ArticleIEEE/ACM transactions on computational biology and bioinformatics
An Effective Computational Method Incorporating Multiple Secondary Structure Predictions in Topology Determination for Cryo-EM Images.
Article in IEEE/ACM transactions on computational biology and bioinformatics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
- Position: Topological Deep Learning is the New Frontier for Relational Learning.Proceedings of machine learning research · 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
- 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
- LPTD: a novel linear programming-based topology determination method for cryo-EM maps.Bioinformatics (Oxford, England) · 2022Article
- Three-Dimensional Graph Matching to Identify Secondary Structure Correspondence of Medium-Resolution Cryo-EM Density Maps.Biomolecules · 2021Article
- Combining Cryo-EM Density Map and Residue Contact for Protein Secondary Structure Topologies.Molecules (Basel, Switzerland) · 2021Article
- A Tool for Segmentation of Secondary Structures in 3D Cryo-EM Density Map Components Using Deep Convolutional Neural Networks.Frontiers in bioinformatics · 2021Article
- Combine Cryo-EM Density Map and Residue Contact for Protein Structure Prediction - A Case Study.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2020Article
- Analytical Approaches to Improve Accuracy in Solving the Protein Topology Problem.Molecules (Basel, Switzerland) · 2018Article
- Analysis of β-strand Twist from the 3-dimensional Image of a Protein.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2017Article
- Modeling Beta-Traces for Beta-Barrels from Cryo-EM Density Maps.BioMed research international · 2017Article
- An Iterative Bézier Method for Fitting Beta-sheet Component of a Cryo-EM Density Map.Molecular based mathematical biology · 2017Article
- Comparing an Atomic Model or Structure to a Corresponding Cryo-electron Microscopy Image at the Central Axis of a Helix.Journal of computational biology : a journal of computational molecular cell biology · 2017Article
- Deep Convolutional Neural Networks for Detecting Secondary Structures in Protein Density Maps from Cryo-Electron Microscopy.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2016Article
- CHALLENGES IN MATCHING SECONDARY STRUCTURES IN CRYO-EM: AN EXPLORATION.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2016Article
- Constrained Cyclic Coordinate Descent for Cryo-EM Images at Medium Resolutions: Beyond the Protein Loop Closure Problem.Robotica · 2016Article
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6 authors.
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Abstract
A key idea in de novo modeling of a medium-resolution density image obtained from cryo-electron microscopy is to compute the optimal mapping between the secondary structure traces observed in the density image and those predicted on the protein sequence. When secondary structures are not determined precisely, either from the image or from the amino acid sequence of the protein, the computational problem becomes more complex. We present an efficient method that addresses the secondary structure placement problem in presence of multiple secondary structure predictions and computes the optimal mapping. We tested the method using 12 simulated images from α-proteins and two Cryo-EM images of α-β proteins. We observed that the rank of the true topologies is consistently improved by using multiple secondary structure predictions instead of a single prediction. The results show that the algorithm is robust and works well even when errors/misses in the predicted secondary structures are present in the image or the sequence. The results also show that the algorithm is efficient and is able to handle proteins with as many as 33 helices.
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