The HA-tag corresponds to amino acids 98-106 of the HA protein (sequence: YPYDVPDYA) and is typically appended to the N- or C-terminus of target proteins. Its DNA sequence is codon-optimized for expression in eukaryotic systems:
Forward strand: 5'-TAC-CCA-TAC-GAT-GTT-CCA-GAT-TAC-GCT-3'
Reverse strand: 5'-TAT-CCA-TAT-GAT-GTT-CCA-GAT-TAT-GCT-3'
| Amino Acid | Position | Role |
|---|---|---|
| YPYDVPDYA | 98-106 | Antibody binding epitope |
Example: HEK-293T cells transfected with HA-tagged Histone H3 showed a ~17 kDa band using HA Tag Monoclonal Antibody (clone 2-2.2.14) .
Protocol: SDS-PAGE separation followed by membrane transfer and chemiluminescent detection .
Use Case: Identifying protein-protein interactions (e.g., co-IP of HA-tagged proteins with binding partners) .
Localization: HA-tagged proteins in HEK-293 cells (e.g., Histone H3) are visualized using Alexa Fluor 488-conjugated secondary antibodies .
HA-tagged proteins are cleaved by Caspase-3/7 during apoptosis (DVPD site), reducing immunoreactivity .
CRISPR-mediated HA-tagging of endogenous proteins (e.g., neurons) enables real-time tracking without overexpression .
Small size (9 amino acids) minimizes interference with protein function.
Commercial availability of validated antibodies ensures reproducibility .
The HA-tag corresponds to amino acid residues YPYDVPDYA derived from the human influenza virus hemagglutinin (HA) protein . This tag is widely used in biotechnology because:
It is relatively small and typically doesn't interfere with the bioactivity or biodistribution of recombinant proteins
It can be positioned at either the N-terminus, C-terminus, or internally within a target protein
Several inexpensive anti-HA antibodies are commercially available
The tag facilitates detection, isolation, and purification of proteins without requiring protein-specific antibodies, making it an economical and versatile tool for protein studies .
HA-tag antibodies have been validated for multiple applications in molecular and cellular biology:
Cited applications in research include 657 publications for Western blot and 189 publications for immunoprecipitation with a single antibody (51064-2-AP) .
The choice depends on your experimental requirements:
Monoclonal Antibodies (e.g., clone 912426 , 2-2.2.14 ):
Advantages: High specificity to a single epitope, consistent lot-to-lot performance, lower background
Best for: Quantitative experiments, applications requiring high reproducibility
Example: Mouse Anti-HA Peptide Monoclonal Antibody (MAB6875) detects specific bands for HA-tagged proteins at expected molecular weights with minimal background
Polyclonal Antibodies (e.g., AHP1075 , 51064-2-AP ):
Advantages: Recognize multiple epitopes, potentially higher sensitivity
Best for: Applications where signal amplification is needed
Example: Rabbit anti-HA-Tag polyclonal has been tested against both the immunogen and recombinant proteins containing the HA sequence, showing recognition regardless of tag position
Optimizing Western blot detection requires systematic protocol adjustments:
Sample Preparation:
Use proper lysis buffers based on protein localization (membrane, cytosolic, nuclear)
Include protease inhibitors to prevent degradation
For difficult proteins, consider specialized buffers
Antibody Selection and Dilution:
Membrane Type:
Blocking Conditions:
Detection System:
Chemiluminescence works well for most applications
For quantitative analysis, consider fluorescent secondary antibodies
Based on research protocols, an effective HA-tag competition assay can be designed as follows :
Assay Principle:
The competition assay relies on the principle that each in vitro translated peptide or protein containing a single HA tag should interact with the anti-HA antibody with equal affinity
This enables quantification independent of the sequence N-terminal to the HA tag
Materials Required:
Synthetic fluorescently labeled HA peptide (signal generator)
Unlabeled synthetic HA peptide (for standard curve)
Anti-HA antibody
In vitro translated samples with HA tags
Appropriate controls (e.g., samples lacking HA tag)
Protocol Outline:
Generate signals using synthetic fluoresceinated HA peptide
Create a standard curve using unlabeled synthetic peptide as competitor
Prepare serial dilutions of samples (e.g., 1:10 followed by four 1:2 dilutions)
Normalize matrix effects by diluting all samples and standards in the same solution
Measure signal reduction as a function of HA-tagged protein concentration
Validation:
Research-validated immunofluorescence protocols follow these steps :
Cell Preparation:
Culture cells on coverslips or in chamber slides
Transfect with HA-tagged construct (allow 24-48 hours for expression)
Fixation Options:
Permeabilization:
0.1% Triton X-100 for 15 minutes at room temperature
Blocking:
2% BSA for 1 hour at room temperature
Antibody Incubation:
Primary: Anti-HA antibody at 2-8 μg/mL in 0.1% BSA, overnight at 4°C
Secondary: Fluorophore-conjugated secondary antibody (e.g., Alexa Fluor 488) at 1:2,000 dilution for 45 minutes at room temperature
Counterstaining:
Nuclei: DAPI (blue)
Optional: F-actin with phalloidin (red)
Mounting and Imaging:
Mount with antifade mounting medium
Image at 60× magnification
Controls:
Untransfected cells
Secondary antibody only (no primary) to assess background
Multiple bands or high background can result from several factors:
A customer review noted: "This antibody recognized the HA-tagged proteins, but also got a lot of non-specific bands" when used at 1:2000 dilution . Another review states: "No background observed and clean bands" when following recommended dilutions , highlighting the importance of proper optimization.
To verify antibody specificity:
Control Experiments:
Peptide Competition:
Cross-Reactivity Testing:
Test against bacterial extracts (should show no cross-reactivity with endogenous proteins)
Some antibodies have been tested against human protein arrays to identify potential cross-reactivity
Example: Clone TANA2 was found to recognize 9 human proteins containing the PDY sequence in addition to the HA tag
Tag Deletion:
Several factors influence detection sensitivity:
Tag Position Effect:
Antibody Concentration:
Secondary Antibody Limitation:
Multimeric Tags:
A sophisticated sandwich assay design for target binding assessment with HA-tagged proteins can be implemented as follows :
AMMP Target-Binding Assay Design:
Immobilize target protein (e.g., Bcl-xL) on magnetic beads
Allow binding of HA-tagged synthetic peptides to the target
Use fluoresceinated anti-HA antibody that binds to both the HA-tagged peptide and antifluorescein antibody on the sensor surface
This links the magnetic bead to the sensor surface
Alternative Radiolabeled Binding Assay:
Controls and Validation:
Include non-binding peptides as negative controls
Include peptides with known binding constants as standards
Verify assay sensitivity and dynamic range
Quantification:
Establish relationship between signal intensity and binding affinity
Use the HA tag competition assay (described earlier) to normalize for peptide concentration
Research data supports these considerations:
Species Compatibility:
Expression Systems:
Context-Dependent Recognition:
Cell Type-Specific Factors:
A comparative analysis based on research data:
Recent quantitative research concluded: "For researchers initiating a new project, the use of the HA or DYKDDDDK tags appears as a good choice" . Additionally: "Only AF291 (anti-HA), TA001 (anti-DYKDDDDK), and AV248 (anti-6xHis) are devoid of intellectual property, allowing them to be produced and used with no restrictions" .
Recent developments in antibody research using HA-targeted antibodies have led to exciting advances in specificity prediction:
Memory B Cell Language Model (mBLM):
Key Findings from Model Development:
HA head and stem antibodies have distinct sequence features
CDR H3 sequences of HA stem antibodies showed significantly higher hydrophobicity than HA head antibodies (p = 0.001)
The tip of CDR H3 showed even more pronounced hydrophobicity differences (p = 4e-12)
No significant difference in CDR H3 length was observed between antibody types (p = 0.38)
Validation and Applications:
Implications for Research:
This approach can accelerate epitope mapping without extensive experimental work
The model can potentially be extended to predict other antibody specificities
Demonstrates the value of data mining and machine learning in antibody research