Cryo-Ralib - A Modular Library For Accelerating Alignment In Cryo-Em

Thanks to GPU-accelerated processing, 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 the emerging pandemic. Compared to the Bayesian-based 2D classification widely used in the workflow, the multi-reference alignment (MRA) is less popular. It is time-consuming despite its superior in differentiating structural variations. Interestingly, the Bayesian approach has higher complexity than MRA. We thereby reason that the popularity of Bayesian is gained through GPU acceleration, where a modular 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 to make it easier for users to perform data analysis and visualization. Benchmarking on the TaiWan Computing Cloud (TWCC) shows that our implementation can accelerate the computation by one order of magnitude. The library is available at https://github.com/phonchi/Cryo-RAlib.