Next Article in Journal
Formation of Sulforaphane and Iberin Products from Thai Cabbage Fermented by Myrosinase-Positive Bacteria
Next Article in Special Issue
Regularized Multi-View Subspace Clustering for Common Modules Across Cancer Stages
Previous Article in Journal
Search for Partner Proteins of A. thaliana Immunophilins Involved in the Control of Plant Immunity
Previous Article in Special Issue
Prediction of Protein-Protein Interactions from Amino Acid Sequences Based on Continuous and Discrete Wavelet Transform Features
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessArticle
Molecules 2018, 23(4), 954;

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

Provincial Key Laboratory of Informational Service for Rural Area of Southwestern Hunan, Shaoyang University, Shaoyang 422000, China
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)
Full-Text   |   PDF [7770 KB, uploaded 3 May 2018]   |  


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

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Supplementary material


Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Molecules EISSN 1420-3049 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top