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

Yongchun Lü,Xiangrui Zeng,Min Xu,Xin Gao,Hua Li,Shirui Li,Xiaofang Zhao

doi:10.1186/s12859-019-3003-2

Abstract

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∘ 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.

Highlights

Cryo-electron tomography (Cryo-ET) is an imaging technique used to generate three-dimensional structures of cellular macromolecule complexes in their native environment
The results show that stochastic average gradient (SAG)-based fine-grained alignment method can achieve higher alignment precision and better averaging of subtomograms at a low signal-to-noise ratio (SNR) of 0.003 with tilt angle range from +60° to -60° and from +40° to -40°, as compared to baseline methods
We design a three-dimensional fine-grained alignment framework for subtomogram alignment based on stochastic average gradient [22], which minimizes the dissimilarity score defined by the Euclidean distance between a function with fixed parameters and a function with optimized parameters

Summary

Introduction

Cryo-electron tomography (Cryo-ET) is an imaging technique used to generate three-dimensional structures of cellular macromolecule complexes in their native environment. In order to tackle these challenges and improve resolution, a large number of subtomograms containing the same structure needs to be aligned and averaged. Subtomogram alignment aims to rotate and translate a subtomogram to minimize its dissimilarity measure with a reference structure. In the iteration procedure of optimizing subtomogram averaging, each subtomogram is rotated and translated in different ways but with the same reference structure. For averaging N subtomograms, the parameter search space is 6N−1 dimensional. Fast translationinvariant rotational matching that obtains better rotational parameter candidate sets using spherical harmonics functions in Fourier space [16] has been proposed [17, 18] and extended to subtomogram alignment [9, 10, 19, 20]

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Results

Discussion

Conclusion