ArticleFrontiers in bioinformatics2021
A Tool for Segmentation of Secondary Structures in 3D Cryo-EM Density Map Components Using Deep Convolutional Neural Networks.
Article in Frontiers in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 22 citations in OpenAlex.
- A Novel Machine-Learning Based Method for Resolving Secondary Structure Topology in Medium-Resolution Cryo-EM Density Maps.International journal of molecular sciences · 2026Article
- Automated model-free analysis of cryo-EM volume ensembles with SIREn.Structure (London, England : 1993) · 2025Article
- Toward Modeling Protein Multimers by Combining AlphaFold 3 Predictions with Secondary Structures from Medium-Resolution Cryo-EM Maps.Computational structural bioinformatics : international workshop, CSBW 2024, Boston, MA, USA, November 16, 2024, proceedings. Computational Structural Bioinformatics Workshop (2024 : Boston, Mass.) · 2025Article
- Connecting the dots: deep learning-based automated model building methods in cryo-EM.Frontiers in molecular biosciences · 2025Review
- DeepSSETracer 2.0: Improved Deep Learning Model Performance for Protein Secondary Structure Segmentation from Cryo-EM Maps.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2025Article
- Evaluating Deep Learning Architectures for Actin Filament Segmentation under Varying Noise Conditions in Simulated Cryo-ET Tomograms.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2025Article
- Visualizing the virus world inside the cell by cryo-electron tomography.Journal of virology · 2024Review
- Automated model-free analysis of cryo-EM volume ensembles with SIREn.bioRxiv : the preprint server for biology · 2024Article
- Determining Protein Secondary Structures in Heterogeneous Medium-Resolution Cryo-EM Images Using CryoSSESeg.ACS omega · 2024Article
- The combined focal loss and dice loss function improves the segmentation of beta-sheets in medium-resolution cryo-electron-microscopy density maps.Bioinformatics advances · 2024Article
- Untangling Irregular Actin Cytoskeleton Architectures in Tomograms of the Cell withInternational journal of molecular sciences · 2023Article
- An Approach to Developing Benchmark Datasets for Protein Secondary Structure Segmentation from Cryo-EM Density Maps.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2023Article
- The Combined Focal Cross Entropy and Dice Loss Function for Segmentation of Protein Secondary Structures from Cryo-EM 3D Density maps.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2022Article
- Spaghetti Tracer: A Framework for Tracing Semiregular Filamentous Densities in 3D Tomograms.Biomolecules · 2022Article
- Refinement of AlphaFold2 models against experimental and hybrid cryo-EM density maps.QRB discovery · 2022Article
- Tracing Filaments in Simulated 3D Cryo-Electron Tomography Maps Using a Fast Dynamic Programming Algorithm.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2021Article
- Combining Cryo-EM Density Map and Residue Contact for Protein Secondary Structure Topologies.Molecules (Basel, Switzerland) · 2021Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Although cryo-electron microscopy (cryo-EM) has been successfully used to derive atomic structures for many proteins, it is still challenging to derive atomic structures when the resolution of cryo-EM density maps is in the medium resolution range, such as 5-10 Å. Detection of protein secondary structures, such as helices and β-sheets, from cryo-EM density maps provides constraints for deriving atomic structures from such maps. As more deep learning methodologies are being developed for solving various molecular problems, effective tools are needed for users to access them. We have developed an effective software bundle, DeepSSETracer, for the detection of protein secondary structure from cryo-EM component maps in medium resolution. The bundle contains the network architecture and a U-Net model trained with a curriculum and gradient of episodic memory (GEM). The bundle integrates the deep neural network with the visualization capacity provided in ChimeraX. Using a Linux server that is remotely accessed by Windows users, it takes about 6 s on one CPU and one GPU for the trained deep neural network to detect secondary structures in a cryo-EM component map containing 446 amino acids. A test using 28 chain components of cryo-EM maps shows overall residue-level F1 scores of 0.72 and 0.65 to detect helices and β-sheets, respectively. Although deep learning applications are built on software frameworks, such as PyTorch and Tensorflow, our pioneer work here shows that integration of deep learning applications with ChimeraX is a promising and effective approach. Our experiments show that the F1 score measured at the residue level is an effective evaluation of secondary structure detection for individual classes. The test using 28 cryo-EM component maps shows that DeepSSETracer detects β-sheets more accurately than Emap2sec+, with a weighted average residue-level F1 score of 0.65 and 0.42, respectively. It also shows that Emap2sec+ detects helices more accurately than DeepSSETracer with a weighted average residue-level F1 score of 0.77 and 0.72 respectively.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.