ncku-talk2021-10-14

Colloquium

DATE 2021-10-14　16:10-17:00

PLACE 數學館3174教室

SPEAKER 鍾思齊助理教授（國立中山大學應用數學系）

TITLE Discovering the Dynamics - Grouping 3D Structure Conformations Using Network Analysis on 2D Cryogenic Electron Microscopy (Cryo-EM) Projection Images

ABSTRACT Recently, computational and learning methods are becoming popular to determine the 3D structure of a protein. The most remarkable achievement is probably the recent release of AlphaFold 2, which can perform high accuracy structure prediction from chains of amino acid sequences. However, the these methods are mainly used to predict a static structure. To understand the function of the protein, scientists need several dynamic movies that describe the conformational changes instead of a single snapshot. Therefore, describing the motions of a protein and determining the discrete or continuous conformations of the target protein is still a challenging problem. Among the tools available, cryo-EM is a promising computational technique with high efficiency that can perform conformation analysis. However, the data characteristics include heavy noise, huge dimension, a large number of unlabeled samples (no clean target is available for training) with unknown orientations, making it very challenging to reach a robust computation conclusion for heterogeneity analysis. Traditional approaches address this problem at 3D level, which is computationally expensive and may not be applicable to datasets whose conformations differ a lot. Therefore, there is a need to develop a new algorithm that preserves accuracy while solving the scalability issue due to the fast-growing data acquisition rate. In this talk, I will first introduce the importance of 3D structure determination of protein as well as the related background of cryo-EM image processing. Second, I will elaborate our approach to the heterogeneity problem, which utilizes the network analysis to partition the dataset into several homogeneous communities. Specifically, I will discuss several novel criteria that we used to measure the conformation similarity between cryo-EM images. Finally, I will discuss some on-going directions in 3D conformation analysis.

Colloquium

DATE	2021-10-14　16:10-17:00

PLACE	數學館3174教室

SPEAKER	鍾思齊助理教授（國立中山大學應用數學系）

TITLE	Discovering the Dynamics - Grouping 3D Structure Conformations Using Network Analysis on 2D Cryogenic Electron Microscopy (Cryo-EM) Projection Images

ABSTRACT	Recently, computational and learning methods are becoming popular to determine the 3D structure of a protein. The most remarkable achievement is probably the recent release of AlphaFold 2, which can perform high accuracy structure prediction from chains of amino acid sequences. However, the these methods are mainly used to predict a static structure. To understand the function of the protein, scientists need several dynamic movies that describe the conformational changes instead of a single snapshot. Therefore, describing the motions of a protein and determining the discrete or continuous conformations of the target protein is still a challenging problem. Among the tools available, cryo-EM is a promising computational technique with high efficiency that can perform conformation analysis. However, the data characteristics include heavy noise, huge dimension, a large number of unlabeled samples (no clean target is available for training) with unknown orientations, making it very challenging to reach a robust computation conclusion for heterogeneity analysis. Traditional approaches address this problem at 3D level, which is computationally expensive and may not be applicable to datasets whose conformations differ a lot. Therefore, there is a need to develop a new algorithm that preserves accuracy while solving the scalability issue due to the fast-growing data acquisition rate. In this talk, I will first introduce the importance of 3D structure determination of protein as well as the related background of cryo-EM image processing. Second, I will elaborate our approach to the heterogeneity problem, which utilizes the network analysis to partition the dataset into several homogeneous communities. Specifically, I will discuss several novel criteria that we used to measure the conformation similarity between cryo-EM images. Finally, I will discuss some on-going directions in 3D conformation analysis.