Abstract

As the structure prediction of 3D protein becomes mature after the release of AlphaFold2 from DeepMind last December, the obvious next step is to predict the movement and flexibility of the underlying protein, which will provide crucial medical insight for the design of enzymes or drugs development. Among the tools available in structure biology, cryo-EM is a promising and powerful technique with high efficiency that can perform conformation analysis. Remarkably, the first dynamic movie that elucidates the functionality of the 2019-nCoV Spike trimer structure is derived within 12 days using computational methods from cryo-EM. However, the data characteristics of cryo-EM include strong noise, huge dimension, large sample size, and unknown orientations, have made analysis very challenging. In this talk, I will first introduce the importance of 3D structure determination of protein as well as the related background and principles of cryo-EM image processing. Second, I will discuss our proposed computational methods, including a two-stage dimension reduction (2SDR) and clustering approach called DRMRA, which alleviate the computation burden and improves performance over existing methods. Specifically, I will elaborate on how to use the 2SDR to improve the workflow and perform 3D conformation analysis. The generation of a dynamic movie beyond snapshot will also be discussed. Finally, I will discuss future directions in 3D conformation analysis.

Bio.

https://phonchi.github.io/

All faculty and students are welcome to join.