Predicting Ligand Selectivity of Mammalian Odorant Receptors
The mammalian olfactory system uses a large family of odorant receptors to detect and discriminate amongst a myriad of volatile odor molecules. The odorant receptors are similar in protein sequence, but their ligand selectivities dramatically differ. It is not clear how the protein sequences determine the responsiveness of odorant receptors. In this study, I attempt to establish the link between the protein sequences of odorant receptors and their ligand selectivity.
Starting from the response profiles of hundreds of mouse odorant receptors to an odorant generated from my previous work, I used machine learning and variable selection methods to identify properties of amino acid residues that predict receptor response. This leads to protein sequence-based models for odorant receptor response prediction. The models trained with mouse odorant receptor data can predict human odorant receptor responses.
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