New Error Measures and Methods for Realizing Protein Graphs from Distance Data.
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| Title: | New Error Measures and Methods for Realizing Protein Graphs from Distance Data. |
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| Authors: | D'Ambrosio, Claudia1 dambrosio@lix.polytechnique.fr, Vu, Ky1 vu@lix.polytechnique.fr, Lavor, Carlile2 clavor@ime.unicamp.br, Liberti, Leo1 liberti@lix.polytechnique.fr, Maculan, Nelson3 maculan@cos.ufrj.br |
| Source: | Discrete & Computational Geometry. Mar2017, Vol. 57 Issue 2, p371-418. 48p. |
| Subjects: | Distance geometry, Protein conformation, Mathematical programming, Euclidean distance, Standard deviations |
| Abstract: | The interval distance geometry problem consists in finding a realization in $$\mathbb {R}^K$$ of a simple undirected graph $$G=(V,E)$$ with non-negative intervals assigned to the edges in such a way that, for each edge, the Euclidean distance between the realization of the adjacent vertices is within the edge interval bounds. In this paper, we focus on the application to the conformation of proteins in space, which is a basic step in determining protein function: given interval estimations of some of the inter-atomic distances, find their shape. Among different families of methods for accomplishing this task, we look at mathematical programming based methods, which are well suited for dealing with intervals. The basic question we want to answer is: what is the best such method for the problem? The most meaningful error measure for evaluating solution quality is the coordinate root mean square deviation. We first introduce a new error measure which addresses a particular feature of protein backbones, i.e. many partial reflections also yield acceptable backbones. We then present a set of new and existing quadratic and semidefinite programming formulations of this problem, and a set of new and existing methods for solving these formulations. Finally, we perform a computational evaluation of all the feasible solver $$+$$ formulation combinations according to new and existing error measures, finding that the best methodology is a new heuristic method based on multiplicative weights updates. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Items | – Name: Title Label: Title Group: Ti Data: New Error Measures and Methods for Realizing Protein Graphs from Distance Data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22D'Ambrosio%2C+Claudia%22">D'Ambrosio, Claudia</searchLink><relatesTo>1</relatesTo><i> dambrosio@lix.polytechnique.fr</i><br /><searchLink fieldCode="AR" term="%22Vu%2C+Ky%22">Vu, Ky</searchLink><relatesTo>1</relatesTo><i> vu@lix.polytechnique.fr</i><br /><searchLink fieldCode="AR" term="%22Lavor%2C+Carlile%22">Lavor, Carlile</searchLink><relatesTo>2</relatesTo><i> clavor@ime.unicamp.br</i><br /><searchLink fieldCode="AR" term="%22Liberti%2C+Leo%22">Liberti, Leo</searchLink><relatesTo>1</relatesTo><i> liberti@lix.polytechnique.fr</i><br /><searchLink fieldCode="AR" term="%22Maculan%2C+Nelson%22">Maculan, Nelson</searchLink><relatesTo>3</relatesTo><i> maculan@cos.ufrj.br</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Discrete+%26+Computational+Geometry%22">Discrete & Computational Geometry</searchLink>. Mar2017, Vol. 57 Issue 2, p371-418. 48p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Distance+geometry%22">Distance geometry</searchLink><br /><searchLink fieldCode="DE" term="%22Protein+conformation%22">Protein conformation</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+programming%22">Mathematical programming</searchLink><br /><searchLink fieldCode="DE" term="%22Euclidean+distance%22">Euclidean distance</searchLink><br /><searchLink fieldCode="DE" term="%22Standard+deviations%22">Standard deviations</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The interval distance geometry problem consists in finding a realization in $$\mathbb {R}^K$$ of a simple undirected graph $$G=(V,E)$$ with non-negative intervals assigned to the edges in such a way that, for each edge, the Euclidean distance between the realization of the adjacent vertices is within the edge interval bounds. In this paper, we focus on the application to the conformation of proteins in space, which is a basic step in determining protein function: given interval estimations of some of the inter-atomic distances, find their shape. Among different families of methods for accomplishing this task, we look at mathematical programming based methods, which are well suited for dealing with intervals. The basic question we want to answer is: what is the best such method for the problem? The most meaningful error measure for evaluating solution quality is the coordinate root mean square deviation. We first introduce a new error measure which addresses a particular feature of protein backbones, i.e. many partial reflections also yield acceptable backbones. We then present a set of new and existing quadratic and semidefinite programming formulations of this problem, and a set of new and existing methods for solving these formulations. Finally, we perform a computational evaluation of all the feasible solver $$+$$ formulation combinations according to new and existing error measures, finding that the best methodology is a new heuristic method based on multiplicative weights updates. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Discrete & Computational Geometry is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00454-016-9846-7 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 48 StartPage: 371 Subjects: – SubjectFull: Distance geometry Type: general – SubjectFull: Protein conformation Type: general – SubjectFull: Mathematical programming Type: general – SubjectFull: Euclidean distance Type: general – SubjectFull: Standard deviations Type: general Titles: – TitleFull: New Error Measures and Methods for Realizing Protein Graphs from Distance Data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: D'Ambrosio, Claudia – PersonEntity: Name: NameFull: Vu, Ky – PersonEntity: Name: NameFull: Lavor, Carlile – PersonEntity: Name: NameFull: Liberti, Leo – PersonEntity: Name: NameFull: Maculan, Nelson IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2017 Type: published Y: 2017 Identifiers: – Type: issn-print Value: 01795376 Numbering: – Type: volume Value: 57 – Type: issue Value: 2 Titles: – TitleFull: Discrete & Computational Geometry Type: main |
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