Discovering conserved epitopes of Monkeypox: Novel immunoinformatic and machine learning approaches
The Monkeypox virus, an Orthopoxvirus with zoonotic origins, has been responsible for a growing number of human infections reminiscent of smallpox since May 2022, as reported by the World Health Organization. As of now, there are no established medical treatments for managing Monkeypox infections. I...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-02-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S240584402401003X |
| _version_ | 1797304558034092032 |
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| author | Mohammad Izadi Fatemeh Mirzaei Mohammad Aref Bagherzadeh Shamim Ghiabi Alireza Khalifeh |
| author_facet | Mohammad Izadi Fatemeh Mirzaei Mohammad Aref Bagherzadeh Shamim Ghiabi Alireza Khalifeh |
| author_sort | Mohammad Izadi |
| collection | DOAJ |
| description | The Monkeypox virus, an Orthopoxvirus with zoonotic origins, has been responsible for a growing number of human infections reminiscent of smallpox since May 2022, as reported by the World Health Organization. As of now, there are no established medical treatments for managing Monkeypox infections. In this study, we used machine learning to select conserved epitopes. Proteins were determined using Reverse Vaccinology and Gene Ontology subcellular localization, and their epitopes were predicted. NextClade was used to calculate the number of mutations in each amino acid position using 2433 Monkeypox sequences. The Unsupervised Nearest Neighbor machine learning algorithm and ideal matrix [0 0] were used to calculate the conservancy score of epitopes. Six proteins were determined for epitope prediction. Finally, 47 MHC-I epitopes, 5 MHC-II epitopes, and 10 Linear B cell epitopes were discovered. Our method can select epitopes for vaccine design to prevent viruses with accelerated evolution and high mutation rate. |
| first_indexed | 2024-03-08T00:11:11Z |
| format | Article |
| id | doaj.art-74d9044ebabd45359cfa1e802755727e |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-03-08T00:11:11Z |
| publishDate | 2024-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-74d9044ebabd45359cfa1e802755727e2024-02-17T06:39:10ZengElsevierHeliyon2405-84402024-02-01103e24972Discovering conserved epitopes of Monkeypox: Novel immunoinformatic and machine learning approachesMohammad Izadi0Fatemeh Mirzaei1Mohammad Aref Bagherzadeh2Shamim Ghiabi3Alireza Khalifeh4Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran; Corresponding author. School of Medicine, Shiraz University of Medical Sciences, Karim Khan Zand Blvd, Shiraz, Iran.Student Research Committee, Shiraz University of Medical Sciences, Shiraz, IranStudent Research Committee, Jahrom University of Medical Sciences, Jahrom, IranDepartment of Medical Chemistry, Faculty of Pharmacy, Tehran Medical Sciences, Islamic Azad University, Tehran, IranDepartment of Pathology, Faculty of Dentistry, Shiraz Branch, Islamic Azad of University, Shiraz, IranThe Monkeypox virus, an Orthopoxvirus with zoonotic origins, has been responsible for a growing number of human infections reminiscent of smallpox since May 2022, as reported by the World Health Organization. As of now, there are no established medical treatments for managing Monkeypox infections. In this study, we used machine learning to select conserved epitopes. Proteins were determined using Reverse Vaccinology and Gene Ontology subcellular localization, and their epitopes were predicted. NextClade was used to calculate the number of mutations in each amino acid position using 2433 Monkeypox sequences. The Unsupervised Nearest Neighbor machine learning algorithm and ideal matrix [0 0] were used to calculate the conservancy score of epitopes. Six proteins were determined for epitope prediction. Finally, 47 MHC-I epitopes, 5 MHC-II epitopes, and 10 Linear B cell epitopes were discovered. Our method can select epitopes for vaccine design to prevent viruses with accelerated evolution and high mutation rate.http://www.sciencedirect.com/science/article/pii/S240584402401003XMonkeypoxConserved epitopesMachine learningImmunoinformatic |
| spellingShingle | Mohammad Izadi Fatemeh Mirzaei Mohammad Aref Bagherzadeh Shamim Ghiabi Alireza Khalifeh Discovering conserved epitopes of Monkeypox: Novel immunoinformatic and machine learning approaches Heliyon Monkeypox Conserved epitopes Machine learning Immunoinformatic |
| title | Discovering conserved epitopes of Monkeypox: Novel immunoinformatic and machine learning approaches |
| title_full | Discovering conserved epitopes of Monkeypox: Novel immunoinformatic and machine learning approaches |
| title_fullStr | Discovering conserved epitopes of Monkeypox: Novel immunoinformatic and machine learning approaches |
| title_full_unstemmed | Discovering conserved epitopes of Monkeypox: Novel immunoinformatic and machine learning approaches |
| title_short | Discovering conserved epitopes of Monkeypox: Novel immunoinformatic and machine learning approaches |
| title_sort | discovering conserved epitopes of monkeypox novel immunoinformatic and machine learning approaches |
| topic | Monkeypox Conserved epitopes Machine learning Immunoinformatic |
| url | http://www.sciencedirect.com/science/article/pii/S240584402401003X |
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