Ranking Valid Topologies of the Secondary Structure Elements Using a Constraint Graph

Nasr, Kamal Al; Ranjan, Desh; Zubair, Mohammad; He, Jing

doi:10.1142/s0219720011005604

Cited by 33 publications

(37 citation statements)

References 42 publications

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“…However, the likely connections may be derived. Given the positions of a helices and b strands in a density map, one can match them with secondary structure sequence segments that can be predicted from the amino acid sequence to derive the overall topology of a protein chain (Al Nasr et al, 2011;Baker et al, 2011;Biswas et al, 2012;Lindert et al, 2012;Lu et al, 2008). Once the topology is determined, backbone and side chains can be constructed and evaluated using energy functions (Al Nasr et al, 2010;Lindert et al, 2012;Sun and He, 2009).…”

Section: Introductionmentioning

confidence: 99%

Tracing Beta Strands Using StrandTwister from Cryo-EM Density Maps at Medium Resolutions

2014

Structure

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Major secondary structure elements such as α helices and β sheets can be computationally detected from cryoelectron microscopy (cryo-EM) density maps with medium resolutions of 5-10 Å. However, a critical piece of information for modeling atomic structures is missing, because there are no tools to detect β strands from cryo-EM maps at medium resolutions. We propose a method, StrandTwister, to detect the traces of β strands through the analysis of twist, an intrinsic nature of a β sheet. StrandTwister has been tested using 100 β sheets simulated at 10 Å resolution and 39 β sheets computationally detected from cryo-EM density maps at 4.4-7.4 Å resolutions. Although experimentally derived cryo-EM maps contain errors, StrandTwister's best detections over 39 cases were able to detect 81.87% of the β strands, with an overall 1.66 Å two-way distance between the detected and observed β traces. StrandTwister appears to detect the traces of β strands on major β sheets quite accurately, particularly at the central area of a β sheet.

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Section: Introductionmentioning

confidence: 99%

Tracing Beta Strands Using StrandTwister from Cryo-EM Density Maps at Medium Resolutions

2014

Structure

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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%

“…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. We previously showed that using a dynamic programming method combined with the K-shortest path algorithm, it takes Δ 2 time to rank the top K topologies using DP-TOSS [23,38]. Here N is the number of secondary structure traces detected in the density map and M is the number of secondary structure sequence segments, and Δ 1.…”

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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“…We previously formulated the SSE topology problem into a constraint graph problem and gave a dynamic programming algorithm for the simplest situation in which [27]. More importantly, our previous algorithm determines the optimal solution that often fails when the optimal solution is not the true topology due to the various errors in the data.…”

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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Ranking Valid Topologies of the Secondary Structure Elements Using a Constraint Graph

Cited by 33 publications

References 42 publications

Tracing Beta Strands Using StrandTwister from Cryo-EM Density Maps at Medium Resolutions

Tracing Beta Strands Using StrandTwister from Cryo-EM 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

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