Neural network for prediction of cysteine disulphide bridge connectivity in proteins

The goal of this thesis is to develop a computational method based on machine learning techniques for predicting disulfide-bonding states of Cysteine residues in proteins, which is a sub-problem of the bigger and yet unsolved problem of protein structure prediction. First, we preprocessed the datase...

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Main Author:	Bostan, Hamed
Format:	Thesis
Language:	English
Published:	2010
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/18275/1/HamedBostanMFSKSM2010.pdf
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spelling	my-utm-ep.182752017-09-18T04:26:19Z Neural network for prediction of cysteine disulphide bridge connectivity in proteins 2010 Bostan, Hamed QA75 Electronic computers. Computer science The goal of this thesis is to develop a computational method based on machine learning techniques for predicting disulfide-bonding states of Cysteine residues in proteins, which is a sub-problem of the bigger and yet unsolved problem of protein structure prediction. First, we preprocessed the datasets from Protein Data Bank (PDB) and filtered mutations and low resolution files out. A number of descriptors in two dimensional (2D) protein sequences are studied. These descriptors are based on local feature values of adjacent amino acid to Cystein residue, namely encoded, propensity value and averaged propensity value. We have used Artificial Neural Network (ANN) as a machine learning technique to develop our prediction method. We use ‘trainlm’, ‘trainrp’ and ‘trainscg’ training functions for training out network and also a 5-fold validation is implemented. Our results show that we can predict the state of Cystein disulphide bond formation. It shows that using propensity valued descriptor and ‘trainscg’ training function is better to be used for Cystein bond state prediction compared to the other training functions and descriptors in this study. The accuracy of prediction in this study is 80.85% on a propensity value descriptor dataset which had been trained by ‘trainscg’ with a dataset of over than 400 thousand protein patterns. Results of this work will have direct implications in site directed mutational studies of protein, protein engineering and the problem of protein folding. 2010 Thesis http://eprints.utm.my/id/eprint/18275/ http://eprints.utm.my/id/eprint/18275/1/HamedBostanMFSKSM2010.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Computer Science and Information Systems Faculty of Computer Science and Information System
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Bostan, Hamed Neural network for prediction of cysteine disulphide bridge connectivity in proteins
description	The goal of this thesis is to develop a computational method based on machine learning techniques for predicting disulfide-bonding states of Cysteine residues in proteins, which is a sub-problem of the bigger and yet unsolved problem of protein structure prediction. First, we preprocessed the datasets from Protein Data Bank (PDB) and filtered mutations and low resolution files out. A number of descriptors in two dimensional (2D) protein sequences are studied. These descriptors are based on local feature values of adjacent amino acid to Cystein residue, namely encoded, propensity value and averaged propensity value. We have used Artificial Neural Network (ANN) as a machine learning technique to develop our prediction method. We use ‘trainlm’, ‘trainrp’ and ‘trainscg’ training functions for training out network and also a 5-fold validation is implemented. Our results show that we can predict the state of Cystein disulphide bond formation. It shows that using propensity valued descriptor and ‘trainscg’ training function is better to be used for Cystein bond state prediction compared to the other training functions and descriptors in this study. The accuracy of prediction in this study is 80.85% on a propensity value descriptor dataset which had been trained by ‘trainscg’ with a dataset of over than 400 thousand protein patterns. Results of this work will have direct implications in site directed mutational studies of protein, protein engineering and the problem of protein folding.
format	Thesis
qualification_level	Master's degree
author	Bostan, Hamed
author_facet	Bostan, Hamed
author_sort	Bostan, Hamed
title	Neural network for prediction of cysteine disulphide bridge connectivity in proteins
title_short	Neural network for prediction of cysteine disulphide bridge connectivity in proteins
title_full	Neural network for prediction of cysteine disulphide bridge connectivity in proteins
title_fullStr	Neural network for prediction of cysteine disulphide bridge connectivity in proteins
title_full_unstemmed	Neural network for prediction of cysteine disulphide bridge connectivity in proteins
title_sort	neural network for prediction of cysteine disulphide bridge connectivity in proteins
granting_institution	Universiti Teknologi Malaysia, Faculty of Computer Science and Information Systems
granting_department	Faculty of Computer Science and Information System
publishDate	2010
url	http://eprints.utm.my/id/eprint/18275/1/HamedBostanMFSKSM2010.pdf
_version_	1747815235427762176

Neural network for prediction of cysteine disulphide bridge connectivity in proteins

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