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Membrane receptor for odour-binding proteins.

ABSTRACT: Specific binding of 125I-labelled bovine odour-binding protein (OBP) to isolated membranes from nasal mucosa was demonstrated. The interaction reached equilibrium within 30 min at 37 degrees C and was reversible. A Scatchard analysis of the equilibrium binding revealed a single population of binding sites, with the calculated equilibrium dissociation constant and maximum number of binding sites being 2.25 +/- 0.5 microM and 18.5 +/- 2 pmol/mg of membrane protein respectively (n = 2). Receptor activity was decreased on digestion by trypsin, proteinase K or endoglycosidase H, was heat labile and was sensitive to thiol-group-specific reagents. With the exception of rat and mouse major urinary proteins, which exhibit a high degree of structural similarity with OBP and bind similar ligands, other members of the lipocalin family, such as retinol-binding protein and beta-lactoglobulin, failed to inhibit the binding of 125I-labelled OBP to its receptor. The receptor seems not to be restricted to olfactory tissues, as it was detected in a variety of other tissues. This suggests that OBP is unlikely to play a role only in olfactory signal transduction. It might have a much broader role within the body; possibilities include a role in detoxification or signalling.

SUBMITTER: Boudjelal M

PROVIDER: S-EPMC1217468 | biostudies-other | 1996 Jul

REPOSITORIES: biostudies-other

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Project description:BACKGROUND: Many integral membrane proteins, like their non-membrane counterparts, form either transient or permanent multi-subunit complexes in order to carry out their biochemical function. Computational methods that provide structural details of these interactions are needed since, despite their importance, relatively few structures of membrane protein complexes are available. RESULTS: We present a method for predicting which residues are in protein-protein binding sites within the transmembrane regions of membrane proteins. The method uses a Random Forest classifier trained on residue type distributions and evolutionary conservation for individual surface residues, followed by spatial averaging of the residue scores. The prediction accuracy achieved for membrane proteins is comparable to that for non-membrane proteins. Also, like previous results for non-membrane proteins, the accuracy is significantly higher for residues distant from the binding site boundary. Furthermore, a predictor trained on non-membrane proteins was found to yield poor accuracy on membrane proteins, as expected from the different distribution of surface residue types between the two classes of proteins. Thus, although the same procedure can be used to predict binding sites in membrane and non-membrane proteins, separate predictors trained on each class of proteins are required. Finally, the contribution of each residue property to the overall prediction accuracy is analyzed and prediction examples are discussed. CONCLUSION: Given a membrane protein structure and a multiple alignment of related sequences, the presented method gives a prioritized list of which surface residues participate in intramembrane protein-protein interactions. The method has potential applications in guiding the experimental verification of membrane protein interactions, structure-based drug discovery, and also in constraining the search space for computational methods, such as protein docking or threading, that predict membrane protein complex structures.

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Membrane receptor for odour-binding proteins.

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