Deep‐GB: A novel deep learning model for globular protein prediction using CNN‐BiLSTM architecture and enhanced PSSM with trisection strategy
Abstract Globular proteins (GPs) play vital roles in a wide range of biological processes, encompassing enzymatic catalysis and immune responses. Enzymes, among these globular proteins, facilitate biochemical reactions, while others, such as haemoglobin, contribute to essential physiological functio...
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | IET Systems Biology |
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| Online Access: | https://doi.org/10.1049/syb2.12108 |
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| author | Sonia Zouari Farman Ali Atef Masmoudi Sarah Abu Ghazalah Wajdi Alghamdi Faris A. Kateb Nouf Ibrahim |
| author_facet | Sonia Zouari Farman Ali Atef Masmoudi Sarah Abu Ghazalah Wajdi Alghamdi Faris A. Kateb Nouf Ibrahim |
| author_sort | Sonia Zouari |
| collection | DOAJ |
| description | Abstract Globular proteins (GPs) play vital roles in a wide range of biological processes, encompassing enzymatic catalysis and immune responses. Enzymes, among these globular proteins, facilitate biochemical reactions, while others, such as haemoglobin, contribute to essential physiological functions such as oxygen transport. Given the importance of these considerations, accurately identifying Globular proteins is essential. To address the need for precise GP identification, this research introduces an innovative approach that employs a hybrid‐based deep learning model called Deep‐GP. We generated two datasets based on primary sequences and developed a novel feature descriptor called, Consensus Sequence‐based Trisection‐Position Specific Scoring Matrix (CST‐PSSM). The model training phase involved the application of deep learning techniques, including the bidirectional long short‐term memory network (BiLSTM), gated recurrent unit (GRU), and convolutional neural network (CNN). The BiLSTM and CNN were hybridised for ensemble learning. The CST‐PSSM‐based ensemble model achieved the most accurate predictive outcomes, outperforming other competitive predictors across both training and testing datasets. This demonstrates the potential of harnessing deep learning for precise GB prediction as a robust tool to expedite research, streamline drug discovery, and unveil novel therapeutic targets. |
| format | Article |
| id | doaj-art-1051302e881d4e01917c8104d711ddba |
| institution | OA Journals |
| issn | 1751-8849 1751-8857 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Systems Biology |
| spelling | doaj-art-1051302e881d4e01917c8104d711ddba2025-08-20T01:59:57ZengWileyIET Systems Biology1751-88491751-88572024-12-0118620821710.1049/syb2.12108Deep‐GB: A novel deep learning model for globular protein prediction using CNN‐BiLSTM architecture and enhanced PSSM with trisection strategySonia Zouari0Farman Ali1Atef Masmoudi2Sarah Abu Ghazalah3Wajdi Alghamdi4Faris A. Kateb5Nouf Ibrahim6National Engineering School of Sfax University of Sfax Sfax TunisiaDepartment of Computer Science Bahria University Islamabad Campus Islamabad PakistanDepartment of Computer Science College of Computer Science King Khalid University Abha Saudi ArabiaDepartment of Informatics and Computer System College of Computer Science King Khalid University Abha Saudi ArabiaDepartment of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi ArabiaDepartment of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi ArabiaFamily Medicine Clinic Makkah Armed Force Medical Center Makkah Saudi ArabiaAbstract Globular proteins (GPs) play vital roles in a wide range of biological processes, encompassing enzymatic catalysis and immune responses. Enzymes, among these globular proteins, facilitate biochemical reactions, while others, such as haemoglobin, contribute to essential physiological functions such as oxygen transport. Given the importance of these considerations, accurately identifying Globular proteins is essential. To address the need for precise GP identification, this research introduces an innovative approach that employs a hybrid‐based deep learning model called Deep‐GP. We generated two datasets based on primary sequences and developed a novel feature descriptor called, Consensus Sequence‐based Trisection‐Position Specific Scoring Matrix (CST‐PSSM). The model training phase involved the application of deep learning techniques, including the bidirectional long short‐term memory network (BiLSTM), gated recurrent unit (GRU), and convolutional neural network (CNN). The BiLSTM and CNN were hybridised for ensemble learning. The CST‐PSSM‐based ensemble model achieved the most accurate predictive outcomes, outperforming other competitive predictors across both training and testing datasets. This demonstrates the potential of harnessing deep learning for precise GB prediction as a robust tool to expedite research, streamline drug discovery, and unveil novel therapeutic targets.https://doi.org/10.1049/syb2.12108bioinformaticsbiological techniques |
| spellingShingle | Sonia Zouari Farman Ali Atef Masmoudi Sarah Abu Ghazalah Wajdi Alghamdi Faris A. Kateb Nouf Ibrahim Deep‐GB: A novel deep learning model for globular protein prediction using CNN‐BiLSTM architecture and enhanced PSSM with trisection strategy IET Systems Biology bioinformatics biological techniques |
| title | Deep‐GB: A novel deep learning model for globular protein prediction using CNN‐BiLSTM architecture and enhanced PSSM with trisection strategy |
| title_full | Deep‐GB: A novel deep learning model for globular protein prediction using CNN‐BiLSTM architecture and enhanced PSSM with trisection strategy |
| title_fullStr | Deep‐GB: A novel deep learning model for globular protein prediction using CNN‐BiLSTM architecture and enhanced PSSM with trisection strategy |
| title_full_unstemmed | Deep‐GB: A novel deep learning model for globular protein prediction using CNN‐BiLSTM architecture and enhanced PSSM with trisection strategy |
| title_short | Deep‐GB: A novel deep learning model for globular protein prediction using CNN‐BiLSTM architecture and enhanced PSSM with trisection strategy |
| title_sort | deep gb a novel deep learning model for globular protein prediction using cnn bilstm architecture and enhanced pssm with trisection strategy |
| topic | bioinformatics biological techniques |
| url | https://doi.org/10.1049/syb2.12108 |
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