Background: An important task of macromolecular structure determination by cryo-electron microscopy (cryo-EM) is the identification of single particles in micrographs (particle picking). Due to the necessity of human involvement in the process, current particle picking techniques are time consuming and often result in many false positives and negatives. Adjusting the parameters to eliminate false positives often excludes true particles in certain orientations. The supervised machine learning (e.g. deep learning) methods for particle picking often need a large training dataset, which requires extensive manual annotation. Other reference-dependent methods rely on low-resolution templates for particle detection, matching and picking, and therefore, are not fully automated. These issues motivate us to develop a fully automated, unbiased framework for particle picking.
Results: We design a fully automated, unsupervised approach for single particle picking in cryo-EM micrographs. Our approach consists of three stages: image preprocessing, particle clustering, and particle picking. The image preprocessing is based on multiple techniques including: image averaging, normalization, cryo-EM image contrast enhancement correction (CEC), histogram equalization, restoration, adaptive histogram equalization, guided image filtering, and morphological operations. Image preprocessing significantly improves the quality of original cryo-EM images. Our particle clustering method is based on an intensity distribution model which is much faster and more accurate than traditional K-means and Fuzzy C-Means (FCM) algorithms for single particle clustering. Our particle picking method, based on image cleaning and shape detection with a modified Circular Hough Transform algorithm, effectively detects the shape and the center of each particle and creates a bounding box encapsulating the particles.
Conclusions: AutoCryoPicker can automatically and effectively recognize particle-like objects from noisy cryo-EM micrographs without the need of labeled training data or human intervention making it a useful tool for cryo-EM protein structure determination.
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http://dx.doi.org/10.1186/s12859-019-2926-y | DOI Listing |
Brief Bioinform
November 2024
Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, United States.
Cryo-electron microscopy (cryo-EM) has revolutionized structural biology by enabling the determination of high-resolution 3-Dimensional (3D) structures of large biological macromolecules. Protein particle picking, the process of identifying individual protein particles in cryo-EM micrographs for building protein structures, has progressed from manual and template-based methods to sophisticated artificial intelligence (AI)-driven approaches in recent years. This review critically examines the evolution and current state of cryo-EM particle picking methods, with an emphasis on the impact of AI.
View Article and Find Full Text PDFNat Commun
January 2025
Key Laboratory for Protein Sciences of Ministry of Education, School of Life Sciences, Tsinghua University, Beijing, China.
Advancements in cryo-electron tomography (cryoET) allow the structure of macromolecules to be determined in situ, which is crucial for studying membrane protein structures and their interactions in the cellular environment. However, membranes are often highly curved and have a strong contrast in cryoET tomograms, which masks the signals from membrane proteins. These factors pose difficulties in observing and revealing the structures of membrane proteins in situ.
View Article and Find Full Text PDFElife
December 2024
Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, Netherlands.
Segmentation is a critical data processing step in many applications of cryo-electron tomography. Downstream analyses, such as subtomogram averaging, are often based on segmentation results, and are thus critically dependent on the availability of open-source software for accurate as well as high-throughput tomogram segmentation. There is a need for more user-friendly, flexible, and comprehensive segmentation software that offers an insightful overview of all steps involved in preparing automated segmentations.
View Article and Find Full Text PDFBrief Bioinform
November 2024
State Key Laboratory of CAD&CG, Zhejiang University, Hangzhou 310058, Zhejiang, China.
Automatic single particle picking is a critical step in the data processing pipeline of cryo-electron microscopy structure reconstruction. In recent years, several deep learning-based algorithms have been developed, demonstrating their potential to solve this challenge. However, current methods highly depend on manually labeled training data, which is labor-intensive and prone to biases especially for high-noise and low-contrast micrographs, resulting in suboptimal precision and recall.
View Article and Find Full Text PDFVaccine
January 2025
Cancer ImmunoPrevention Laboratory, Frederick National Laboratory for Cancer Research, Frederick, MD, USA. Electronic address:
The TLR4 (Toll-like receptor 4)-activating agonist MPLA (monophosphoryl lipid A) is a key component of the adjuvant systems AS01 and AS04, utilized in marketed preventive vaccines for several infectious pathogens. As MPLA is a biologically-derived product containing a mixture of several lipid A congeners with a 4' phosphoryl group and varying numbers of acyl chains with distinct activities, extensive efforts to refine its production and immunogenicity are ongoing; notably, the development of the BECC (Bacterial Enzymatic Combinatorial Chemistry) system in which bacteria express lipid A-modifying enzymes to produce a panoply of lipid A congeners. In an effort to characterize the adjuvant activity of these lipid A congeners, we compared biologically-derived and synthetic versions of BECC470 and BECC438 for adjuvant activity in BALB/c mice vaccinated with the HPV (Human papilloma virus) VLP-based vaccine, RG1-VLP.
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