OR5R1 Antibody

What is OR5R1 and what experimental systems express this protein?

OR5R1 (Olfactory receptor 5R1) is a member of the G-protein coupled receptor 1 family with a calculated molecular weight of approximately 37 kDa . It functions as an odorant receptor . OR5R1 is primarily expressed in olfactory sensory neurons in the main olfactory epithelium. While primarily studied in human samples, research suggests potential expression patterns in other mammalian species, though cross-species reactivity must be experimentally validated for each antibody .

What are the key applications for OR5R1 antibodies in research?

OR5R1 antibodies are primarily utilized in Western blot (WB) and ELISA applications . Based on studies with related olfactory receptor antibodies, they may also be suitable for immunofluorescence (IF) and immunocytochemistry (ICC) when properly validated . When designing experiments, researchers should consider that most commercial OR5R1 antibodies are polyclonal and derived from synthetic peptide immunogens corresponding to specific amino acid regions of the human OR5R1 protein .

What validation approaches should be employed before using OR5R1 antibodies?

Prior to extensive experimental use, validation should include:

• Positive and negative control samples (tissues known to express/not express OR5R1)

• Peptide blocking experiments to confirm specificity

• Multiple antibody approach (using antibodies targeting different epitopes)

• Knockout/knockdown controls when available

As demonstrated with other olfactory receptor antibodies, proper validation can significantly improve experimental reliability . Researchers should be aware that the literature on olfactory receptor antibodies indicates that only 20-30% of commercially available antibodies may be informative in immunohistochemical analyses .

How can researchers optimize Western blot protocols for OR5R1 detection?

For optimal Western blot detection of OR5R1:

When troubleshooting, researchers should consider that OR5R1, like other olfactory receptors, is a membrane protein that may form aggregates during sample preparation, potentially appearing at higher molecular weights than calculated .

What are the critical factors affecting immunofluorescence results with OR5R1 antibodies?

Based on studies with related olfactory receptor antibodies, successful immunofluorescence detection requires attention to:

• Antigen retrieval methods: Test multiple approaches (heat-induced, enzymatic) as demonstrated necessary for other olfactory receptors .

• Fixation protocol: Paraformaldehyde (PFA) fixation is generally recommended; freshly prepared PFA is crucial as stored PFA can convert to formalin .

• Permeabilization: Optimize detergent concentration (0.1-0.3% Triton X-100) for membrane penetration without epitope destruction.

• Dilution optimization: Test a range (typically 1:200-1:1000) as recommended for related olfactory receptor antibodies .

• Incubation conditions: Longer incubation times (overnight at 4°C) often yield better results than shorter incubations at room temperature.

How can researchers address cross-reactivity concerns with OR5R1 antibodies?

Cross-reactivity is a significant concern with olfactory receptor antibodies due to sequence homology within this large receptor family. Strategies to address this include:

• Epitope analysis: Compare the immunogen sequence against other olfactory receptors to predict potential cross-reactivity.

• Absorption controls: Pre-incubate antibody with the immunizing peptide to confirm signal specificity.

• Multiple antibody approach: Use antibodies recognizing different epitopes of OR5R1.

• Orthogonal methods: Confirm protein expression with nucleic acid-based methods (RT-PCR, RNA-Seq).

• Knockout validation: When available, use tissues from OR5R1 knockout models as negative controls, though this approach may be challenging given limited availability of such models .

What should researchers consider when using OR5R1 antibodies across species?

When working with OR5R1 antibodies across different species:

• Most commercial OR5R1 antibodies are raised against human sequences, though some may claim reactivity with mouse and rat samples .

• Sequence alignment analysis should be performed between the immunogen and the target species' OR5R1 sequence.

• Empirical validation is essential, as demonstrated in studies with other olfactory receptor antibodies where cross-reactivity predictions based on sequence alone were insufficient .

• Consider that the utility of antibodies may vary across applications even within the same species - an antibody that works for Western blot may not work for immunohistochemistry .

What experimental controls are essential when using OR5R1 antibodies in unpublished applications?

When extending OR5R1 antibody use to new applications or tissues:

• Positive expression control: Include samples with confirmed OR5R1 expression.

• Negative expression control: Include samples without OR5R1 expression.

• Antibody controls:

  • Primary antibody omission

  • Isotype control (matching the host species and antibody class)

  • Blocking peptide competition

• Signal specificity controls:

  • Decreasing antibody concentration should reduce signal proportionally

  • Signal localization should match the expected subcellular distribution of OR5R1

• Reproducibility controls: Confirm findings with multiple antibody lots and/or different antibodies targeting the same protein .

What approaches are recommended for quantifying OR5R1 expression in tissue samples?

For accurate quantification of OR5R1 expression:

• Western blot quantification:

  • Use housekeeping proteins appropriate for the tissue type

  • Include standard curves from recombinant OR5R1 when available

  • Employ digital image analysis with appropriate background subtraction

  • Validate linearity of signal across a range of protein concentrations

• Immunohistochemistry quantification:

  • Use automated image analysis with consistent thresholding

  • Quantify by percentage of positive cells or mean fluorescence intensity

  • Include reference standards across experiments

  • Account for tissue autofluorescence, particularly in olfactory tissues

• ELISA-based approaches:

  • Consider sandwich ELISA when two antibodies recognizing different epitopes are available

  • Include standard curves with recombinant protein

  • Validate with spike-and-recovery experiments

How should researchers interpret contradictory results between antibody-based and RNA-based detection methods?

When facing discrepancies between protein and RNA detection:

• Consider that protein and mRNA levels may not correlate due to post-transcriptional regulation or protein stability differences.

• Evaluate antibody specificity using additional validation methods.

• For olfactory receptors specifically, consider that antibody access to epitopes may be limited by protein conformation or interaction with other membrane components .

• Assess whether the epitope could be masked by post-translational modifications or protein-protein interactions.

• Employ orthogonal protein detection methods such as mass spectrometry when available.

What are the considerations for co-immunoprecipitation studies with OR5R1 antibodies?

For co-immunoprecipitation of OR5R1 and potential interaction partners:

• Membrane protein solubilization:

  • Test multiple detergents (CHAPS, DDM, digitonin) at various concentrations

  • Optimize solubilization conditions to maintain protein-protein interactions

  • Consider crosslinking approaches for transient interactions

• Antibody selection:

  • Evaluate antibody binding efficiency in the IP buffer system

  • Consider epitope accessibility in the native protein conformation

  • Test both direct antibody coupling to beads and protein A/G approaches

• Controls:

  • Include IgG control from the same species as the antibody

  • Perform reverse co-IP when antibodies to interaction partners are available

  • Include input controls representing a defined percentage of starting material

How can machine learning approaches enhance antibody specificity prediction and experimental design?

Recent advances in computational approaches offer new possibilities:

• Epitope prediction: Computational tools can predict potentially immunogenic regions that might result in more specific antibodies .

• Cross-reactivity prediction: Machine learning models trained on antibody-antigen binding data can predict potential cross-reactivity with related proteins .

• Active learning for experimental design: As described in recent literature (2025), active learning strategies can significantly improve experimental efficiency in antibody-antigen binding prediction, potentially reducing the number of required experiments by up to 35% .

• Customized specificity profiles: Advanced computational approaches now enable the design of antibodies with customized specificity profiles, either with specific high affinity for particular targets or with cross-specificity for multiple target ligands .

• Out-of-distribution prediction: Machine learning models can help predict antibody behavior in experimental contexts not represented in training data, addressing a key challenge in antibody research .