A machine learning approach for the identification of odorant binding proteins from sequence-derived properties |
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| Authors: | Ganesan Pugalenthi Ke Tang PN Suganthan G Archunan R Sowdhamini |
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| Institution: | (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, Trichirapalli, Tamilnadu, 620 024, India;(4) National Centre for Biological Sciences, UAS-GKVK campus, Bellary Road, Bangalore, 560 065, India |
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| Abstract: | 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. |
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