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J Theor Biol. 2014 Mar 7;344:78-87. doi: 10.1016/j.jtbi.2013.11.012. Epub 2013 Nov 27.

Prediction of posttranslational modification sites from amino acid sequences with kernel methods.

Author information

1
Department of Information and Computer Science, University of Science and Technology Beijing, Beijing 100083, China.
2
Department of Applied Mathematics, College of Science, China Agricultural University, Beijing 10083, China.
3
Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing 100190, China.
4
Department of Mathematics and Information Science, BinZhou University, BinZhou 256603, China.
5
Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.
6
Department of Applied Mathematics, College of Science, China Agricultural University, Beijing 10083, China. Electronic address: dengnaiyang@cau.edu.cn.

Abstract

Post-translational modification (PTM) is the chemical modification of a protein after its translation and one of the later steps in protein biosynthesis for many proteins. It plays an important role which modifies the end product of gene expression and contributes to biological processes and diseased conditions. However, the experimental methods for identifying PTM sites are both costly and time-consuming. Hence computational methods are highly desired. In this work, a novel encoding method PSPM (position-specific propensity matrices) is developed. Then a support vector machine (SVM) with the kernel matrix computed by PSPM is applied to predict the PTM sites. The experimental results indicate that the performance of new method is better or comparable with the existing methods. Therefore, the new method is a useful computational resource for the identification of PTM sites. A unified standalone software PTMPred is developed. It can be used to predict all types of PTM sites if the user provides the training datasets. The software can be freely downloaded from http://www.aporc.org/doc/wiki/PTMPred.

KEYWORDS:

Kinase-specific; O-glycosylation; Phosphorylation; Support vector machine

PMID:
24291233
DOI:
10.1016/j.jtbi.2013.11.012
[Indexed for MEDLINE]

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