| dc.description.abstract |
<p>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.</p><p>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.</p>
|
|
