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Molecules 2018, 23(4), 954; https://doi.org/10.3390/molecules23040954

Computational Prediction and Analysis of Associations between Small Molecules and Binding-Associated S-Nitrosylation Sites

1
Provincial Key Laboratory of Informational Service for Rural Area of Southwestern Hunan, Shaoyang University, Shaoyang 422000, China
2
College of Information Engineering, Shaoyang University, Shaoyang 422000, China
*
Author to whom correspondence should be addressed.
Received: 24 February 2018 / Revised: 30 March 2018 / Accepted: 9 April 2018 / Published: 19 April 2018
(This article belongs to the Special Issue Computational Analysis for Protein Structure and Interaction)
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Abstract

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. View Full-Text
Keywords: SNO; random forest; fingerprints; information entropy; machine learning SNO; random forest; fingerprints; information entropy; machine learning
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Huang, G.; Li, J.; Zhao, C. Computational Prediction and Analysis of Associations between Small Molecules and Binding-Associated S-Nitrosylation Sites. Molecules 2018, 23, 954.

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