Prediction of Phage Virion Proteins Using Machine Learning Methods
Antimicrobial resistance (AMR) is a major problem and an immediate alternative to antibiotics is the need of the hour. Research on the possible alternative products to tackle bacterial infections is ongoing worldwide. One of the most promising alternatives to antibiotics is the use of bacteriophages...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Molecules |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1420-3049/28/5/2238 |
| _version_ | 1797614804742963200 |
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| author | Ranjan Kumar Barman Alok Kumar Chakrabarti Shanta Dutta |
| author_facet | Ranjan Kumar Barman Alok Kumar Chakrabarti Shanta Dutta |
| author_sort | Ranjan Kumar Barman |
| collection | DOAJ |
| description | Antimicrobial resistance (AMR) is a major problem and an immediate alternative to antibiotics is the need of the hour. Research on the possible alternative products to tackle bacterial infections is ongoing worldwide. One of the most promising alternatives to antibiotics is the use of bacteriophages (phage) or phage-driven antibacterial drugs to cure bacterial infections caused by AMR bacteria. Phage-driven proteins, including holins, endolysins, and exopolysaccharides, have shown great potential in the development of antibacterial drugs. Likewise, phage virion proteins (PVPs) might also play an important role in the development of antibacterial drugs. Here, we have developed a machine learning-based prediction method to predict PVPs using phage protein sequences. We have employed well-known basic and ensemble machine learning methods with protein sequence composition features for the prediction of PVPs. We found that the gradient boosting classifier (GBC) method achieved the best accuracy of 80% on the training dataset and an accuracy of 83% on the independent dataset. The performance on the independent dataset is better than other existing methods. A user-friendly web server developed by us is freely available to all users for the prediction of PVPs from phage protein sequences. The web server might facilitate the large-scale prediction of PVPs and hypothesis-driven experimental study design. |
| first_indexed | 2024-03-11T07:16:17Z |
| format | Article |
| id | doaj.art-30355761767d4094ac149ca7e87abb11 |
| institution | Directory Open Access Journal |
| issn | 1420-3049 |
| language | English |
| last_indexed | 2024-03-11T07:16:17Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Molecules |
| spelling | doaj.art-30355761767d4094ac149ca7e87abb112023-11-17T08:13:54ZengMDPI AGMolecules1420-30492023-02-01285223810.3390/molecules28052238Prediction of Phage Virion Proteins Using Machine Learning MethodsRanjan Kumar Barman0Alok Kumar Chakrabarti1Shanta Dutta2Division of Virology, ICMR-National Institute of Cholera and Enteric Diseases, P-33, C.I.T.Road Scheme XM, Beliaghata, Kolkata 700010, West Bengal, IndiaDivision of Virology, ICMR-National Institute of Cholera and Enteric Diseases, P-33, C.I.T.Road Scheme XM, Beliaghata, Kolkata 700010, West Bengal, IndiaDivision of Bacteriology, ICMR-National Institute of Cholera and Enteric Diseases, P-33, C.I.T.Road Scheme XM, Beliaghata, Kolkata 700010, West Bengal, IndiaAntimicrobial resistance (AMR) is a major problem and an immediate alternative to antibiotics is the need of the hour. Research on the possible alternative products to tackle bacterial infections is ongoing worldwide. One of the most promising alternatives to antibiotics is the use of bacteriophages (phage) or phage-driven antibacterial drugs to cure bacterial infections caused by AMR bacteria. Phage-driven proteins, including holins, endolysins, and exopolysaccharides, have shown great potential in the development of antibacterial drugs. Likewise, phage virion proteins (PVPs) might also play an important role in the development of antibacterial drugs. Here, we have developed a machine learning-based prediction method to predict PVPs using phage protein sequences. We have employed well-known basic and ensemble machine learning methods with protein sequence composition features for the prediction of PVPs. We found that the gradient boosting classifier (GBC) method achieved the best accuracy of 80% on the training dataset and an accuracy of 83% on the independent dataset. The performance on the independent dataset is better than other existing methods. A user-friendly web server developed by us is freely available to all users for the prediction of PVPs from phage protein sequences. The web server might facilitate the large-scale prediction of PVPs and hypothesis-driven experimental study design.https://www.mdpi.com/1420-3049/28/5/2238AMRbacteriophagephage virion proteinmachine learningphage therapyweb server |
| spellingShingle | Ranjan Kumar Barman Alok Kumar Chakrabarti Shanta Dutta Prediction of Phage Virion Proteins Using Machine Learning Methods Molecules AMR bacteriophage phage virion protein machine learning phage therapy web server |
| title | Prediction of Phage Virion Proteins Using Machine Learning Methods |
| title_full | Prediction of Phage Virion Proteins Using Machine Learning Methods |
| title_fullStr | Prediction of Phage Virion Proteins Using Machine Learning Methods |
| title_full_unstemmed | Prediction of Phage Virion Proteins Using Machine Learning Methods |
| title_short | Prediction of Phage Virion Proteins Using Machine Learning Methods |
| title_sort | prediction of phage virion proteins using machine learning methods |
| topic | AMR bacteriophage phage virion protein machine learning phage therapy web server |
| url | https://www.mdpi.com/1420-3049/28/5/2238 |
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