Fine-grained alignment of cryo-electron subtomograms based on MPI parallel optimization

Abstract

Background: Cryo-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). Results: In 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. Conclusions: We 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 • or ±40 •. 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.

One-Shot Learning With Attention-Guided Segmentation in Cryo-Electron Tomography

“…The macromolecule in the support set and target set may not be aligned, i.e., they have different orientations before feeding into our network, which could potentially decrease the classification accuracy. Subtomogram pre-processing by alignment of macromolecule in subtomograms could potentially further improve our classification accuracy and will be a focus in our future work (Lü et al, 2019 ; Zeng and Xu, 2020 ). Second, the cryo-ET imaging data is reconstructed from limited angle conditions.…”

Computational methods for in situ structural studies with cryogenic electron tomography

“…Because the MLE method needs to go through all pixels of 3D subtomograms, the computational cost and time of the MLE method are relatively high. For speeding up the calculation of the MCCF-based methods in real space, there are some fast aligning approaches based on common gradient ( Xu and Alber, 2012 ) and stochastic average gradient (SAG) ( Lü et al., 2019 ) by parallel optimization. The multi-reference alignment ( Zhao et al., 2022 ) is a simple mathematical model for particles alignment and averaging.…”

AITom: Open-source AI platform for cryo-electron tomography data analysis

“…Subtmogram alignment estimates the 3D rigid body geometric correspondence between a structural template and a subtomogram or a pair of subtomograms. AITom implements three fast subtomogram alignment methods [19,20,21]. Compared with exhaustive search based methods, fast subtomogram alignment methods apply heuristics to perform this computationally intensive task efficiently.…”