Table of Contents
ISRN Bioinformatics
Volume 2013, Article ID 671269, 11 pages
http://dx.doi.org/10.1155/2013/671269
Research Article

SUMOhunt: Combining Spatial Staging between Lysine and SUMO with Random Forests to Predict SUMOylation

National Institute of Biotechnology and Genetic Engineering, P.O. Box 577, Jhang Road, Faisalabad, Pakistan

Received 24 April 2013; Accepted 28 May 2013

Academic Editors: A. Lukas, A. Pulvirenti, J. T. L. Wang, and K. Yura

Copyright © 2013 Amna Ijaz. 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.

Abstract

Modification with SUMO protein has many key roles in eukaryotic systems which renders the identification of its target proteins and sites of considerable importance. Information regarding the SUMOylation of a protein may tell us about its subcellular localization, function, and spatial orientation. This modification occurs at particular and not all lysine residues in a given protein. In competition with biochemical means of modified-site recognition, computational methods are strong contenders in the prediction of SUMOylation-undergoing sites on proteins. In this research, physicochemical properties of amino acids retrieved from AAIndex, especially those involved in docking of modifier and target proteins and optimal presentation of target lysine, in combination with sequence information and random forest-based classifier presented in WEKA have been used to develop a prediction model, SUMOhunt, with statistics significantly better than all previous predictors. In this model 97.56% accuracy, 100% sensitivity, 94% specificity, and 0.95 MCC have been achieved which shows that proposed amino acid properties have a significant role in SUMO attachment. SUMOhunt will hence bring great reliability and efficiency in SUMOylation prediction.