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Molecules 2017, 22(11), 1891; doi:10.3390/molecules22111891

Glypre: In Silico Prediction of Protein Glycation Sites by Fusing Multiple Features and Support Vector Machine

1
School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China
2
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
3
School of Computer Engineering, Weifang University, Weifang 261061, China
*
Author to whom correspondence should be addressed.
Received: 20 September 2017 / Accepted: 26 October 2017 / Published: 3 November 2017
(This article belongs to the Special Issue Computational Analysis for Protein Structure and Interaction)
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Abstract

Glycation is a non-enzymatic process occurring inside or outside the host body by attaching a sugar molecule to a protein or lipid molecule. It is an important form of post-translational modification (PTM), which impairs the function and changes the characteristics of the proteins so that the identification of the glycation sites may provide some useful guidelines to understand various biological functions of proteins. In this study, we proposed an accurate prediction tool, named Glypre, for lysine glycation. Firstly, we used multiple informative features to encode the peptides. These features included the position scoring function, secondary structure, AAindex, and the composition of k-spaced amino acid pairs. Secondly, the distribution of distinctive features of the residues surrounding the glycation and non-glycation sites was statistically analysed. Thirdly, based on the distribution of these features, we developed a new predictor by using different optimal window sizes for different properties and a two-step feature selection method, which utilized the maximum relevance minimum redundancy method followed by a greedy feature selection procedure. The performance of Glypre was measured with a sensitivity of 57.47%, a specificity of 90.78%, an accuracy of 79.68%, area under the receiver-operating characteristic (ROC) curve (AUC) of 0.86, and a Matthews’s correlation coefficient (MCC) of 0.52 by 10-fold cross-validation. The detailed analysis results showed that our predictor may play a complementary role to other existing methods for identifying protein lysine glycation. The source code and datasets of the Glypre are available in the Supplementary File. View Full-Text
Keywords: glycation sites; support vector machine; feature analysis glycation sites; support vector machine; feature analysis
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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).

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MDPI and ACS Style

Zhao, X.; Zhao, X.; Bao, L.; Zhang, Y.; Dai, J.; Yin, M. Glypre: In Silico Prediction of Protein Glycation Sites by Fusing Multiple Features and Support Vector Machine. Molecules 2017, 22, 1891.

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