Recombinant Human Olfactory receptor 10R2 (OR10R2)

What is OR10R2 and what gene encodes it?

OR10R2 (olfactory receptor family 10 subfamily R member 2) is a member of the G-protein coupled receptor (GPCR) family that functions in olfactory sensory perception. The receptor is encoded by the OR10R2 gene, which is also sometimes referred to by alternative names including OR1-8 and OR10R2Q . As part of the olfactory receptor family, it belongs to the broader class A GPCR superfamily. These receptors are characterized by their seven-transmembrane domain structure and their role in converting chemical stimuli into cellular responses through G-protein signaling cascades.

What are the main structural features of OR10R2 as an olfactory receptor?

OR10R2 displays the canonical seven-transmembrane domain architecture characteristic of Class A GPCRs. Recent structural studies have revealed several important features:

• The presence of conserved acidic residues, particularly D69^2.50 and E110^3.39, which form a sodium binding site critical for stabilizing the inactive conformation .

• Specific transmembrane helical arrangements, particularly between TM6 and TM7, that appear to be important for receptor function .

• Key residues in TM6 (including S258^6.55 and R262^6.59) that are involved in ligand binding, as determined by both computational models and experimental data .

The structural stability of the inactive state depends significantly on the binding of sodium ions at a specific pocket formed by these conserved acidic residues, a feature that appears to be shared among many human olfactory receptors .

How is recombinant OR10R2 typically produced for research applications?

Recombinant OR10R2 is typically expressed in bacterial expression systems, with E. coli being the predominant host organism . The production process generally follows these methodological steps:

• Gene optimization: The OR10R2 gene sequence is often codon-optimized for bacterial expression.

• Vector construction: The optimized sequence is cloned into an expression vector containing appropriate regulatory elements.

• Transformation and expression: The construct is introduced into E. coli cells, followed by induction of protein expression.

• Purification: The expressed protein is typically purified using affinity chromatography.

• Formulation: The purified protein is typically stored in a liquid formulation containing glycerol to enhance stability .

For optimal storage stability, recombinant OR10R2 should be stored at -20°C, with extended storage at -80°C recommended. Repeated freeze-thaw cycles should be avoided, and working aliquots can be maintained at 4°C for up to one week .

What antibodies are available for OR10R2 detection and what applications are they validated for?

Several OR10R2 antibodies are commercially available with different validation profiles:

When selecting antibodies for experimental work, researchers should consider the specific application needs and cross-reactivity requirements . For optimal results, it is recommended to validate antibodies in your specific experimental system even if they have been previously validated for particular applications.

How have machine learning approaches advanced our understanding of OR10R2 structure?

Recent advances in machine learning (ML) have revolutionized the structural investigation of olfactory receptors, including OR10R2. The development of tools like AlphaFold2 has enabled researchers to generate de novo structural predictions with unprecedented accuracy . Key advancements include:

• Multiple prediction algorithms: Different ML approaches (AlphaFold, AlphaFold-MultiState, OpenFold, RoseTTAFold, and SwissModel) have been used to generate structural models of olfactory receptors .

• Comparative accuracy: Studies have shown that these algorithms produce structures with varying levels of accuracy, particularly when predicting different conformational states (active vs. inactive) .

• Structural refinement: Molecular dynamics simulations have proven essential for refining and validating ML-predicted structures, revealing important features such as the necessity of sodium ions for stabilizing the inactive state .

• State-specific prediction: Specialized versions of prediction algorithms (like AlphaFold-MultiState) trained on state-specific datasets have improved the accuracy of predicting distinct conformational states .

These computational approaches have been particularly valuable for olfactory receptors like OR10R2, which have historically been challenging to study using traditional structural biology methods due to their membrane protein nature.

What is the role of sodium ions in OR10R2 structure and function?

Molecular dynamics simulations and computational modeling have revealed that sodium ions play a critical role in stabilizing the inactive conformation of OR10R2 . Specifically:

• Sodium binds at a pocket formed by two conserved negatively charged residues: D69^2.50 and E110^3.39 .

• The D2.50 position is a known sodium binding site in class A GPCRs, while the E3.39 position is unusual - this position is typically occupied by serine in non-olfactory class A GPCRs .

• Remarkably, approximately 93% of human olfactory receptors contain aspartic acid or glutamic acid at both positions 2.50 and 3.39, suggesting evolutionary conservation of this sodium-binding feature .

• Computational simulations demonstrate that removal of the sodium ion leads to significant structural instability, indicating its essential role in maintaining receptor conformation .

• The presence of sodium at this position has been independently predicted by AlphaFill, an ML-based protein-ligand binding predictor .

This sodium-binding property appears to be a distinguishing feature of olfactory receptors and is crucial for proper folding and function in the inactive state.

How do different computational prediction methods compare for OR10R2 structural modeling?

Various computational approaches have been employed to model OR10R2 structure, with notable differences in their predictions:

Comparison of these methods reveals that:

What methodological challenges exist in studying OR10R2 and how can they be addressed?

Research on OR10R2 and other olfactory receptors faces several methodological challenges:

These challenges can be addressed through integrated research strategies combining computational predictions, molecular dynamics simulations, experimental structural biology, and functional assays.

How can OR10R2 research contribute to our understanding of olfactory signaling mechanisms?

OR10R2 research provides valuable insights into several aspects of olfactory signaling:

• Receptor activation mechanisms: Studying the conformational changes between inactive (sodium-bound) and active states of OR10R2 illuminates the molecular mechanisms of olfactory signal transduction.

• Ligand recognition diversity: Understanding the structural basis for ligand binding in OR10R2 contributes to explaining how the olfactory system recognizes thousands of different odorants with a limited number of receptors.

• Evolutionary adaptations: The conserved sodium-binding site featuring acidic residues at positions 2.50 and 3.39 across 93% of human ORs suggests evolutionary adaptation specific to olfactory function .

• Structural biology advancements: The ability to accurately model OR10R2 structure using ML-based approaches paves the way for similar studies across the approximately 400 members of the human olfactory receptor family .

What recent technological developments have advanced OR10R2 research?

Several technological breakthroughs have accelerated research on OR10R2 and other olfactory receptors:

• Machine learning structural prediction: Tools like AlphaFold2 and its specialized variants have enabled accurate de novo structural predictions, addressing the historical difficulty of obtaining experimental structures .

• Molecular dynamics simulation refinement: Advanced simulation techniques have proven essential for refining and validating predicted structures, particularly for identifying features like ion binding sites .

• Cryo-EM advancements: Recent successes in obtaining experimental structures of olfactory receptors by cryo-EM have provided crucial validation points for computational models .

• Proteochemometric modeling: The development of PCM approaches combining protein sequence and ligand feature information has improved the prediction of OR-ligand interactions .

• Integrated structural biology: Combining computational predictions, simulations, experimental structures, and functional assays has created a more comprehensive understanding of OR10R2 biology.