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Published September 2004 | public
Journal Article

Predicted 3-D Structures for Mouse I7 and Rat I7 Olfactory Receptors and Comparison of Predicted Odor Recognition Profiles with Experiment

Abstract

The first step in the perception of an odor is the activation of one or more olfactory receptors (ORs) following binding of the odorant molecule to the OR. In order to initiate the process of determining how the molecular level receptor-odorant interactions are related to odor perception, we used the MembStruk computational method to predict the three-dimensional (3-D) structure of the I7 OR for both mouse and rat. We then used the HierDock ligand docking computational method to predict the binding site and binding energy for the library of 56 odorants to these receptors for which experiment response data are now available. We find that the predicted 3-D structures of the mouse and rat I7 OR lead to predictions of odorant binding that are in good agreement with the experimental results, thus validating the accuracy of both the 3-D structure and the predicted binding site. In particular we predict that heptanal and octanal both bind strongly to both mouse and rat I7 ORs, which conflicts with the older literature but agrees with recent experiments. To provide the basis of additional validations of our 3-D structures, we also report the odorant binding site for a new odorant (8-hydroxy-octanal) with a novel functionality designed to bind strongly to mouse I7. Such validated computational methods should be very useful in predicting the structure and function of many other ORs.

Additional Information

© 2004 Oxford University Press. Accepted June 17, 2004. We thank Dr Thomas Bozza and Professor Peter Mombaerts of Rockefeller University for their suggestion of the blind test on I7 and their assistance in this collaboration. Our special thanks to Dr Thomas Bozza for conducting some competitive assay experiments proposed by us. This work was initiated with support by ARO-MURI (Dr Robert Campbell) with some of the HierDock work supported by NIH (BRGRO1 and GM625523). The computational facilities used here were supported by an IBM-SUR grant and by an ARO-DURIP grant. Other facilities of the Materials and Process Simulation Center used in this project are supported also by DOE (ASCI ASAP), General Motors, ChevronTexaco, ONR, NSF (CHE and MRI), ARO, ONR, Beckman Institute, Seiko-Epson, NIH HD and Asahi Kasei.

Additional details

Created:
August 22, 2023
Modified:
October 24, 2023