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Predicting sumoylation site by feature selection method.

Abstract：

:The small ubiquitin-like modifier (SUMO) proteins are a kind of proteins that can be attached to a series of proteins. The sumoylation of protein is an important posttranslational modification. Thus, the prediction of the sumoylation site of a given protein is significant. Here we employed a combined method to perform this task. We predicted the sumoylation site of a protein by a two-staged procedure. At the first stage, whether a protein would be sumoylated was predicted; whereas at the second stage, the sumoylation sites of the protein were predicted if it was determined to be modified by SUMO at the first stage. At the first stage, we encoded a protein with protein families (PFAM) and trained the predictor with nearest network algorithm (NNA); at the second stage, we encoded nonapeptides (peptides that contain nine residues) of the protein containing the lysine residues, with Amino Acid Index, and trained the predictor with NNA. The predictor was tested by the k-fold cross-validation method. The highest accuracy of the second-staged predictor was 99.55% when 12 features were incorporated in the predictor. The corresponding Matthews Correlation Coefficient was 0.7952. These results indicate that the method is a promising tool to predict the sumoylation site of a protein. At last, the features used in the predictor are discussed. The software is available at request.

journal_name

J Biomol Struct Dyn

journal_title

Journal of biomolecular structure & dynamics

authors

Cai Y,He J,Lu L

doi

10.1080/07391102.2011.10508607

subject

Has Abstract

pub_date

2011-04-01 00:00:00

pages

797-804

issue

5

eissn

0739-1102

issn

1538-0254

pii

c4309/Predicting-Sumoylation-Site-by-Feature-Selec

journal_volume

28

pub_type

杂志文章

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