Odorant receptor specificities and receptor combinatorials: implications for olfactory coding.
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Percy Luu
Publications and source records attributed to Percy Luu.
Molecular determinants of ligand selectivity in a vertebrate odorant receptor.
The identification of the chemical structure of an odorant by the vertebrate olfactory system is thought to occur through the combinatorial activity from multiple receptors, each tuned to recognize different chemical features. What are the molecular determinants underlying the selectivity of individual odorant receptors for their cognate ligands? To address this question, we performed molecular modeling and site-directed mutagenesis on the ligand-binding region of two orthologous amino acid odorant receptors belonging to the "C family" of G-protein-coupled receptors in goldfish and zebrafish. We identified the critical ligand-receptor interactions that afford ligand binding as well as selectivity for different amino acids. Moreover, predictions regarding binding pocket structure allowed us to alter, in a predictable manner, the receptor preferences for different ligands. These results reveal how this class of odorant receptor has evolved to accommodate ligands of varying chemical structure and further illuminate the molecular principles underlying ligand recognition and selectivity in this family of chemosensory receptors.
Amino Acids↗
Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation.
There are many sources of systematic variation in cDNA microarray experiments which affect the measured gene expression levels (e.g. differences in labeling efficiency between the two fluorescent dyes). The term normalization refers to the process of removing such variation. A constant adjustment is often used to force the distribution of the intensity log ratios to have a median of zero for each slide. However, such global normalization approaches are not adequate in situations where dye biases can depend on spot overall intensity and/or spatial location within the array. This article proposes normalization methods that are based on robust local regression and account for intensity and spatial dependence in dye biases for different types of cDNA microarray experiments. The selection of appropriate controls for normalization is discussed and a novel set of controls (microarray sample pool, MSP) is introduced to aid in intensity-dependent normalization. Lastly, to allow for comparisons of expression levels across slides, a robust method based on maximum likelihood estimation is proposed to adjust for scale differences among slides.
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