Deep Consensus, a deep learning-based approach for particle pruning in cryo-electron microscopy

IUCrJ

Sanchez-Garcia, R.¹ 

[1]Biocomputing Unit, Spanish National Center for Biotechnology, Calle Darwin 3, 28049 Madrid, Spain

关键词: CRYO-EM;    DEEP LEARNING;    IMAGE PROCESSING;    PARTICLE PRUNING;    THREE-DIMENSIONAL RECONSTRUCTION;   

DOI  :  10.1107/S2052252518014392

学科分类：数学（综合）

来源: International Union of Crystallography

【 摘 要 】

Single-particle cryo-electron microscopy (cryo-EM) has recently become a mainstream technique for the structural determination of macromolecules. Typical cryo-EM workflows collect hundreds of thousands of single-particle projections from thousands of micrographs using particle-picking algorithms. However, the number of false positives selected by these algorithms is large, so that a number of different `cleaning steps' are necessary to decrease the false-positive ratio. Most commonly employed techniques for the pruning of false-positive particles are time-consuming and require user intervention. In order to overcome these limitations, a deep learning-based algorithm named Deep Consensus is presented in this work. Deep Consensus works by computing a smart consensus over the output of different particle-picking algorithms, resulting in a set of particles with a lower false-positive ratio than the initial set obtained by the pickers. Deep Consensus is based on a deep convolutional neural network that is trained on a semi-automatically generated data set. The performance of Deep Consensus has been assessed on two well known experimental data sets, virtually eliminating user intervention for pruning, and enhances the reproducibility and objectivity of the whole process while achieving precision and recall figures above 90%.

【 授权许可】

CC BY