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

Pn Suganthan,R Sowdhamini,G Archunan,Ke Tang,Ganesan Pugalenthi

doi:10.1186/1471-2105-8-351

Abstract

BackgroundOdorant 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.ResultsIn 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).ConclusionOur 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.

Highlights

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
The results show that our method can distinguish odorant binding proteins from other protein sequences with an accuracy of >90% and Matthews Correlation Coefficient (MCC) of 0.922, when evaluated through leave one out cross validation
Overall prediction accuracy of 97.7% (94.5% and 98.4% for positive and negative classes respectively) shows that regularized least squares classifier (RLSC) is a potentially useful tool for the prediction of odorant-binding proteins

Summary

Introduction

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. Olfaction is an important process to establish behavioural response and involves the binding of small, hydrophobic, volatile molecules to receptors of the nasal neuroepithelia [1]. The first step in olfaction is the solubilization of the hydrophobic odorants in the hydrophilic nasal mucus. Odorant Binding Proteins (OBPs) play a vital role in the olfaction. OBPs are small soluble polypeptides, which are thought to act as a carrier for odorants and carries odorant from the environment to the nasal epithelium in vertebrates and sensillar lymph in insects [5,6].

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Results

Conclusion