Shape modeling and matching in identifying 3D protein structures

Abeysinghe, Sasakthi S.; Ju, Tao; Baker, Matthew L.; Chiu, Wah

doi:10.1016/j.cad.2008.01.013

Cited by 37 publications

(26 citation statements)

References 41 publications

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“…The de novo modeling combines information from the density map and the amino acid sequence of the protein to derive the topology of secondary structure traces [21,23,[37][38][39]. A topology maps the secondary structure traces from the density map to the amino acid sequence, and therefore determines how the protein chain thread through the traces.…”

Section: Introductionmentioning

confidence: 99%

Deriving Protein Backbone Using Traces Extracted from Density Maps at Medium Resolutions

Nasr

2015

Bioinformatics Research and Applications

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Abstract. Electron cryomicroscopy is an experimental technique that is capable to produce three dimensional gray-scale images for protein molecules, called density maps. At medium resolution, the atomic details of the molecule cannot be visualized from density maps. However, some features of the molecule can be seen such as the locations of major secondary structures and the skeleton of the molecule. In addition, the order and direction of the detected secondary structure traces can be inferred. We introduce a method to construct the entire model of a protein directly for traces extracted from the density map. The initial results show that this method has good potential. A single model was built for each of the 12 proteins used in the test. The RMSD 100 of the models is slightly improved from our previous method. Keywords:Cryo-EM · Volume image · Skeletonization · Protein modeling · Loop modeling IntroductionElectron cryomicroscopy (cryo-EM) is an emerging technique that produces threedimensional (3D) electron density maps at a wide-range of resolutions [1][2][3][4]. When the resolution of density maps is higher than 4Å, the atomic structure can often be derived [5][6][7][8][9]. At the medium resolutions, such as 5-10Å, the backbone and the characteristic features of amino acids are not resolved. It is still challenging to derive the atomic structure from such a density map. When a component of the protein has atomic structure available, fitting can be performed to derive the atomic structure [10][11][12]. When a homologous model is available, rigid or flexible fitting can be used to derive the atomic structure [13][14][15][16][17][18]. However, it is still challenging to find a suitable template for many proteins. De novo modeling is an alternative method to derive atomic structures without relying on template structures [19][20][21][22][23][24]. It relies on the detection of secondary structure positions and the connection patterns encoded in the skeleton of the density map. A number of computational methods have been developed to detect α-helices from the density maps [25][26][27][28][29][30][31]. Most helices longer than two turns can be detected. Most of the major β-sheets can also be detected using various methods such as SheetTracer, SSEhunter,. By analyzing the twist of β-sheet

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Section: Introductionmentioning

confidence: 99%

Deriving Protein Backbone Using Traces Extracted from Density Maps at Medium Resolutions

Nasr

2015

Bioinformatics Research and Applications

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“…The third, perhaps the most effective approach is to translate the topology problem into a graph problem by exploiting the constraints from a pair of sticks. Gorgon is such a graph-matching method [26]. It produces two graphs, one represents to the connectivity relationship of the sticks derived from the volumetric map, and the other represents the linear relationship of the segments on the proteins sequence.…”

Section: Introductionmentioning

confidence: 99%

A Constrained K-shortest Path Algorithm to Rank the Topologies of the Protein Secondary Structure Elements Detected in CryoEM Volume Maps

Nasr

Chen

Ranjan

et al. 2013

Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics

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Although many electron density maps have been produced into the medium resolutions, it is still challenging to derive the atomic structure from such volumetric data. Current methods primarily rely on the availability of an existing atomic structure for fitting or a homologous template structure for modeling. In the process of developing a template-free, de novo, method, the topology of the secondary structure elements need to be resolved first. In this paper, we extend our previous algorithm of finding the optimal solution in the constraint graph problem. We illustrate an approach to obtain the top-K topologies by combining a dynamic programming algorithm with the K-shortest path algorithm. The effectiveness of the algorithms is demonstrated from the test using three datasets of different nature. The algorithm improves the accuracy, space and time of an existing method.

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“…Methods have been developed to fit solved structures into such maps, to find locations of secondary structure elements 1,2 and determine the topology of these elements 3 , to select and rethreading homology models using density data 4 , and to flexibly fit models into density 5,6,7,8,9,10,11 . These methods generally start with complete all-atom models, rather than the Cα-only models that are often traced through low-resolution density.…”

Section: Introductionmentioning

confidence: 99%

Refinement of Protein Structures into Low-Resolution Density Maps Using Rosetta

DiMaio

Tyka

Baker

et al. 2009

Journal of Molecular Biology

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282

296

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We describe a method based on Rosetta structure refinement for generating high-resolution all-atom protein models from electron cryo-microscopy density maps. A local measure of the fit of a model to the density is used to directly guide structure refinement and to identify regions incompatible with the density that are then targeted for extensive rebuilding. Over a range of test cases using both simulated and experimentally generated data, the method consistently increases the accuracy of starting models generated either by comparative modeling or by hand-tracing the density. The method can achieve near atomic resolution starting from density maps at 4-6Å resolution.

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Shape modeling and matching in identifying 3D protein structures

Cited by 37 publications

References 41 publications

Deriving Protein Backbone Using Traces Extracted from Density Maps at Medium Resolutions

Deriving Protein Backbone Using Traces Extracted from Density Maps at Medium Resolutions

A Constrained K-shortest Path Algorithm to Rank the Topologies of the Protein Secondary Structure Elements Detected in CryoEM Volume Maps

Refinement of Protein Structures into Low-Resolution Density Maps Using Rosetta

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