{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T05:22:51Z","timestamp":1789017771256,"version":"build-2803163510"},"reference-count":29,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,2,28]],"date-time":"2023-02-28T00:00:00Z","timestamp":1677542400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Indian Council of Medical Research","award":["04\/Phage Therapy\/2021\u201322"],"award-info":[{"award-number":["04\/Phage Therapy\/2021\u201322"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Molecules"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/molecules28052238","type":"journal-article","created":{"date-parts":[[2023,2,28]],"date-time":"2023-02-28T02:01:51Z","timestamp":1677549711000},"page":"2238","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Prediction of Phage Virion Proteins Using Machine Learning Methods"],"prefix":"10.3390","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9901-3108","authenticated-orcid":false,"given":"Ranjan Kumar","family":"Barman","sequence":"first","affiliation":[{"name":"Division of Virology, ICMR-National Institute of Cholera and Enteric Diseases, P-33, C.I.T.Road Scheme XM, Beliaghata, Kolkata 700010, West Bengal, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alok Kumar","family":"Chakrabarti","sequence":"additional","affiliation":[{"name":"Division of Virology, ICMR-National Institute of Cholera and Enteric Diseases, P-33, C.I.T.Road Scheme XM, Beliaghata, Kolkata 700010, West Bengal, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shanta","family":"Dutta","sequence":"additional","affiliation":[{"name":"Division of Bacteriology, ICMR-National Institute of Cholera and Enteric Diseases, P-33, C.I.T.Road Scheme XM, Beliaghata, Kolkata 700010, West Bengal, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"130","DOI":"10.4161\/bact.20757","article-title":"The strange history of phage therapy","volume":"2","author":"Summers","year":"2012","journal-title":"Bacteriophage"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.femsre.2003.08.001","article-title":"Ecology of prokaryotic viruses","volume":"28","author":"Weinbauer","year":"2004","journal-title":"FEMS Microbiol. Rev."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1260","DOI":"10.1016\/j.cell.2017.10.045","article-title":"Contemporary Phage Biology: From Classic Models to New Insights","volume":"172","author":"Ofir","year":"2018","journal-title":"Cell"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"162","DOI":"10.4292\/wjgpt.v8.i3.162","article-title":"Phage therapy: An alternative to antibiotics in the age of multi-drug resistance","volume":"8","author":"Lin","year":"2017","journal-title":"World J. Gastrointest. Pharmacol. Ther."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Singh, K., Biswas, A., Chakrabarti, A.K., and Dutta, S. (2022). Phage therapy as a protective tool against pathogenic bacteria: How far we are?. Curr. Pharm. Biotechnol.","DOI":"10.2174\/1389201024666221207114047"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1746","DOI":"10.1001\/jama.2017.12938","article-title":"Phage Therapy\u2019s Role in Combating Antibiotic-Resistant Pathogens","volume":"318","author":"Lyon","year":"2017","journal-title":"JAMA"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"D\u2019Accolti, M., Soffritti, I., Mazzacane, S., and Caselli, E. (2021). Bacteriophages as a Potential 360-Degree Pathogen Control Strategy. Microorganisms, 9.","DOI":"10.3390\/microorganisms9020261"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1016\/j.envpol.2016.11.059","article-title":"Exploring the contribution of bacteriophages to antibiotic resistance","volume":"220","author":"Lekunberri","year":"2017","journal-title":"Environ. Pollut."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1007\/978-1-4939-7447-4_20","article-title":"Screening Phage-Display Antibody Libraries Using Protein Arrays","volume":"1701","author":"Diez","year":"2018","journal-title":"Methods Mol. Biol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/978-1-60327-565-1_14","article-title":"Phage proteomics: Applications of mass spectrometry","volume":"502","author":"Lavigne","year":"2009","journal-title":"Methods Mol. Biol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"745","DOI":"10.3389\/fmicb.2016.00745","article-title":"Proteomic Analysis of a Novel Bacillus Jumbo Phage Revealing Glycoside Hydrolase As Structural Component","volume":"7","author":"Yuan","year":"2016","journal-title":"Front. Microbiol."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Charoenkwan, P., Kanthawong, S., Schaduangrat, N., Yana, J., and Shoombuatong, W. (2020). PVPred-SCM: Improved Prediction and Analysis of Phage Virion Proteins Using a Scoring Card Method. Cells, 9.","DOI":"10.3390\/cells9020353"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2229","DOI":"10.1039\/C4MB00316K","article-title":"Identification of bacteriophage virion proteins by the ANOVA feature selection and analysis","volume":"10","author":"Ding","year":"2014","journal-title":"Mol. Biosyst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"530696","DOI":"10.1155\/2013\/530696","article-title":"Naive Bayes classifier with feature selection to identify phage virion proteins","volume":"2013","author":"Feng","year":"2013","journal-title":"Comput. Math. Methods Med."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"476","DOI":"10.3389\/fmicb.2018.00476","article-title":"PVP-SVM: Sequence-Based Prediction of Phage Virion Proteins Using a Support Vector Machine","volume":"9","author":"Manavalan","year":"2018","journal-title":"Front. Microbiol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Tan, J.X., Dao, F.Y., Lv, H., Feng, P.M., and Ding, H. (2018). Identifying Phage Virion Proteins by Using Two-Step Feature Selection Methods. Molecules, 23.","DOI":"10.3390\/molecules23082000"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"111","DOI":"10.4161\/bact.1.2.14590","article-title":"Pros and cons of phage therapy","volume":"1","author":"Abedon","year":"2011","journal-title":"Bacteriophage"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"513","DOI":"10.3389\/fphar.2019.00513","article-title":"Advantages and Limitations of Bacteriophages for the Treatment of Bacterial Infections","volume":"10","author":"Principi","year":"2019","journal-title":"Front. Pharm."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1855","DOI":"10.1111\/jeb.13904","article-title":"Evolutionary biology and development model of medicines: A necessary \u2018pas de deux\u2019 for future successful bacteriophage therapy","volume":"34","author":"Froissart","year":"2021","journal-title":"J. Evol. Biol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1089\/sur.2018.184","article-title":"Bacteriophages: A Therapy Concept against Multi-Drug-Resistant Bacteria","volume":"19","author":"Rohde","year":"2018","journal-title":"Surg. Infect."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"699","DOI":"10.2174\/138920312804871193","article-title":"Learning from bacteriophages\u2014Advantages and limitations of phage and phage-encoded protein applications","volume":"13","author":"Maciejewska","year":"2012","journal-title":"Curr. Protein Pept. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1105","DOI":"10.1007\/s10822-020-00323-z","article-title":"Meta-iPVP: A sequence-based meta-predictor for improving the prediction of phage virion proteins using effective feature representation","volume":"34","author":"Charoenkwan","year":"2020","journal-title":"J. Comput. Aided Mol. Des."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"D480","DOI":"10.1093\/nar\/gkaa1100","article-title":"UniProt: The universal protein knowledgebase in 2021","volume":"49","author":"UniProt","year":"2021","journal-title":"Nucleic Acids Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1697","DOI":"10.1038\/s41598-018-19752-w","article-title":"AmPEP: Sequence-based prediction of antimicrobial peptides using distribution patterns of amino acid properties and random forest","volume":"8","author":"Bhadra","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Meher, P.K., Sahu, T.K., Banchariya, A., and Rao, A.R. (2017). DIRProt: A computational approach for discriminating insecticide resistant proteins from non-resistant proteins. BMC Bioinform., 18.","DOI":"10.1186\/s12859-017-1587-y"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1100","DOI":"10.3389\/fmicb.2018.01100","article-title":"nifPred: Proteome-Wide Identification and Categorization of Nitrogen-Fixation Proteins of Diaztrophs Based on Composition-Transition-Distribution Features Using Support Vector Machine","volume":"9","author":"Meher","year":"2018","journal-title":"Front. Microbiol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"140406","DOI":"10.1016\/j.bbapap.2020.140406","article-title":"Review and comparative analysis of machine learning-based phage virion protein identification methods","volume":"1868","author":"Meng","year":"2020","journal-title":"Biochim. Biophys. Acta Proteins Proteom."},{"key":"ref_28","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Barman, R.K., Mukhopadhyay, A., Maulik, U., and Das, S. (2019). Identification of infectious disease-associated host genes using machine learning techniques. 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