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J Chem Inf Model. 2019 Apr 22;59(4):1658-1667. doi: 10.1021/acs.jcim.8b00853. Epub 2019 Feb 19.

Clustering Enhancement of Noisy Cryo-Electron Microscopy Single-Particle Images with a Network Structural Similarity Metric.

Author information

1
Institute of Image Processing and Pattern Recognition , Shanghai Jiao Tong University , and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240 , China.
2
Department of Computer Science , Shanghai Jiao Tong University , and Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Shanghai 200240 , China.
3
State Key Laboratory of Infrared Physics Shanghai Institute of Technical Physics , Chinese Academy of Sciences , 500 Yutian Road , Shanghai 200083 , China.

Abstract

The reconstruction of a three-dimensional model from cryo-electron microscopy (cryo-EM) two-dimensional images is currently a mainstream technology for revealing the structure of biomacromolecules. In this structure solution protocol, an important step is to identify each particle's projection orientation. Because the obtained single-particle images are often too noisy, clustering is an important step to mitigate noise by averaging images within the same class. The core of clustering is to place similar cryo-EM images into the same class; hence, measurement of similarity between data samples is an essential element in any clustering algorithm. As the cryo-EM images are highly noisy, directly measuring the similarity of two images will be easily biased by the hidden noise. In this study, we propose a new network structural similarity metric-based clustering protocol NCEM for clustering the noisy cryo-EM images. We first construct an image complex network for all of the cryo-EM single-particle images, where each image is represented as a node in the network. Then the similarity between two images is refined from the network structural geometry. By extending the similarity measurement from two independent images to their corresponding neighboring sets in the network, this new NCEM has typical advantages over direct measurement of two images for its noise resistance by using the structural information on the network. Our experimental results for both synthetic and real data sets demonstrate the efficacy of the protocol.

PMID:
30676727
DOI:
10.1021/acs.jcim.8b00853

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