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Analysis on protein structures using...

Iowa State University., Biochemistry, Biophysics, and Molecular Biology.

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Analysis on protein structures using statistical and bioinformatical methods.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Analysis on protein structures using statistical and bioinformatical methods./
作者:	Yan, Aimin.
面頁冊數:	150 p.
附註:	Advisers: Robert L. Jernigan; Zhijun Wu; Vasant Honavar.
Contained By:	Dissertation Abstracts International69-08B.
標題:	Biology, Bioinformatics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoeng/servlet/advanced?query=3316183
ISBN:	9780549688143

Analysis on protein structures using statistical and bioinformatical methods.
Yan, Aimin.

Analysis on protein structures using statistical and bioinformatical methods. - 150 p.

Advisers: Robert L. Jernigan; Zhijun Wu; Vasant Honavar.

Thesis (Ph.D.)--Iowa State University, 2008.

This PhD dissertation mainly focuses on the statistical analysis for the protein structure data. The first research project focuses on data mining and prediction for side chain orientation in protein structure. Through this study, we find that the general side chain orientation can be viewed as a manifestation of hydrophobic force. Along with this study, we also developed the software for visualizing the general side chain vector and applied statistical machine learning methods to fit several models for predicting general side chain orientation. In the second project, we studied the motion of partially assembled ribosome 30S subunit using the coarse-grained elastic network model. Besides our studies on ribosome motion, using 176 NMR structure ensembles, we applied principal component analysis to analyze the essential conformational changes to validate the motion generated by the elastic network model. Furthermore, we also studied the effects of different superposition methods on the correspondence between the conformational changes and the simulated motion. Principal component shaving method is often used to cluster gene with the similar gene expression pattern in micro-array data analysis. In the third research project, we applied this method to cluster the structures within a NMR structure ensemble and demonstrated that this method could be used to find the similar structure cluster.

ISBN: 9780549688143Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

Analysis on protein structures using statistical and bioinformatical methods.
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100 1 $a Yan, Aimin. $3 1020877
245 1 0 $a Analysis on protein structures using statistical and bioinformatical methods.
300 $a 150 p.
500 $a Advisers: Robert L. Jernigan; Zhijun Wu; Vasant Honavar.
500 $a Source: Dissertation Abstracts International, Volume: 69-08, Section: B, page: 4508.
502 $a Thesis (Ph.D.)--Iowa State University, 2008.
520 $a This PhD dissertation mainly focuses on the statistical analysis for the protein structure data. The first research project focuses on data mining and prediction for side chain orientation in protein structure. Through this study, we find that the general side chain orientation can be viewed as a manifestation of hydrophobic force. Along with this study, we also developed the software for visualizing the general side chain vector and applied statistical machine learning methods to fit several models for predicting general side chain orientation. In the second project, we studied the motion of partially assembled ribosome 30S subunit using the coarse-grained elastic network model. Besides our studies on ribosome motion, using 176 NMR structure ensembles, we applied principal component analysis to analyze the essential conformational changes to validate the motion generated by the elastic network model. Furthermore, we also studied the effects of different superposition methods on the correspondence between the conformational changes and the simulated motion. Principal component shaving method is often used to cluster gene with the similar gene expression pattern in micro-array data analysis. In the third research project, we applied this method to cluster the structures within a NMR structure ensemble and demonstrated that this method could be used to find the similar structure cluster.
590 $a School code: 0097.
650 4 $a Biology, Bioinformatics. $3 1018415
650 4 $a Biology, Biostatistics. $3 1018416
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710 2 $a Iowa State University. $b Biochemistry, Biophysics, and Molecular Biology. $3 1020876
773 0 $t Dissertation Abstracts International $g 69-08B.
790 $a 0097
790 1 0 $a Carriquiry, Alicia $e committee member
790 1 0 $a Fernandez-Baca, David $e committee member
790 1 0 $a Ho, Kai-Ming $e committee member
790 1 0 $a Honavar, Vasant, $e advisor
790 1 0 $a Jernigan, Robert L., $e advisor
790 1 0 $a Wu, Zhijun, $e advisor
791 $a Ph.D.
792 $a 2008
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoeng/servlet/advanced?query=3316183