| Title | Robustness of signal detection in cryo-electron microscopy via a bi-objective-function approach |
| Authors | Wang, Wei Li Yu, Zhou Castillo-Menendez, Luis R. Sodroski, Joseph Mao, Youdong |
| Affiliation | Dana Farber Canc Inst, Intel Parallel Comp Ctr Struct Biol, Boston, MA 02215 USA Harvard Med Sch, Dana Farber Canc Inst, Dept Canc Immunol & Virol, Dept Microbiol, Boston, MA 02115 USA Peking Univ, Sch Phys, Ctr Quantitat Biol, State Key Lab Artificial Microstruct & Mesoscop P, Beijing 100871, Peoples R China Harvard Univ, Grad Sch Arts & Sci, Dept Cellular & Mol Biol, Cambridge, MA 02138 USA Harvard TH Chan Sch Publ Hlth, Dept Immunol & Infect Dis, Boston, MA 02115 USA |
| Keywords | Automatic particle picking Fast local correlation function Cryo-EM Maximum-likelihood estimate Single-particle analysis |
| Issue Date | 2019 |
| Publisher | BMC BIOINFORMATICS |
| Abstract | BackgroundThe 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.ResultsThe 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.ConclusionThe 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. |
| URI | http://hdl.handle.net/20.500.11897/549221 |
| ISSN | 1471-2105 |
| DOI | 10.1186/s12859-019-2714-8 |
| Indexed | SCI(E) EI |
| Appears in Collections: | 物理学院 人工微结构和介观物理国家重点实验室 |
