Deciphering Membrane Proteins Through Deep Learning Models by Revealing Their Locale Within the Cell
Membrane proteins constitute essential biomolecules attached to or integrated into cellular and organelle membranes, playing diverse roles in cellular processes. Their precise localization is crucial for understanding their functions. Existing protein subcellular localization predictors are predomin...
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MDPI AG
2024-11-01
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| Series: | Bioengineering |
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| author | Mehwish Faiz Saad Jawaid Khan Fahad Azim Nazia Ejaz Fahad Shamim |
| author_facet | Mehwish Faiz Saad Jawaid Khan Fahad Azim Nazia Ejaz Fahad Shamim |
| author_sort | Mehwish Faiz |
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| description | Membrane proteins constitute essential biomolecules attached to or integrated into cellular and organelle membranes, playing diverse roles in cellular processes. Their precise localization is crucial for understanding their functions. Existing protein subcellular localization predictors are predominantly trained on globular proteins; their performance diminishes for membrane proteins, explicitly via deep learning models. To address this challenge, the proposed study segregates membrane proteins into three distinct locations, including the plasma membrane, internal membrane, and membrane of the organelle, using deep learning algorithms including recurrent neural networks (RNN) and Long Short-Term Memory (LSTM). A redundancy-curtailed dataset of 3000 proteins from the MemLoci approach is selected for the investigation, along with incorporating pseudo amino acid composition (PseAAC). PseAAC is an exemplary technique for extracting protein information hidden in the amino acid sequences. After extensive testing, the results show that the accuracy for LSTM and RNN is 83.4% and 80.5%, respectively. The results show that the LSTM model outperforms the RNN and is most commonly employed in proteomics. |
| format | Article |
| id | doaj-art-8cd7997bdbe54bc0a197af3ca9220a89 |
| institution | OA Journals |
| issn | 2306-5354 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Bioengineering |
| spelling | doaj-art-8cd7997bdbe54bc0a197af3ca9220a892025-08-20T01:53:52ZengMDPI AGBioengineering2306-53542024-11-011111115010.3390/bioengineering11111150Deciphering Membrane Proteins Through Deep Learning Models by Revealing Their Locale Within the CellMehwish Faiz0Saad Jawaid Khan1Fahad Azim2Nazia Ejaz3Fahad Shamim4Department of Electrical Engineering, Faculty of Engineering, Science, Technology and Management, Ziauddin University, Karachi 74200, PakistanDepartment of Biomedical Engineering, Faculty of Engineering, Science, Technology and Management, Ziauddin University, Karachi 74200, PakistanDepartment of Electrical Engineering, Faculty of Engineering, Science, Technology and Management, Ziauddin University, Karachi 74200, PakistanDepartment of Biomedical Engineering, Balochistan University of Engineering and Technology, Khuzdar 89100, PakistanInstitute of Biomedical Engineering & Technology (IBET), Liaquat University of Medical and Health Sciences, Jamshoro 76060, PakistanMembrane proteins constitute essential biomolecules attached to or integrated into cellular and organelle membranes, playing diverse roles in cellular processes. Their precise localization is crucial for understanding their functions. Existing protein subcellular localization predictors are predominantly trained on globular proteins; their performance diminishes for membrane proteins, explicitly via deep learning models. To address this challenge, the proposed study segregates membrane proteins into three distinct locations, including the plasma membrane, internal membrane, and membrane of the organelle, using deep learning algorithms including recurrent neural networks (RNN) and Long Short-Term Memory (LSTM). A redundancy-curtailed dataset of 3000 proteins from the MemLoci approach is selected for the investigation, along with incorporating pseudo amino acid composition (PseAAC). PseAAC is an exemplary technique for extracting protein information hidden in the amino acid sequences. After extensive testing, the results show that the accuracy for LSTM and RNN is 83.4% and 80.5%, respectively. The results show that the LSTM model outperforms the RNN and is most commonly employed in proteomics.https://www.mdpi.com/2306-5354/11/11/1150membrane proteinsubcellular localizationcellpseudo amino acid compositiondeep learning modelsproteomics |
| spellingShingle | Mehwish Faiz Saad Jawaid Khan Fahad Azim Nazia Ejaz Fahad Shamim Deciphering Membrane Proteins Through Deep Learning Models by Revealing Their Locale Within the Cell Bioengineering membrane protein subcellular localization cell pseudo amino acid composition deep learning models proteomics |
| title | Deciphering Membrane Proteins Through Deep Learning Models by Revealing Their Locale Within the Cell |
| title_full | Deciphering Membrane Proteins Through Deep Learning Models by Revealing Their Locale Within the Cell |
| title_fullStr | Deciphering Membrane Proteins Through Deep Learning Models by Revealing Their Locale Within the Cell |
| title_full_unstemmed | Deciphering Membrane Proteins Through Deep Learning Models by Revealing Their Locale Within the Cell |
| title_short | Deciphering Membrane Proteins Through Deep Learning Models by Revealing Their Locale Within the Cell |
| title_sort | deciphering membrane proteins through deep learning models by revealing their locale within the cell |
| topic | membrane protein subcellular localization cell pseudo amino acid composition deep learning models proteomics |
| url | https://www.mdpi.com/2306-5354/11/11/1150 |
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