pHisPred: a tool for the identification of histidine phosphorylation sites by integrating amino acid patterns and properties
Abstract Background Protein histidine phosphorylation (pHis) plays critical roles in prokaryotic signal transduction pathways and various eukaryotic cellular processes. It is estimated to account for 6–10% of the phosphoproteome, however only hundreds of pHis sites have been discovered to date. Due...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-09-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-022-04938-x |
| _version_ | 1811202874302005248 |
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| author | Jian Zhao Minhui Zhuang Jingjing Liu Meng Zhang Cong Zeng Bin Jiang Jing Wu Xiaofeng Song |
| author_facet | Jian Zhao Minhui Zhuang Jingjing Liu Meng Zhang Cong Zeng Bin Jiang Jing Wu Xiaofeng Song |
| author_sort | Jian Zhao |
| collection | DOAJ |
| description | Abstract Background Protein histidine phosphorylation (pHis) plays critical roles in prokaryotic signal transduction pathways and various eukaryotic cellular processes. It is estimated to account for 6–10% of the phosphoproteome, however only hundreds of pHis sites have been discovered to date. Due to the inherent disadvantages of experimental methods, it is an urgent task for developing efficient computational approaches to identify pHis sites. Results Here, we present a novel tool, pHisPred, for accurately identifying pHis sites from protein sequences. We manually collected the largest number of experimental validated pHis sites to build benchmark datasets. Using randomized tenfold CV, the weighted SVM-RBF model shows the best performance than other four commonly used classification models (LR, KNN, RF, and MLP). From ten thousands of features, 140 and 150 most informative features were individually selected out for eukaryotic and prokaryotic models. The average AUC and F1-score values of pHisPred were (0.81, 0.40) and (0.78, 0.46) for tenfold CV on the eukaryotic and prokaryotic training datasets, respectively. In addition, pHisPred significantly outperforms other tools on testing datasets, in particular on the eukaryotic one. Conclusion We implemented a python program of pHisPred, which is freely available for non-commercial use at https://github.com/xiaofengsong/pHisPred . Moreover, users can use it to train new models with their own data. |
| first_indexed | 2024-04-12T02:46:18Z |
| format | Article |
| id | doaj.art-8104357d2c21495d8422b41ea4c39a63 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-12T02:46:18Z |
| publishDate | 2022-09-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-8104357d2c21495d8422b41ea4c39a632022-12-22T03:51:10ZengBMCBMC Bioinformatics1471-21052022-09-0123S311710.1186/s12859-022-04938-xpHisPred: a tool for the identification of histidine phosphorylation sites by integrating amino acid patterns and propertiesJian Zhao0Minhui Zhuang1Jingjing Liu2Meng Zhang3Cong Zeng4Bin Jiang5Jing Wu6Xiaofeng Song7Department of Biomedical Engineering, Nanjing University of Aeronautics and AstronauticsDepartment of Biomedical Engineering, Nanjing University of Aeronautics and AstronauticsDepartment of Biomedical Engineering, Nanjing University of Aeronautics and AstronauticsDepartment of Biomedical Engineering, Nanjing University of Aeronautics and AstronauticsDepartment of Biomedical Engineering, Nanjing University of Aeronautics and AstronauticsCollege of Automation Engineering, Nanjing University of Aeronautics and AstronauticsSchool of Biomedical Engineering and Informatics, Nanjing Medical UniversityDepartment of Biomedical Engineering, Nanjing University of Aeronautics and AstronauticsAbstract Background Protein histidine phosphorylation (pHis) plays critical roles in prokaryotic signal transduction pathways and various eukaryotic cellular processes. It is estimated to account for 6–10% of the phosphoproteome, however only hundreds of pHis sites have been discovered to date. Due to the inherent disadvantages of experimental methods, it is an urgent task for developing efficient computational approaches to identify pHis sites. Results Here, we present a novel tool, pHisPred, for accurately identifying pHis sites from protein sequences. We manually collected the largest number of experimental validated pHis sites to build benchmark datasets. Using randomized tenfold CV, the weighted SVM-RBF model shows the best performance than other four commonly used classification models (LR, KNN, RF, and MLP). From ten thousands of features, 140 and 150 most informative features were individually selected out for eukaryotic and prokaryotic models. The average AUC and F1-score values of pHisPred were (0.81, 0.40) and (0.78, 0.46) for tenfold CV on the eukaryotic and prokaryotic training datasets, respectively. In addition, pHisPred significantly outperforms other tools on testing datasets, in particular on the eukaryotic one. Conclusion We implemented a python program of pHisPred, which is freely available for non-commercial use at https://github.com/xiaofengsong/pHisPred . Moreover, users can use it to train new models with their own data.https://doi.org/10.1186/s12859-022-04938-xHistidine phosphorylationPhosphohistidine siteMachine learningpHis predictionpHisPred |
| spellingShingle | Jian Zhao Minhui Zhuang Jingjing Liu Meng Zhang Cong Zeng Bin Jiang Jing Wu Xiaofeng Song pHisPred: a tool for the identification of histidine phosphorylation sites by integrating amino acid patterns and properties BMC Bioinformatics Histidine phosphorylation Phosphohistidine site Machine learning pHis prediction pHisPred |
| title | pHisPred: a tool for the identification of histidine phosphorylation sites by integrating amino acid patterns and properties |
| title_full | pHisPred: a tool for the identification of histidine phosphorylation sites by integrating amino acid patterns and properties |
| title_fullStr | pHisPred: a tool for the identification of histidine phosphorylation sites by integrating amino acid patterns and properties |
| title_full_unstemmed | pHisPred: a tool for the identification of histidine phosphorylation sites by integrating amino acid patterns and properties |
| title_short | pHisPred: a tool for the identification of histidine phosphorylation sites by integrating amino acid patterns and properties |
| title_sort | phispred a tool for the identification of histidine phosphorylation sites by integrating amino acid patterns and properties |
| topic | Histidine phosphorylation Phosphohistidine site Machine learning pHis prediction pHisPred |
| url | https://doi.org/10.1186/s12859-022-04938-x |
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