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

Background: 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. Results: 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 odorantbinding 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). Conclusion: Our study suggests that RLSC is potentially useful for predicting the odorant binding proteins from sequence-derived pro...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Ganesan Pugalenthi, E. Ke Tang, Ponnuthurai N. Suganthan, Govindaraju Archunan, Ramanathan Sowdhamini
• Keywords: BMCBI 2007, Odorant, Odorant Binding Proteins, Odorant Receptors