SUMOhydro: a novel method for the prediction of sumoylation sites based on hydrophobic properties

Sumoylation is one of the most essential mechanisms of reversible protein post-translational modifications and is a crucial biochemical process in the regulation of a variety of important biological functions. Sumoylation is also closely involved in various human diseases. The accurate computational...

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Bibliographic Details
Published in:PloS one 2012-06, Vol.7 (6), p.e39195-e39195
Main Authors: Chen, Yong-Zi, Chen, Zhen, Gong, Yu-Ai, Ying, Guoguang
Format: Article
Language:English
Subjects:
Alzheimer's disease
Alzheimers disease
Amino Acid Sequence
Amino acids
Analysis
Aqueous solutions
Artificial intelligence
Binding Sites
Bioinformatics
Biology
Cancer
Computer applications
Correlation coefficient
Correlation coefficients
Disease prevention
Experimental design
Gene expression
Hospitals
Hydrophobicity
Laboratories
Localization
Methods
Molecular Sequence Data
Phosphorylation
Post-translation
ROC Curve
Signal transduction
Small Ubiquitin-Related Modifier Proteins - metabolism
Statistical analysis
Studies
SUMO protein
Sumoylation
Support Vector Machine
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Summary:Sumoylation is one of the most essential mechanisms of reversible protein post-translational modifications and is a crucial biochemical process in the regulation of a variety of important biological functions. Sumoylation is also closely involved in various human diseases. The accurate computational identification of sumoylation sites in protein sequences aids in experimental design and mechanistic research in cellular biology. In this study, we introduced amino acid hydrophobicity as a parameter into a traditional binary encoding scheme and developed a novel sumoylation site prediction tool termed SUMOhydro. With the assistance of a support vector machine, the proposed method was trained and tested using a stringent non-redundant sumoylation dataset. In a leave-one-out cross-validation, the proposed method yielded an excellent performance with a correlation coefficient, specificity, sensitivity and accuracy equal to 0.690, 98.6%, 71.1% and 97.5%, respectively. In addition, SUMOhydro has been benchmarked against previously described predictors based on an independent dataset, thereby suggesting that the introduction of hydrophobicity as an additional parameter could assist in the prediction of sumoylation sites. Currently, SUMOhydro is freely accessible at http://protein.cau.edu.cn/others/SUMOhydro/.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0039195