One of the major drawbacks in the functional analysis of olfactory receptors, other than experimental challenges, is that the mechanism by which an odor molecule interacts with the olfactory receptor is not known. To address this issue, which is a black-box (experimentally), computational methodologies are used, which themselves are fraught with technical difficulties, the primary difficulty being the lack of an experimentally derived structure of olfactory receptors. We will: I. Use computational methodologies to study the interactions between ORs and odor ligands in order to explore a predictive paradigm for these interactions by: 1) applying a rigorously OR protein model creating protocol;2) docking odor ligands within the binding region of the OR;3) performing molecular dynamics simulations of the interactions between the ORs and odor ligands.;and, 4) studying the electronic character of the binding region of OR to predict which odorant molecules are likely to interact with which ORs. II. Create a database, OR-ModelDB, which will house information related to computational models of ORs and interactions between OR and odors. This resource will leverage the database and web architecture of the successful ORDB and OdorDB and be housed within the SenseLab suite of databases at the Yale University School of Medicine. OR-ModelDB will serve as a worldwide repository for computational structural models of ORs. We will encourage other computational structural biologists to submit their published and evolving models in OR-ModelDB. During the period of support, we propose to test our above protocols on a human olfactory receptor OR17-210. This genomic pseudogene has been shown to interact with and is excited by several odor ligands. The PI led a study, which showed that this OR maintains function, is potentially important to OR-evolution among mammals and has sequence structural features not observed in other ORs. The methodologies developed and tested here will be extended to other ORs.
We propose to use computational simulation studies to study the long-range, dynamic interactions between olfactory receptors and odor ligands. We will also study the electronic character of OR binding pockets. We will develop Odor-ModelDB, a repository of computational structural models of ORs.
| Lai, Peter C; Guida, Brandon; Shi, Jing et al. (2014) Preferential binding of an odor within olfactory receptors: a precursor to receptor activation. Chem Senses 39:107-23 |
| Marenco, Luis N; Bahl, Gautam; Hyland, Lorra et al. (2013) Databases in SenseLab for the genomics, proteomics, and function of olfactory receptors. Methods Mol Biol 1003:3-22 |
| Crasto, Chiquito; Luo, Dajie; Yu, Feliciano et al. (2011) GenDrux: a biomedical literature search system to identify gene expression-based drug sensitivity in breast cancer. BMC Med Inform Decis Mak 11:28 |
| Crasto, Chiquito J (2010) Hydrophobicity profiles in G protein-coupled receptor transmembrane helical domains. J Receptor Ligand Channel Res 2010:123-133 |
| Crasto, Chiquito J (2009) Computational Biology of Olfactory Receptors. Curr Bioinform 4:8-15 |
