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Enhancing Sumoylation Site Prediction: A Deep Neural Network with Discriminative Features
Sumoylation is a post-translation modification (PTM) mechanism that involves many critical biological processes, such as gene expression, localizing and stabilizing proteins, and replicating the genome. Moreover, sumoylation sites are associated with different diseases, including Parkinson’s and Alz...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10672286/ https://www.ncbi.nlm.nih.gov/pubmed/38004293 http://dx.doi.org/10.3390/life13112153 |
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author | Khan, Salman Khan, Mukhtaj Iqbal, Nadeem Dilshad, Naqqash Almufareh, Maram Fahaad Alsubaie, Najah |
author_facet | Khan, Salman Khan, Mukhtaj Iqbal, Nadeem Dilshad, Naqqash Almufareh, Maram Fahaad Alsubaie, Najah |
author_sort | Khan, Salman |
collection | PubMed |
description | Sumoylation is a post-translation modification (PTM) mechanism that involves many critical biological processes, such as gene expression, localizing and stabilizing proteins, and replicating the genome. Moreover, sumoylation sites are associated with different diseases, including Parkinson’s and Alzheimer’s. Due to its vital role in the biological process, identifying sumoylation sites in proteins is significant for monitoring protein functions and discovering multiple diseases. Therefore, in the literature, several computational models utilizing conventional ML methods have been introduced to classify sumoylation sites. However, these models cannot accurately classify the sumoylation sites due to intrinsic limitations associated with the conventional learning methods. This paper proposes a robust computational model (called Deep-Sumo) for predicting sumoylation sites based on a deep-learning algorithm with efficient feature representation methods. The proposed model employs a half-sphere exposure method to represent protein sequences in a feature vector. Principal Component Analysis is applied to extract discriminative features by eliminating noisy and redundant features. The discriminant features are given to a multilayer Deep Neural Network (DNN) model to predict sumoylation sites accurately. The performance of the proposed model is extensively evaluated using a 10-fold cross-validation test by considering various statistical-based performance measurement metrics. Initially, the proposed DNN is compared with the traditional learning algorithm, and subsequently, the performance of the Deep-Sumo is compared with the existing models. The validation results show that the proposed model reports an average accuracy of 96.47%, with improvement compared with the existing models. It is anticipated that the proposed model can be used as an effective tool for drug discovery and the diagnosis of multiple diseases. |
format | Online Article Text |
id | pubmed-10672286 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-106722862023-11-02 Enhancing Sumoylation Site Prediction: A Deep Neural Network with Discriminative Features Khan, Salman Khan, Mukhtaj Iqbal, Nadeem Dilshad, Naqqash Almufareh, Maram Fahaad Alsubaie, Najah Life (Basel) Article Sumoylation is a post-translation modification (PTM) mechanism that involves many critical biological processes, such as gene expression, localizing and stabilizing proteins, and replicating the genome. Moreover, sumoylation sites are associated with different diseases, including Parkinson’s and Alzheimer’s. Due to its vital role in the biological process, identifying sumoylation sites in proteins is significant for monitoring protein functions and discovering multiple diseases. Therefore, in the literature, several computational models utilizing conventional ML methods have been introduced to classify sumoylation sites. However, these models cannot accurately classify the sumoylation sites due to intrinsic limitations associated with the conventional learning methods. This paper proposes a robust computational model (called Deep-Sumo) for predicting sumoylation sites based on a deep-learning algorithm with efficient feature representation methods. The proposed model employs a half-sphere exposure method to represent protein sequences in a feature vector. Principal Component Analysis is applied to extract discriminative features by eliminating noisy and redundant features. The discriminant features are given to a multilayer Deep Neural Network (DNN) model to predict sumoylation sites accurately. The performance of the proposed model is extensively evaluated using a 10-fold cross-validation test by considering various statistical-based performance measurement metrics. Initially, the proposed DNN is compared with the traditional learning algorithm, and subsequently, the performance of the Deep-Sumo is compared with the existing models. The validation results show that the proposed model reports an average accuracy of 96.47%, with improvement compared with the existing models. It is anticipated that the proposed model can be used as an effective tool for drug discovery and the diagnosis of multiple diseases. MDPI 2023-11-02 /pmc/articles/PMC10672286/ /pubmed/38004293 http://dx.doi.org/10.3390/life13112153 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Khan, Salman Khan, Mukhtaj Iqbal, Nadeem Dilshad, Naqqash Almufareh, Maram Fahaad Alsubaie, Najah Enhancing Sumoylation Site Prediction: A Deep Neural Network with Discriminative Features |
title | Enhancing Sumoylation Site Prediction: A Deep Neural Network with Discriminative Features |
title_full | Enhancing Sumoylation Site Prediction: A Deep Neural Network with Discriminative Features |
title_fullStr | Enhancing Sumoylation Site Prediction: A Deep Neural Network with Discriminative Features |
title_full_unstemmed | Enhancing Sumoylation Site Prediction: A Deep Neural Network with Discriminative Features |
title_short | Enhancing Sumoylation Site Prediction: A Deep Neural Network with Discriminative Features |
title_sort | enhancing sumoylation site prediction: a deep neural network with discriminative features |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10672286/ https://www.ncbi.nlm.nih.gov/pubmed/38004293 http://dx.doi.org/10.3390/life13112153 |
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