Open Access Open Badges Research article

A machine learning approach for the identification of odorant binding proteins from sequence-derived properties

Ganesan Pugalenthi1, Ke Tang12, PN Suganthan1, G Archunan3 and R Sowdhamini4*

Author Affiliations

1 School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore

2 Nature Inspired Computation and Applications Laboratory (NICAL), Department of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China

3 Department of Animal Science, Bharathidasan University Trichirapalli, Tamilnadu, 620 024, India

4 National Centre for Biological Sciences, UAS-GKVK campus, Bellary Road, Bangalore 560 065, India

For all author emails, please log on.

BMC Bioinformatics 2007, 8:351  doi:10.1186/1471-2105-8-351

Published: 19 September 2007



Odorant binding proteins (OBPs) are believed to shuttle odorants from the environment to the underlying odorant receptors, for which they could potentially serve as odorant presenters. Although several sequence based search methods have been exploited for protein family prediction, less effort has been devoted to the prediction of OBPs from sequence data and this area is more challenging due to poor sequence identity between these proteins.


In this paper, we propose a new algorithm that uses Regularized Least Squares Classifier (RLSC) in conjunction with multiple physicochemical properties of amino acids to predict odorant-binding proteins. The algorithm was applied to the dataset derived from Pfam and GenDiS database and we obtained overall prediction accuracy of 97.7% (94.5% and 98.4% for positive and negative classes respectively).


Our study suggests that RLSC is potentially useful for predicting the odorant binding proteins from sequence-derived properties irrespective of sequence similarity. Our method predicts 92.8% of 56 odorant binding proteins non-homologous to any protein in the swissprot database and 97.1% of the 414 independent dataset proteins, suggesting the usefulness of RLSC method for facilitating the prediction of odorant binding proteins from sequence information.