Orientations of beta-strand traces and near maximum twist

Si, Dong; He, Jing

doi:10.1145/2649387.2660835

Cited by 6 publications

(7 citation statements)

References 17 publications

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“…α-helices, β-sheet, and β-strands detected in a medium resolution cryo-TEM density map. (A) The density region of EMD-1780 (5.5Å resolution) corresponding to chain K of PDB ID 3IZ6 is superimposed with the helices (colored cylinders) and β-sheet (blue) detected by SSETracer [4]. (B) The atomic structure of PDB ID 3IZ6 chain K (ribbon) is superimposed with the detected helices (thick cylinders) and β-strand traces (thin sticks) using StrandTwister [5].…”

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confidence: 99%

“…At medium resolution it is important to know the location of the major components of a protein structure, such as α-helices and β-sheets, in the density map. Although the backbone of a protein structure is often not visible at medium resolution, secondary structures, such as α-helices and β-sheets, can be computationally identified [1][2][3][4]. In addition, the location of β-strands can be derived from the density region of a β-sheet once it is segmented out from the surrounding density [5].…”

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confidence: 99%

“…Although the backbone of a protein structure is often not visible at medium resolution, secondary structures, such as α-helices and β-sheets, can be computationally identified [1][2][3][4]. In addition, the location of β-strands can be derived from the density region of a β-sheet once it is segmented out from the surrounding density [5].Over the years, various pattern recognition methods have been developed for the identification of α-helices [1-4, 6, 7] and β-sheets [1,2,4,8]. More recently, machine learning techniques, such as Support Vector Machine (SVM) and Convolutional Neural Networks (CNN), have been applied to this problem [2,9].…”

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confidence: 99%

“…Over the years, various pattern recognition methods have been developed for the identification of α-helices [1-4, 6, 7] and β-sheets [1,2,4,8]. More recently, machine learning techniques, such as Support Vector Machine (SVM) and Convolutional Neural Networks (CNN), have been applied to this problem [2,9].…”

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confidence: 99%

“…SSETracer [4] utilizes a feature voting technique to identify the spatial characteristics of a cylinder. VolTrac [3] uses a genetic algorithm for placing seeds, followed by a bidirectional optimization of cross-correlation between cylinder templates and the density map.…”

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Detection of Protein Secondary Structure Patterns from 3D Cryo-TEM Maps at Medium Resolution - Combining the Best of SSETracer and VolTrac

Spillers

Wriggers

2017

Microsc Microanal

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Cryo-electron microscopy (Cryo-TEM) has been successfully used to derive the atomic structures of macromolecular assemblies. Structure details are often sufficient for deriving the atomic structure when a 3D density map exhibits a resolution better than 4Å. However, it is still challenging to derive atomic structures from medium-resolution (5-10Å) density maps. At medium resolution it is important to know the location of the major components of a protein structure, such as α-helices and β-sheets, in the density map. Although the backbone of a protein structure is often not visible at medium resolution, secondary structures, such as α-helices and β-sheets, can be computationally identified [1][2][3][4]. In addition, the location of β-strands can be derived from the density region of a β-sheet once it is segmented out from the surrounding density [5].Over the years, various pattern recognition methods have been developed for the identification of α-helices [1-4, 6, 7] and β-sheets [1,2,4,8]. More recently, machine learning techniques, such as Support Vector Machine (SVM) and Convolutional Neural Networks (CNN), have been applied to this problem [2,9]. Figure 1 shows an example of α-helices, β-sheet, and β-strands detected in a cryo-TEM density map at 5.5Å resolution. The location of secondary structures in the density map can be utilized for prediction of the tertiary structure of the protein. This goal is particularly feasible when the location of the β-strands is predictable from the density map. We have developed a computational method, StrandTwister, to derive the orientation of β-strands from the β-sheet region in the density map. Although β-strands are not distinguishable in a density map at medium resolution, we have shown that their locations are predictable utilizing the twist of a β-sheet. The detected traces of α-helices and β-strands were combined with multiple secondary structure predictions from the protein sequence to score candidate topologies of the secondary structure traces. The true topological order of the traces was ranked the 2 nd highest on the list of possible topologies [10] ( Figure 1B). Although major secondary structures can be identified, the precise detection of secondary structures remains a challenging problem [11]. For example, a predicted helix can be longer or shorter than a known benchmark. Small helices and β-sheets are particularly challenging to detect.We have taken advantage of two independently developed methods for secondary structure detection. SSETracer [4] utilizes a feature voting technique to identify the spatial characteristics of a cylinder. VolTrac [3] uses a genetic algorithm for placing seeds, followed by a bidirectional optimization of cross-correlation between cylinder templates and the density map. The two α-helix detection methods have complementary strengths. SSETracer is faster than VolTrac, but it is not as accurate in determining the end position of a helix. Our new hybrid method is built on a quick scan of initial positions of α-helices using SSETracer...

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confidence: 99%

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confidence: 99%

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confidence: 99%

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confidence: 99%

mentioning

confidence: 99%

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Detection of Protein Secondary Structure Patterns from 3D Cryo-TEM Maps at Medium Resolution - Combining the Best of SSETracer and VolTrac

Spillers

Wriggers

2017

Microsc Microanal

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Smart de novo Macromolecular Structure Modeling from Cryo-EM Maps

Chen

Nakamura

et al. 2023

Journal of Molecular Biology

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Numerical geometry of map and model assessment

Wriggers

2015

Journal of Structural Biology

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We are describing best practices and assessment strategies for the atomic interpretation of cryo-electron microscopy (cryo-EM) maps. Multiscale numerical geometry strategies in the Situs package and in secondary structure detection software are currently evolving due to the recent increases in cryo-EM resolution. Criteria that aim to predict the accuracy of fitted atomic models at low (worse than 8 Å) and medium (4–8 Å) resolutions remain challenging. However, a high level of confidence in atomic models can be achieved by combining such criteria. The observed errors are due to map–model discrepancies and due to the effect of imperfect global docking strategies. Extending the earlier motion capture approach developed for flexible fitting, we use simulated fiducials (pseudoatoms) at varying levels of coarse-graining to track the local drift of structural features. We compare three tracking approaches: naïve vector quantization, a smoothly deformable model, and a tessellation of the structure into rigid Voronoi cells, which are fitted using a multi-fragment refinement approach. The lowest error is an upper bound for the (small) discrepancy between the crystal structure and the EM map due to different conditions in their structure determination. When internal features such as secondary structures are visible in medium-resolution EM maps, it is possible to extend the idea of point-based fiducials to more complex geometric representations such as helical axes, strands, and skeletons. We propose quantitative strategies to assess map–model pairs when such secondary structure patterns are prominent.

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Orientations of beta-strand traces and near maximum twist

Cited by 6 publications

References 17 publications

Detection of Protein Secondary Structure Patterns from 3D Cryo-TEM Maps at Medium Resolution - Combining the Best of SSETracer and VolTrac

Detection of Protein Secondary Structure Patterns from 3D Cryo-TEM Maps at Medium Resolution - Combining the Best of SSETracer and VolTrac

Smart de novo Macromolecular Structure Modeling from Cryo-EM Maps

Numerical geometry of map and model assessment

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