ArticleBioinformatics (Oxford, England)2011
Template-free detection of macromolecular complexes in cryo electron tomograms.
Article in Bioinformatics (Oxford, England), 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Deep-learning methods for contrast enhancement and artifact reduction in cryo-electron tomography: a systematic analysis of the state of the art and proposed improvements.Acta crystallographica. Section D, Structural biology · 2026Article
- Convolutional networks for supervised mining of molecular patterns within cellular context.Nature methods · 2023Article
- Quantitative Cryo-Electron Tomography.Frontiers in molecular biosciences · 2022Review
- Assessment of scoring functions to rank the quality of 3D subtomogram clusters from cryo-electron tomography.Journal of structural biology · 2021Article
- Detecting Protein Communities in Native Cell Extracts by Machine Learning: A Structural Biologist's Perspective.Frontiers in molecular biosciences · 2021Article
- Template-free detection and classification of membrane-bound complexes in cryo-electron tomograms.Nature methods · 2020Article
- Fine-grained alignment of cryo-electron subtomograms based on MPI parallel optimization.BMC bioinformatics · 2019Article
- De Novo Structural Pattern Mining in Cellular Electron Cryotomograms.Structure (London, England : 1993) · 2019Article
- Improved deep learning-based macromolecules structure classification from electron cryo-tomograms.Machine vision and applications · 2018Article
- From Atoms to Cells: Using Mesoscale Landscapes to Construct Visual Narratives.Journal of molecular biology · 2018Review
- A convolutional autoencoder approach for mining features in cellular electron cryo-tomograms and weakly supervised coarse segmentation.Journal of structural biology · 2018Article
- Review
- Deep learning-based subdivision approach for large scale macromolecules structure recovery from electron cryo tomograms.Bioinformatics (Oxford, England) · 2017Article
- TomoMiner and TomoMinerCloud: A Software Platform for Large-Scale Subtomogram Structural Analysis.Structure (London, England : 1993) · 2017Article
- Simulating cryo electron tomograms of crowded cell cytoplasm for assessment of automated particle picking.BMC bioinformatics · 2016Article
- Navigating 3D electron microscopy maps with EM-SURFER.BMC bioinformatics · 2015Article
- Automated target segmentation and real space fast alignment methods for high-throughput classification and averaging of crowded cryo-electron subtomograms.Bioinformatics (Oxford, England) · 2013Article
- High-throughput subtomogram alignment and classification by Fourier space constrained fast volumetric matching.Journal of structural biology · 2012Article
- Putting structure into context: fitting of atomic models into electron microscopic and electron tomographic reconstructions.Current opinion in cell biology · 2012Review
- High precision alignment of cryo-electron subtomograms through gradient-based parallel optimization.BMC systems biology · 2012Article
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3 authors.
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Abstract
motivationCryo electron tomography (CryoET) produces 3D density maps of biological specimen in its near native states. Applied to small cells, cryoET produces 3D snapshots of the cellular distributions of large complexes. However, retrieving this information is non-trivial due to the low resolution and low signal-to-noise ratio in tomograms. Current pattern recognition methods identify complexes by matching known structures to the cryo electron tomogram. However, so far only a small fraction of all protein complexes have been structurally resolved. It is, therefore, of great importance to develop template-free methods for the discovery of previously unknown protein complexes in cryo electron tomograms.
resultsHere, we have developed an inference method for the template-free discovery of frequently occurring protein complexes in cryo electron tomograms. We provide a first proof-of-principle of the approach and assess its applicability using realistically simulated tomograms, allowing for the inclusion of noise and distortions due to missing wedge and electron optical factors. Our method is a step toward the template-free discovery of the shapes, abundance and spatial distributions of previously unknown macromolecular complexes in whole cell tomograms. CONTACT: alber@usc.edu
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