Protein Backbone Reconstruction with Tool Preference Classification for Standard and Nonstandard Proteins
碩士 === 國立中山大學 === 資訊工程學系研究所 === 101 === Given a protein sequence and the Cα coordinates on its backbone, the all-atom protein backbone reconstruction problem (PBRP) is to reconstruct the backbone by its 3D coordinates of N, C and O atoms. In the past few decades, many methods have been proposed for...
Main Authors: | , |
---|---|
Other Authors: | |
Format: | Others |
Language: | en_US |
Published: |
2012
|
Online Access: | http://ndltd.ncl.edu.tw/handle/01650908496708950616 |
id |
ndltd-TW-101NSYS5392003 |
---|---|
record_format |
oai_dc |
spelling |
ndltd-TW-101NSYS53920032015-10-13T22:40:31Z http://ndltd.ncl.edu.tw/handle/01650908496708950616 Protein Backbone Reconstruction with Tool Preference Classification for Standard and Nonstandard Proteins 利用工具偏好分類於標準及非標準蛋白質之蛋白質骨幹重建 Hsin-Fang Wu 吳欣芳 碩士 國立中山大學 資訊工程學系研究所 101 Given a protein sequence and the Cα coordinates on its backbone, the all-atom protein backbone reconstruction problem (PBRP) is to reconstruct the backbone by its 3D coordinates of N, C and O atoms. In the past few decades, many methods have been proposed for solving PBRP, such as ab initio, homology modeling, SABBAC, Wang’s method, Chang’s method, BBQ (Backbone Building from Quadrilaterals) and Chen’s method. Chen found that, if they can choose the correct prediction tool to build the 3D coordinates of the desired atoms, the RMSD may be improved. In this thesis, we propose a method for solving PBRP based on Chen’s method. We use tool preference classification on each atom of the residue, where the classification model is generated by SVM (Support Vector Machine). We rebuild the backbone by combing the prediction results of all atoms in all residues. The data sets used in our experiments are CASP7, CASP8 and CASP9, which have 65, 52 and 63 proteins, respectively. These data sets contain nonstandard amino acids as well as standard ones. We improve the average RMSDs of Chen’s results in some cases. The average RMSDs of our method are 0.3496 in CASP7, 0.3084 in CASP8 and 0.3286 in CASP9. Chang-Biau Yang 楊昌彪 2012 學位論文 ; thesis 60 en_US |
collection |
NDLTD |
language |
en_US |
format |
Others
|
sources |
NDLTD |
description |
碩士 === 國立中山大學 === 資訊工程學系研究所 === 101 === Given a protein sequence and the Cα coordinates on its backbone, the all-atom protein backbone reconstruction problem (PBRP) is to reconstruct the backbone by
its 3D coordinates of N, C and O atoms. In the past few decades, many methods have been proposed for solving PBRP, such as ab initio, homology modeling, SABBAC,
Wang’s method, Chang’s method, BBQ (Backbone Building from Quadrilaterals) and Chen’s method. Chen found that, if they can choose the correct prediction tool
to build the 3D coordinates of the desired atoms, the RMSD may be improved. In this thesis, we propose a method for solving PBRP based on Chen’s method. We
use tool preference classification on each atom of the residue, where the classification model is generated by SVM (Support Vector Machine). We rebuild the backbone by
combing the prediction results of all atoms in all residues. The data sets used in our experiments are CASP7, CASP8 and CASP9, which have 65, 52 and 63 proteins, respectively. These data sets contain nonstandard amino acids as well as standard ones. We improve the average RMSDs of Chen’s results in some cases. The average
RMSDs of our method are 0.3496 in CASP7, 0.3084 in CASP8 and 0.3286 in CASP9.
|
author2 |
Chang-Biau Yang |
author_facet |
Chang-Biau Yang Hsin-Fang Wu 吳欣芳 |
author |
Hsin-Fang Wu 吳欣芳 |
spellingShingle |
Hsin-Fang Wu 吳欣芳 Protein Backbone Reconstruction with Tool Preference Classification for Standard and Nonstandard Proteins |
author_sort |
Hsin-Fang Wu |
title |
Protein Backbone Reconstruction with Tool Preference Classification for Standard and Nonstandard Proteins |
title_short |
Protein Backbone Reconstruction with Tool Preference Classification for Standard and Nonstandard Proteins |
title_full |
Protein Backbone Reconstruction with Tool Preference Classification for Standard and Nonstandard Proteins |
title_fullStr |
Protein Backbone Reconstruction with Tool Preference Classification for Standard and Nonstandard Proteins |
title_full_unstemmed |
Protein Backbone Reconstruction with Tool Preference Classification for Standard and Nonstandard Proteins |
title_sort |
protein backbone reconstruction with tool preference classification for standard and nonstandard proteins |
publishDate |
2012 |
url |
http://ndltd.ncl.edu.tw/handle/01650908496708950616 |
work_keys_str_mv |
AT hsinfangwu proteinbackbonereconstructionwithtoolpreferenceclassificationforstandardandnonstandardproteins AT wúxīnfāng proteinbackbonereconstructionwithtoolpreferenceclassificationforstandardandnonstandardproteins AT hsinfangwu lìyònggōngjùpiānhǎofēnlèiyúbiāozhǔnjífēibiāozhǔndànbáizhìzhīdànbáizhìgǔgànzhòngjiàn AT wúxīnfāng lìyònggōngjùpiānhǎofēnlèiyúbiāozhǔnjífēibiāozhǔndànbáizhìzhīdànbáizhìgǔgànzhòngjiàn |
_version_ |
1718079611909177344 |