Neural Network Method for Protein Structure Search using Cell-to-Cell Adhesion

碩士 === 臺灣大學 === 資訊工程學研究所 === 95 === Protein structure analysis and alignment is a topic receiving public focus now due to the important applications in biological and medical fields. We introduce a neural network based protein structure search method using the cell-to-cell adhesion property of the f...

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Main Authors: Cheng-Jung Ho, 何政融
Other Authors: Cheng-Yuan Liou
Format: Others
Language:en_US
Published: 2007
Online Access:http://ndltd.ncl.edu.tw/handle/15798096482143995110
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spelling ndltd-TW-095NTU053921462015-10-13T13:55:54Z http://ndltd.ncl.edu.tw/handle/15798096482143995110 Neural Network Method for Protein Structure Search using Cell-to-Cell Adhesion 使用特徵相連關係的類神經網路蛋白質結構搜尋方法 Cheng-Jung Ho 何政融 碩士 臺灣大學 資訊工程學研究所 95 Protein structure analysis and alignment is a topic receiving public focus now due to the important applications in biological and medical fields. We introduce a neural network based protein structure search method using the cell-to-cell adhesion property of the feature cells in this article. This method aims to find protein structures consisting of a target segment structure of interest from the protein structure database. By extending the basic idea from a handprinted Chinese character recognition method, we design this method for protein structure matching by taking advantage of the speed and performance of the character recogntion method. The method is a coarse one that aims to identify the protein structure that may contain the substructure of interest and needs further post-processing on the candidates to locate the exact location of the substructure. We design a new feature extraction method that generates rotation and translation invariant features and use the Procrustes algorithm to find the key substructures which are denoted by radicals. The Procrustes algorithm is originally designed for linguistic analysis and we modify it without loss of its characteristics to apply on the protein structures. Compatibility measurements between the protein structure and radical substructures is of key importance in the process and is formulated as an optimization problem solvable by a Hopfield network. The searching work is performed by a trained backpropagation network that takes in the compatibilty scores of the query substructure and outputs the reference to the candidate protein structures. Finally we compare this method to another index-based method that uses a modified suffix tree algorithm. We hope that this work can contribute to the biomedical researches in finding cures for a new virus by searching for the similar viral protein structures of viruses with existing cures. Since similar protein structures may have similar functions, the existing cures found by this method may be effective against the new virus too. This work was partially supported by National Science Council, ROC under contract number NSC 94-2213-E-002-105. Cheng-Yuan Liou 劉長遠 2007 學位論文 ; thesis 36 en_US
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description 碩士 === 臺灣大學 === 資訊工程學研究所 === 95 === Protein structure analysis and alignment is a topic receiving public focus now due to the important applications in biological and medical fields. We introduce a neural network based protein structure search method using the cell-to-cell adhesion property of the feature cells in this article. This method aims to find protein structures consisting of a target segment structure of interest from the protein structure database. By extending the basic idea from a handprinted Chinese character recognition method, we design this method for protein structure matching by taking advantage of the speed and performance of the character recogntion method. The method is a coarse one that aims to identify the protein structure that may contain the substructure of interest and needs further post-processing on the candidates to locate the exact location of the substructure. We design a new feature extraction method that generates rotation and translation invariant features and use the Procrustes algorithm to find the key substructures which are denoted by radicals. The Procrustes algorithm is originally designed for linguistic analysis and we modify it without loss of its characteristics to apply on the protein structures. Compatibility measurements between the protein structure and radical substructures is of key importance in the process and is formulated as an optimization problem solvable by a Hopfield network. The searching work is performed by a trained backpropagation network that takes in the compatibilty scores of the query substructure and outputs the reference to the candidate protein structures. Finally we compare this method to another index-based method that uses a modified suffix tree algorithm. We hope that this work can contribute to the biomedical researches in finding cures for a new virus by searching for the similar viral protein structures of viruses with existing cures. Since similar protein structures may have similar functions, the existing cures found by this method may be effective against the new virus too. This work was partially supported by National Science Council, ROC under contract number NSC 94-2213-E-002-105.
author2 Cheng-Yuan Liou
author_facet Cheng-Yuan Liou
Cheng-Jung Ho
何政融
author Cheng-Jung Ho
何政融
spellingShingle Cheng-Jung Ho
何政融
Neural Network Method for Protein Structure Search using Cell-to-Cell Adhesion
author_sort Cheng-Jung Ho
title Neural Network Method for Protein Structure Search using Cell-to-Cell Adhesion
title_short Neural Network Method for Protein Structure Search using Cell-to-Cell Adhesion
title_full Neural Network Method for Protein Structure Search using Cell-to-Cell Adhesion
title_fullStr Neural Network Method for Protein Structure Search using Cell-to-Cell Adhesion
title_full_unstemmed Neural Network Method for Protein Structure Search using Cell-to-Cell Adhesion
title_sort neural network method for protein structure search using cell-to-cell adhesion
publishDate 2007
url http://ndltd.ncl.edu.tw/handle/15798096482143995110
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