Fine-grained alignment of cryo-electron subtomograms based on MPI parallel optimization
Fine-grained alignment of cryo-electron subtomograms based on MPI parallel optimization
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DOI：
10.1186/s12859-019-3003-2
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发表时间：
2019-08-28
影响因子：
3
通讯作者：
Xu, Min
中科院分区：
文献类型：
--
作者：
Lu, Yongchun;Zeng, Xiangrui;Xu, Min
背景冷冻电子断层扫描（Cryo-ET）是一种成像技术，用于在天然环境中生成细胞大分子复合物的三维结构。由于冷冻电镜技术的发展，冷冻电子断层扫描三维重建的图像质量得到了很大的提高。然而，冷冻电子断层扫描图像具有分辨率低、部分数据丢失和信噪比(SNR)低的特点。为了应对这些挑战并提高分辨率，需要对包含相同结构的大量子断层图进行对齐和平均。现有的细化和对齐子断层图的方法仍然非常耗时，需要许多计算密集型处理步骤（即三维空间中子断层图的旋转和平移）。结果在本文中，我们提出了一种随机平均梯度（SAG）细粒度对齐方法，用于优化真实空间中的相异度量之和。我们引入了消息传递接口（MPI）并行编程模型，以探索进一步的加速。结论我们将随机平均梯度细粒度对齐算法与两种基线方法（高精度对齐和快速对齐）进行了比较。我们的 SAG 细粒度对齐算法比两种基线方法快得多。来自蛋白质数据库（PDB ID：1KP8）的GroEL模拟数据结果表明，我们的基于并行SAG的细粒度对准方法可以实现接近最优的刚性变换，其精度高于低SNR（SNR = 0.003）下的高精度对准和快速对准，倾斜角度范围为+/- 60度或+/- 40度。对于 GroEL 和 GroEL/GroES 复合体的实验亚断层图数据结构，我们的基于并行 SAG 的细粒度对齐可以比两种基线方法实现更高的精度和更少的迭代收敛。
BackgroundCryo-electron tomography (Cryo-ET) is an imaging technique used to generate three-dimensional structures of cellular macromolecule complexes in their native environment. Due to developing cryo-electron microscopy technology, the image quality of three-dimensional reconstruction of cryo-electron tomography has greatly improved.However, cryo-ET images are characterized by low resolution, partial data loss and low signal-to-noise ratio (SNR). In order to tackle these challenges and improve resolution, a large number of subtomograms containing the same structure needs to be aligned and averaged. Existing methods for refining and aligning subtomograms are still highly time-consuming, requiring many computationally intensive processing steps (i.e. the rotations and translations of subtomograms in three-dimensional space).ResultsIn this article, we propose a Stochastic Average Gradient (SAG) fine-grained alignment method for optimizing the sum of dissimilarity measure in real space. We introduce a Message Passing Interface (MPI) parallel programming model in order to explore further speedup.ConclusionsWe compare our stochastic average gradient fine-grained alignment algorithm with two baseline methods, high-precision alignment and fast alignment. Our SAG fine-grained alignment algorithm is much faster than the two baseline methods. Results on simulated data of GroEL from the Protein Data Bank (PDB ID:1KP8) showed that our parallel SAG-based fine-grained alignment method could achieve close-to-optimal rigid transformations with higher precision than both high-precision alignment and fast alignment at a low SNR (SNR=0.003) with tilt angle range +/- 60 degrees or +/- 40 degrees. For the experimental subtomograms data structures of GroEL and GroEL/GroES complexes, our parallel SAG-based fine-grained alignment can achieve higher precision and fewer iterations to converge than the two baseline methods.