ACPNet: A Deep Learning Network to Identify Anticancer Peptides by Hybrid Sequence Information
Cancer is one of the most dangerous threats to human health. One of the issues is drug resistance action, which leads to side effects after drug treatment. Numerous therapies have endeavored to relieve the drug resistance action. Recently, anticancer peptides could be a novel and promising anticance...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Molecules |
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| Online Access: | https://www.mdpi.com/1420-3049/27/5/1544 |
| _version_ | 1797474393482330112 |
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| author | Mingwei Sun Sen Yang Xuemei Hu You Zhou |
| author_facet | Mingwei Sun Sen Yang Xuemei Hu You Zhou |
| author_sort | Mingwei Sun |
| collection | DOAJ |
| description | Cancer is one of the most dangerous threats to human health. One of the issues is drug resistance action, which leads to side effects after drug treatment. Numerous therapies have endeavored to relieve the drug resistance action. Recently, anticancer peptides could be a novel and promising anticancer candidate, which can inhibit tumor cell proliferation, migration, and suppress the formation of tumor blood vessels, with fewer side effects. However, it is costly, laborious and time consuming to identify anticancer peptides by biological experiments with a high throughput. Therefore, accurately identifying anti-cancer peptides becomes a key and indispensable step for anticancer peptides therapy. Although some existing computer methods have been developed to predict anticancer peptides, the accuracy still needs to be improved. Thus, in this study, we propose a deep learning-based model, called ACPNet, to distinguish anticancer peptides from non-anticancer peptides (non-ACPs). ACPNet employs three different types of peptide sequence information, peptide physicochemical properties and auto-encoding features linking the training process. ACPNet is a hybrid deep learning network, which fuses fully connected networks and recurrent neural networks. The comparison with other existing methods on ACPs82 datasets shows that ACPNet not only achieves the improvement of 1.2% Accuracy, 2.0% F1-score, and 7.2% Recall, but also gets balanced performance on the Matthews correlation coefficient. Meanwhile, ACPNet is verified on an independent dataset, with 20 proven anticancer peptides, and only one anticancer peptide is predicted as non-ACPs. The comparison and independent validation experiment indicate that ACPNet can accurately distinguish anticancer peptides from non-ACPs. |
| first_indexed | 2024-03-09T20:30:23Z |
| format | Article |
| id | doaj.art-b285f85ef619413e90175b1d00cad2e8 |
| institution | Directory Open Access Journal |
| issn | 1420-3049 |
| language | English |
| last_indexed | 2024-03-09T20:30:23Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Molecules |
| spelling | doaj.art-b285f85ef619413e90175b1d00cad2e82023-11-23T23:25:59ZengMDPI AGMolecules1420-30492022-02-01275154410.3390/molecules27051544ACPNet: A Deep Learning Network to Identify Anticancer Peptides by Hybrid Sequence InformationMingwei Sun0Sen Yang1Xuemei Hu2You Zhou3Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, ChinaSchool of Computer Science and Artificial Intelligence Aliyun School of Big Data School of Software, Changzhou University, Changzhou 213164, ChinaKey Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, ChinaCollege of Software, Jilin University, Changchun 130012, ChinaCancer is one of the most dangerous threats to human health. One of the issues is drug resistance action, which leads to side effects after drug treatment. Numerous therapies have endeavored to relieve the drug resistance action. Recently, anticancer peptides could be a novel and promising anticancer candidate, which can inhibit tumor cell proliferation, migration, and suppress the formation of tumor blood vessels, with fewer side effects. However, it is costly, laborious and time consuming to identify anticancer peptides by biological experiments with a high throughput. Therefore, accurately identifying anti-cancer peptides becomes a key and indispensable step for anticancer peptides therapy. Although some existing computer methods have been developed to predict anticancer peptides, the accuracy still needs to be improved. Thus, in this study, we propose a deep learning-based model, called ACPNet, to distinguish anticancer peptides from non-anticancer peptides (non-ACPs). ACPNet employs three different types of peptide sequence information, peptide physicochemical properties and auto-encoding features linking the training process. ACPNet is a hybrid deep learning network, which fuses fully connected networks and recurrent neural networks. The comparison with other existing methods on ACPs82 datasets shows that ACPNet not only achieves the improvement of 1.2% Accuracy, 2.0% F1-score, and 7.2% Recall, but also gets balanced performance on the Matthews correlation coefficient. Meanwhile, ACPNet is verified on an independent dataset, with 20 proven anticancer peptides, and only one anticancer peptide is predicted as non-ACPs. The comparison and independent validation experiment indicate that ACPNet can accurately distinguish anticancer peptides from non-ACPs.https://www.mdpi.com/1420-3049/27/5/1544anticancer peptidesmulti-view informationdeep learning |
| spellingShingle | Mingwei Sun Sen Yang Xuemei Hu You Zhou ACPNet: A Deep Learning Network to Identify Anticancer Peptides by Hybrid Sequence Information Molecules anticancer peptides multi-view information deep learning |
| title | ACPNet: A Deep Learning Network to Identify Anticancer Peptides by Hybrid Sequence Information |
| title_full | ACPNet: A Deep Learning Network to Identify Anticancer Peptides by Hybrid Sequence Information |
| title_fullStr | ACPNet: A Deep Learning Network to Identify Anticancer Peptides by Hybrid Sequence Information |
| title_full_unstemmed | ACPNet: A Deep Learning Network to Identify Anticancer Peptides by Hybrid Sequence Information |
| title_short | ACPNet: A Deep Learning Network to Identify Anticancer Peptides by Hybrid Sequence Information |
| title_sort | acpnet a deep learning network to identify anticancer peptides by hybrid sequence information |
| topic | anticancer peptides multi-view information deep learning |
| url | https://www.mdpi.com/1420-3049/27/5/1544 |
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