of the root-mean-square deviation (RMSD) and template modelling (TM) scores with respect to the ground truth. Furthermore, there is evidence that larger-scale structures are being prioritised with the multiscale algorithm, which reduces the possibility of convergence to bad local minima.
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