Protein Secondary Structure Prediction Using Ensemble Neural Networks With Local And Long-range Amino-acid Features

Predicting protein structures from sequences is a challenging problem. Determining the secondary structures of the protein is an effective approach to infer the complete protein structure. The interactions of local and long-range amino-acid residues in proteins are key contributors in defining the p...

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Main Author:	Hazzaa Mahyoub, Fawaz Hameed
Format:	Thesis
Language:	English
Published:	2021
Subjects:	QA75.5-76.95 Electronic computers Computer science
Online Access:	http://eprints.usm.my/52692/1/FAWAZ%20HAMEED%20HAZZAA%20MAHYOUB%20-%20TESIS24.pdf
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spelling	my-usm-ep.526922022-05-31T17:13:39Z Protein Secondary Structure Prediction Using Ensemble Neural Networks With Local And Long-range Amino-acid Features 2021-10 Hazzaa Mahyoub, Fawaz Hameed QA75.5-76.95 Electronic computers. Computer science Predicting protein structures from sequences is a challenging problem. Determining the secondary structures of the protein is an effective approach to infer the complete protein structure. The interactions of local and long-range amino-acid residues in proteins are key contributors in defining the protein secondary structures. Recent works have focused on capturing local and long-range amino-acid interactions using various predicted protein structural features via an ensemble of deep learning techniques. Nevertheless, determining these structural features is always associated with intensive computing. Moreover, their predictive performance is heavily relied on the quality of the data features resulting from evolutionarily related proteins. This study proposes a method for predicting protein secondary structure by incorporating Feed-Forward Neural Network (FFNN) with bidirectional Long Short-Term Memory (LSTM) networks to capture local and long-range amino-acid interactions. To further improve the prediction accuracy of proteins with few evolutionarily related proteins, additional data features based on the physicochemical properties of amino acids have been proposed. The empirical outcomes indicate that the proposed method in this study shows competitive prediction accuracy compared to Sequence-based Prediction Online Tools for one dimensional structural features (SPOT-1D) and PORTER5. In addition to that, the method outperformed several well-known cutting-edge methods by 2-3 percentagepoint improvement. 2021-10 Thesis http://eprints.usm.my/52692/ http://eprints.usm.my/52692/1/FAWAZ%20HAMEED%20HAZZAA%20MAHYOUB%20-%20TESIS24.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Sains Komputer
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	QA75.5-76.95 Electronic computers Computer science
spellingShingle	QA75.5-76.95 Electronic computers Computer science Hazzaa Mahyoub, Fawaz Hameed Protein Secondary Structure Prediction Using Ensemble Neural Networks With Local And Long-range Amino-acid Features
description	Predicting protein structures from sequences is a challenging problem. Determining the secondary structures of the protein is an effective approach to infer the complete protein structure. The interactions of local and long-range amino-acid residues in proteins are key contributors in defining the protein secondary structures. Recent works have focused on capturing local and long-range amino-acid interactions using various predicted protein structural features via an ensemble of deep learning techniques. Nevertheless, determining these structural features is always associated with intensive computing. Moreover, their predictive performance is heavily relied on the quality of the data features resulting from evolutionarily related proteins. This study proposes a method for predicting protein secondary structure by incorporating Feed-Forward Neural Network (FFNN) with bidirectional Long Short-Term Memory (LSTM) networks to capture local and long-range amino-acid interactions. To further improve the prediction accuracy of proteins with few evolutionarily related proteins, additional data features based on the physicochemical properties of amino acids have been proposed. The empirical outcomes indicate that the proposed method in this study shows competitive prediction accuracy compared to Sequence-based Prediction Online Tools for one dimensional structural features (SPOT-1D) and PORTER5. In addition to that, the method outperformed several well-known cutting-edge methods by 2-3 percentagepoint improvement.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Hazzaa Mahyoub, Fawaz Hameed
author_facet	Hazzaa Mahyoub, Fawaz Hameed
author_sort	Hazzaa Mahyoub, Fawaz Hameed
title	Protein Secondary Structure Prediction Using Ensemble Neural Networks With Local And Long-range Amino-acid Features
title_short	Protein Secondary Structure Prediction Using Ensemble Neural Networks With Local And Long-range Amino-acid Features
title_full	Protein Secondary Structure Prediction Using Ensemble Neural Networks With Local And Long-range Amino-acid Features
title_fullStr	Protein Secondary Structure Prediction Using Ensemble Neural Networks With Local And Long-range Amino-acid Features
title_full_unstemmed	Protein Secondary Structure Prediction Using Ensemble Neural Networks With Local And Long-range Amino-acid Features
title_sort	protein secondary structure prediction using ensemble neural networks with local and long-range amino-acid features
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Sains Komputer
publishDate	2021
url	http://eprints.usm.my/52692/1/FAWAZ%20HAMEED%20HAZZAA%20MAHYOUB%20-%20TESIS24.pdf
_version_	1747822204699017216

Protein Secondary Structure Prediction Using Ensemble Neural Networks With Local And Long-range Amino-acid Features

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