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

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

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


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.
Original languageEnglish (US)
JournalBMC bioinformatics
Issue number1
StatePublished - Aug 28 2019

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): URF/1/2602-01, URF/1/3007-01
Acknowledgements: We thank Prof. Xingwu Liu for fruitful discussions and suggestions. We thank Dr. Yuxiang Chen and Dr. Haiyang Li for technical assistance. We thank Dr. Friedrich Förster for sharing the GroEL subtomograms for subtomogram alignment and averaging test. We thank Alex Singh for revising the English writing.


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