Cryo-RALib -- a modular library for accelerating alignment in cryo-EM

TL;DR

Cryo-RALib is a GPU-accelerated modular library that significantly speeds up multi-reference alignment in cryo-EM image processing, facilitating rapid structural analysis crucial for vaccine development.

Contribution

The paper introduces Cryo-RALib, a GPU-accelerated library that enhances MRA-based cryo-EM classification, integrating with Python for easier analysis and visualization.

Findings

Achieves tenfold acceleration in MRA computations

Enables faster cryo-EM data analysis for structural biology

Integrates with Python data science tools

Abstract

Thanks to automated cryo-EM and GPU-accelerated processing, single-particle cryo-EM has become a rapid structure determination method that permits capture of dynamical structures of molecules in solution, which has been recently demonstrated by the determination of COVID-19 spike protein in March, shortly after its breakout in late January 2020. This rapidity is critical for vaccine development in response to emerging pandemic. This explains why a 2D classification approach based on multi-reference alignment (MRA) is not as popular as the Bayesian-based approach despite that the former has advantage in differentiating structural variations under low signal-to-noise ratio. This is perhaps because that MRA is a time-consuming process and a modular GPU-acceleration library for MRA is lacking. Here, we introduce a library called Cryo-RALib that expands the functionality of CUDA library used by GPU ISAC. It contains a GPU-accelerated MRA routine for accelerating MRA-based classification algorithms. In addition, we connect the cryo-EM image analysis with the python data science stack so as to make it easier for users to perform data analysis and visualization. Benchmarking on the TaiWan Computing Cloud (TWCC) container shows that our implementation can accelerate the computation by one order of magnitude. The library is available at https://github.com/phonchi/Cryo-RAlib.