Sphericity of a protein via the -complex

Kim, Deok Soo; Kim, Jae Kwan; Won, Chung In; Kim, Chong Min; Park, Joon‐Young; Bhak, Jong

doi:10.1016/j.jmgm.2010.01.001

Cited by 9 publications

(6 citation statements)

References 65 publications

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“…The sphericity is a metric that measures how similar an arbitrary shape is to that of a sphere . It can be measured as the ratio between ideal surface area of a sphere to the surface area of the queried shape, while assuming both the sphere and the shape are at the same constant volume . A hypothetical, perfectly spherical globular protein would have a sphericity of 1.0.…”

Section: Methodsmentioning

confidence: 99%

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Princeton_TIGRESS: Protein geometry refinement using simulations and support vector machines

et al. 2013

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Protein structure refinement aims to perform a set of operations given a predicted structure to improve model quality and accuracy with respect to the native in a blind fashion. Despite the numerous computational approaches to the protein refinement problem reported in the previous three CASPs, an overwhelming majority of methods degrade models rather than improve them. We initially developed a method tested using blind predictions during CASP10 which was officially ranked in 5th place among all methods in the refinement category. Here, we present Princeton_TIGRESS, which when benchmarked on all CASP 7,8,9, and 10 refinement targets, simultaneously increased GDT_TS 76% of the time with an average improvement of 0.83 GDT_TS points per structure. The method was additionally benchmarked on models produced by top performing three-dimensional structure prediction servers during CASP10. The robustness of the Princeton_TIGRESS protocol was also tested for different random seeds. We make the Princeton_TIGRESS refinement protocol freely available as a web server at http://atlas.princeton.edu/refinement. Using this protocol, one can consistently refine a prediction to help bridge the gap between a predicted structure and the actual native structure.

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Section: Methodsmentioning

confidence: 99%

“…If the protein is not globular, it would have a low sphericity. The β‐complex, presented by Kim et al . was used to calculate the sphericity of the protein structures.…”

Section: Methodsmentioning

confidence: 99%

Princeton_TIGRESS: Protein geometry refinement using simulations and support vector machines

et al. 2013

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“…This approach, called the QTDB‐approach, is significant because the quasi‐triangulation and the Voronoi diagram are used not only for extracting voids but also for many other problems related to the geometry of molecules. Some applications are as follows: the computation of the Connolly surface, docking simulation, computation of molecular volume, computation of molecular sphericity, etc. We anticipate that many more shape‐related problems that are not known today will also be solved efficiently using the quasi‐triangulation of molecules.…”

Section: Methodsmentioning

confidence: 99%

BetaVoid: Molecular voids via beta-complexes and Voronoi diagrams

et al. 2014

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Molecular external structure is important for molecular function, with voids on the surface and interior being one of the most important features. Hence, recognition of molecular voids and accurate computation of their geometrical properties, such as volume, area and topology, are crucial, yet most popular algorithms are based on the crude use of sampling points and thus are approximations even with a significant amount of computation. In this article, we propose an analytic approach to the problem using the Voronoi diagram of atoms and the beta-complex. The correctness and efficiency of the proposed algorithm is mathematically proved and experimentally verified. The benchmark test clearly shows the superiority of BetaVoid to two popular programs: VOIDOO and CASTp. The proposed algorithm is implemented in the BetaVoid program which is freely available at the Voronoi Diagram Research Center (http://voronoi.hanyang.ac.kr).

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“…The proximity information is stored in the topology of the quasi-triangulation and thus its computation is the most important precondition for many applications running on the BetaMol such as the computation of the Connolly surface (28), (29) , the docking simulation (35), (36) , the computation of the molecular volume (37) , the computation of the molecular sphericity (38) , etc.…”

Section: Topology Structure Computation For Voronoi Diagram/quasi-trimentioning

confidence: 99%

BetaMol: A Molecular Modeling, Analysis and Visualization Software Based on the Beta-Complex and the Quasi-Triangulation

Cho

Kim

Ryu

et al. 2012

JAMDSM

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Molecular shape is one of the most critical factors that determines molecular function. Therefore, it is frequently desirable to understand geometric characteristics of a molecule more precisely and efficiently. In this paper, we introduce the BetaMol, a molecular modeling, analysis, and visualization software based on the recent theory of the beta-complex and the quasi-triangulation that are derived from the Voronoi diagram of three-dimensional spherical atoms. The powerful features of the BetaMol are solely based on a unified, single framework of the mathematically rigorous and computationally efficient beta-complex theory. The BetaMol is implemented in the standard C++ language with OpenGL graphics library and freely available at Voronoi Diagram Research Center web site (http://voronoi.hanyang.ac.kr).

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Sphericity of a protein via the -complex

Cited by 9 publications

References 65 publications

Princeton_TIGRESS: Protein geometry refinement using simulations and support vector machines

Princeton_TIGRESS: Protein geometry refinement using simulations and support vector machines

BetaVoid: Molecular voids via beta-complexes and Voronoi diagrams

BetaMol: A Molecular Modeling, Analysis and Visualization Software Based on the Beta-Complex and the Quasi-Triangulation

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