Robustness of signal detection in cryo-electron microscopy via a bi-objective-function approach

The detection of weak signals and selection of single particles from low-contrast micrographs of frozen hydrated biomolecules by cryo-electron microscopy (cryo-EM) represents a major practical bottleneck in cryo-EM data analysis. Template-based particle picking by an objective function using fast lo...

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Bibliographic Details
Published in:	BMC bioinformatics 2019-04, Vol.20 (1), p.169-169, Article 169
Main Authors:	Wang, Wei Li, Yu, Zhou, Castillo-Menendez, Luis R, Sodroski, Joseph, Mao, Youdong
Format:	Article
Language:	eng
Subjects:	Algorithms Analysis Automatic particle picking Automation Biochemistry Biomolecules Carbon Computer applications Computer simulation Cryo-EM Cryoelectron microscopy Cryoelectron Microscopy - methods Data analysis Data processing Dependence Electron microscopy Electrons Fast local correlation function Fourier transforms Glycoproteins Image Processing, Computer-Assisted Information management Likelihood Functions Mathematical models Maximum likelihood estimates Maximum-likelihood estimate Micrography Microscopy Noise Objective function Photomicrographs Robustness (mathematics) Signal detection Signal processing Signal-To-Noise Ratio Single-particle analysis Structural analysis Template matching Transmission electron microscopy Utilities
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Summary:	The detection of weak signals and selection of single particles from low-contrast micrographs of frozen hydrated biomolecules by cryo-electron microscopy (cryo-EM) represents a major practical bottleneck in cryo-EM data analysis. Template-based particle picking by an objective function using fast local correlation (FLC) allows computational extraction of a large number of candidate particles from micrographs. Another independent objective function based on maximum likelihood estimates (MLE) can be used to align the images and verify the presence of a signal in the selected particles. Despite the widespread applications of the two objective functions, an optimal combination of their utilities has not been exploited. Here we propose a bi-objective function (BOF) approach that combines both FLC and MLE and explore the potential advantages and limitations of BOF in signal detection from cryo-EM data. The robustness of the BOF strategy in particle selection and verification was systematically examined with both simulated and experimental cryo-EM data. We investigated how the performance of the BOF approach is quantitatively affected by the signal-to-noise ratio (SNR) of cryo-EM data and by the choice of initialization for FLC and MLE. We quantitatively pinpointed the critical SNR (~ 0.005), at which the BOF approach starts losing its ability to select and verify particles reliably. We found that the use of a Gaussian model to initialize the MLE suppresses the adverse effects of reference dependency in the FLC function used for template-matching. The BOF approach, which combines two distinct objective functions, provides a sensitive way to verify particles for downstream cryo-EM structure analysis. Importantly, reference dependency of the FLC does not necessarily transfer to the MLE, enabling the robust detection of weak signals. Our insights into the numerical behavior of the BOF approach can be used to improve automation efficiency in the cryo-EM data processing pipeline for high-resolution structural determination.
ISSN:	1471-2105 1471-2105