Prediction of protein-mannose binding sites using random forest

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Title	Prediction of protein-mannose binding sites using random forest
Authors	Harshvardan Khare¹, Vivek Ratnaparkhi¹, Sonali Chavan¹ & Valadi Jayraman²*
Affiliation	¹Bioinformatics centre, University of Pune, Pune, India; ²Centre for Development of Advanced Computing (C-DAC), Pune, India.
Email	vkjayaram@yahoo.com; *Corresponding author
Article Type	Hypothesis
Date	Received November 16, 2012; Accepted November 19, 2012; Published December 08, 2012
Abstract	Mannose is an abundant cell surface monosaccharide and has an important role in many biochemical processes. It binds to a great diversity of receptor proteins. In this study we have employed Random Forest for prediction of mannose binding sites. Mannose-binding site is taken to be a sphere around the centroid of the ligand and the sphere is subdivided into different layers and atom wise and residue wise features were extracted for each layer. The method achieves 95.59 % of accuracy using Random Forest with 10 fold cross validation. Prediction of mannose binding site analysis will be quite useful in drug design.
Keywords	Binding site prediction, Carbohydrate binding site prediction, Mannose binding site prediction, Machine learning, Random Forest.
Citation	*Khare et al.* Bioinformation 8(24): 1202-1205 (2012)
Edited by	P Kangueane
ISSN	0973-2063
Publisher	Biomedical Informatics
License	This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.