Computational Prediction and Analysis of Associations between Small Molecules and Binding-Associated S-Nitrosylation Sites
Interactions between drugs and proteins occupy a central position during the process of drug discovery and development. Numerous methods have recently been developed for identifying drug–target interactions, but few have been devoted to finding interactions between post-translationally mod...
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MDPI AG
2018-04-01
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| Series: | Molecules |
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| Online Access: | http://www.mdpi.com/1420-3049/23/4/954 |
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doaj-3a2c5bf38278448e84ebdf8df91ba3222020-11-24T22:37:44ZengMDPI AGMolecules1420-30492018-04-0123495410.3390/molecules23040954molecules23040954Computational Prediction and Analysis of Associations between Small Molecules and Binding-Associated S-Nitrosylation SitesGuohua Huang0Jincheng Li1Chenglin Zhao2Provincial Key Laboratory of Informational Service for Rural Area of Southwestern Hunan, Shaoyang University, Shaoyang 422000, ChinaProvincial Key Laboratory of Informational Service for Rural Area of Southwestern Hunan, Shaoyang University, Shaoyang 422000, ChinaProvincial Key Laboratory of Informational Service for Rural Area of Southwestern Hunan, Shaoyang University, Shaoyang 422000, ChinaInteractions between drugs and proteins occupy a central position during the process of drug discovery and development. Numerous methods have recently been developed for identifying drug–target interactions, but few have been devoted to finding interactions between post-translationally modified proteins and drugs. We presented a machine learning-based method for identifying associations between small molecules and binding-associated S-nitrosylated (SNO-) proteins. Namely, small molecules were encoded by molecular fingerprint, SNO-proteins were encoded by the information entropy-based method, and the random forest was used to train a classifier. Ten-fold and leave-one-out cross validations achieved, respectively, 0.7235 and 0.7490 of the area under a receiver operating characteristic curve. Computational analysis of similarity suggested that SNO-proteins associated with the same drug shared statistically significant similarity, and vice versa. This method and finding are useful to identify drug–SNO associations and further facilitate the discovery and development of SNO-associated drugs.http://www.mdpi.com/1420-3049/23/4/954SNOrandom forestfingerprintsinformation entropymachine learning |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Guohua Huang Jincheng Li Chenglin Zhao |
| spellingShingle |
Guohua Huang Jincheng Li Chenglin Zhao Computational Prediction and Analysis of Associations between Small Molecules and Binding-Associated S-Nitrosylation Sites Molecules SNO random forest fingerprints information entropy machine learning |
| author_facet |
Guohua Huang Jincheng Li Chenglin Zhao |
| author_sort |
Guohua Huang |
| title |
Computational Prediction and Analysis of Associations between Small Molecules and Binding-Associated S-Nitrosylation Sites |
| title_short |
Computational Prediction and Analysis of Associations between Small Molecules and Binding-Associated S-Nitrosylation Sites |
| title_full |
Computational Prediction and Analysis of Associations between Small Molecules and Binding-Associated S-Nitrosylation Sites |
| title_fullStr |
Computational Prediction and Analysis of Associations between Small Molecules and Binding-Associated S-Nitrosylation Sites |
| title_full_unstemmed |
Computational Prediction and Analysis of Associations between Small Molecules and Binding-Associated S-Nitrosylation Sites |
| title_sort |
computational prediction and analysis of associations between small molecules and binding-associated s-nitrosylation sites |
| publisher |
MDPI AG |
| series |
Molecules |
| issn |
1420-3049 |
| publishDate |
2018-04-01 |
| description |
Interactions between drugs and proteins occupy a central position during the process of drug discovery and development. Numerous methods have recently been developed for identifying drug–target interactions, but few have been devoted to finding interactions between post-translationally modified proteins and drugs. We presented a machine learning-based method for identifying associations between small molecules and binding-associated S-nitrosylated (SNO-) proteins. Namely, small molecules were encoded by molecular fingerprint, SNO-proteins were encoded by the information entropy-based method, and the random forest was used to train a classifier. Ten-fold and leave-one-out cross validations achieved, respectively, 0.7235 and 0.7490 of the area under a receiver operating characteristic curve. Computational analysis of similarity suggested that SNO-proteins associated with the same drug shared statistically significant similarity, and vice versa. This method and finding are useful to identify drug–SNO associations and further facilitate the discovery and development of SNO-associated drugs. |
| topic |
SNO random forest fingerprints information entropy machine learning |
| url |
http://www.mdpi.com/1420-3049/23/4/954 |
| work_keys_str_mv |
AT guohuahuang computationalpredictionandanalysisofassociationsbetweensmallmoleculesandbindingassociatedsnitrosylationsites AT jinchengli computationalpredictionandanalysisofassociationsbetweensmallmoleculesandbindingassociatedsnitrosylationsites AT chenglinzhao computationalpredictionandanalysisofassociationsbetweensmallmoleculesandbindingassociatedsnitrosylationsites |
| _version_ |
1725715759946006528 |