AntiDMPpred: a web service for identifying anti-diabetic peptides
Diabetes mellitus (DM) is a chronic metabolic disease that has been a major threat to human health globally, causing great economic and social adversities. The oral administration of anti-diabetic peptide drugs has become a novel route for diabetes therapy. Numerous bioactive peptides have demonstra...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2022-06-01
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| Series: | PeerJ |
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| Online Access: | https://peerj.com/articles/13581.pdf |
| _version_ | 1797419006079008768 |
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| author | Xue Chen Jian Huang Bifang He |
| author_facet | Xue Chen Jian Huang Bifang He |
| author_sort | Xue Chen |
| collection | DOAJ |
| description | Diabetes mellitus (DM) is a chronic metabolic disease that has been a major threat to human health globally, causing great economic and social adversities. The oral administration of anti-diabetic peptide drugs has become a novel route for diabetes therapy. Numerous bioactive peptides have demonstrated potential anti-diabetic properties and are promising as alternative treatment measures to prevent and manage diabetes. The computational prediction of anti-diabetic peptides can help promote peptide-based drug discovery in the process of searching newly effective therapeutic peptide agents for diabetes treatment. Here, we resorted to random forest to develop a computational model, named AntiDMPpred, for predicting anti-diabetic peptides. A benchmark dataset with 236 anti-diabetic and 236 non-anti-diabetic peptides was first constructed. Four types of sequence-derived descriptors were used to represent the peptide sequences. We then combined four machine learning methods and six feature scoring methods to select the non-redundant features, which were fed into diverse machine learning classifiers to train the models. Experimental results show that AntiDMPpred reached an accuracy of 77.12% and area under the receiver operating curve (AUCROC) of 0.8193 in the nested five-fold cross-validation, yielding a satisfactory performance and surpassing other classifiers implemented in the study. The web service is freely accessible at http://i.uestc.edu.cn/AntiDMPpred/cgi-bin/AntiDMPpred.pl. We hope AntiDMPpred could improve the discovery of anti-diabetic bioactive peptides. |
| first_indexed | 2024-03-09T06:41:17Z |
| format | Article |
| id | doaj.art-6052112fb54e4facbafbef4ff1a73a31 |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T06:41:17Z |
| publishDate | 2022-06-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-6052112fb54e4facbafbef4ff1a73a312023-12-03T10:49:45ZengPeerJ Inc.PeerJ2167-83592022-06-0110e1358110.7717/peerj.13581AntiDMPpred: a web service for identifying anti-diabetic peptidesXue Chen0Jian Huang1Bifang He2Medical College, Guizhou University, Guiyang, ChinaSchool of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, ChinaMedical College, Guizhou University, Guiyang, ChinaDiabetes mellitus (DM) is a chronic metabolic disease that has been a major threat to human health globally, causing great economic and social adversities. The oral administration of anti-diabetic peptide drugs has become a novel route for diabetes therapy. Numerous bioactive peptides have demonstrated potential anti-diabetic properties and are promising as alternative treatment measures to prevent and manage diabetes. The computational prediction of anti-diabetic peptides can help promote peptide-based drug discovery in the process of searching newly effective therapeutic peptide agents for diabetes treatment. Here, we resorted to random forest to develop a computational model, named AntiDMPpred, for predicting anti-diabetic peptides. A benchmark dataset with 236 anti-diabetic and 236 non-anti-diabetic peptides was first constructed. Four types of sequence-derived descriptors were used to represent the peptide sequences. We then combined four machine learning methods and six feature scoring methods to select the non-redundant features, which were fed into diverse machine learning classifiers to train the models. Experimental results show that AntiDMPpred reached an accuracy of 77.12% and area under the receiver operating curve (AUCROC) of 0.8193 in the nested five-fold cross-validation, yielding a satisfactory performance and surpassing other classifiers implemented in the study. The web service is freely accessible at http://i.uestc.edu.cn/AntiDMPpred/cgi-bin/AntiDMPpred.pl. We hope AntiDMPpred could improve the discovery of anti-diabetic bioactive peptides.https://peerj.com/articles/13581.pdfAnti-diabetic peptidesPeptide descriptorsMachine learningComputational model |
| spellingShingle | Xue Chen Jian Huang Bifang He AntiDMPpred: a web service for identifying anti-diabetic peptides PeerJ Anti-diabetic peptides Peptide descriptors Machine learning Computational model |
| title | AntiDMPpred: a web service for identifying anti-diabetic peptides |
| title_full | AntiDMPpred: a web service for identifying anti-diabetic peptides |
| title_fullStr | AntiDMPpred: a web service for identifying anti-diabetic peptides |
| title_full_unstemmed | AntiDMPpred: a web service for identifying anti-diabetic peptides |
| title_short | AntiDMPpred: a web service for identifying anti-diabetic peptides |
| title_sort | antidmppred a web service for identifying anti diabetic peptides |
| topic | Anti-diabetic peptides Peptide descriptors Machine learning Computational model |
| url | https://peerj.com/articles/13581.pdf |
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