ATL33 Antibody

What is the target specificity of ATL3 antibody and how is it validated?

ATL3 antibody (16921-1-AP) targets atlastin GTPase 3 and has been validated across multiple applications including Western Blot, Immunohistochemistry, Immunofluorescence, Immunoprecipitation, and Co-IP. Validation tests have confirmed reactivity with human, mouse, and rat samples. The antibody has been tested in multiple cell lines including HEK-293T, HeLa, HepG2, and various tissue samples, demonstrating consistent specificity .

For proper validation, the antibody undergoes:

• Application-specific testing (WB, IHC, IF/ICC, IP)

• Cross-reactivity testing across multiple species

• Molecular weight verification (observed molecular weight of 61 kDa)

• Background signal assessment

The validation approach ensures reproducibility and specificity, critical factors for reliable antibody-based research.

What are the recommended protocols for using antibodies in different experimental applications?

When employing antibodies for various applications, optimization of protocols is essential. Based on standardized protocols for ATL3 antibody as a reference model:

Each application requires protocol optimization for the specific antibody being used. Researchers should conduct preliminary titration experiments to determine optimal conditions for their experimental system .

How do storage conditions affect antibody stability and performance?

Proper storage is critical for maintaining antibody functionality. For most research antibodies:

• Storage temperature: Typically -20°C for long-term stability

• Buffer composition: PBS with 0.02% sodium azide and 50% glycerol (pH 7.3) provides optimal stability

• Aliquoting recommendations: For antibodies stored at -20°C, aliquoting may be unnecessary if glycerol is present

• Shelf life: Most properly stored antibodies remain stable for one year after shipment

• Freeze-thaw cycles: Minimize to prevent degradation

For specific examples like the ATL3 antibody, proper storage (-20°C in appropriate buffer) ensures consistent performance across experiments over time .

How can researchers distinguish between specific and non-specific binding in antibody applications?

Distinguishing specific from non-specific binding requires rigorous validation approaches:

Methodological approaches:

• Knockout/knockdown validation: Test antibody in samples where the target protein has been genetically removed or reduced. ATL3 antibody publications include KD/KO validations demonstrating this approach .

• Peptide competition assays: Pre-incubation of the antibody with the immunizing peptide should block specific binding signals.

• Cross-validation with multiple antibodies: Using antibodies targeting different epitopes of the same protein.

• Correlation of signal with protein expression levels: Signal intensity should correlate with known expression patterns across tissues/cell types.

• Application-specific controls:

  • For IHC/IF: Include isotype controls and secondary-only controls

  • For WB: Molecular weight verification and recombinant protein controls

  • For IP: Use non-specific IgG as negative control

These validation approaches are particularly critical when investigating novel targets or when antibody characterization data is limited.

What are the latest developments in antibody engineering for therapeutic applications?

Recent advances in antibody engineering have produced novel molecular formats with enhanced therapeutic potential:

One-armed (monovalent) antibodies:
Genentech has developed onartuzumab, a one-armed antibody currently in late-stage clinical trials for multiple cancer types. Traditional bivalent antibodies can sometimes cause receptor crosslinking and activation rather than inhibition. Onartuzumab targets the receptor tyrosine kinase MET without causing dimerization, addressing a limitation of conventional bivalent antibodies .

Mechanism of action determination:
Crystal structures obtained through advanced crystallography techniques at ALS Beamline 5.0.2 have revealed the mechanism by which onartuzumab binds to MET. This represents an innovative approach to addressing the challenge of receptor dimerization in antibody therapeutics .

Structural considerations for therapeutic antibodies:

• Fab regions (fragment antigen-binding) determine target specificity

• Fc regions (fragment crystallizable) influence half-life and effector functions

• Monovalent designs prevent unwanted target clustering and activation

These engineering approaches highlight the importance of understanding antibody structure-function relationships for therapeutic development.

How are computational methods enhancing antibody-antigen binding prediction?

Machine learning approaches are increasingly important for predicting antibody-antigen interactions:

Current challenges in binding prediction:

• Out-of-distribution prediction: Models struggle when test antibodies and antigens are not represented in the training data

• Limited availability of comprehensive datasets due to the high cost of generating experimental binding data

Active learning approaches:
Recent research has developed fourteen novel active learning strategies for antibody-antigen binding prediction in library-on-library settings. Three of these algorithms significantly outperformed random sampling baselines, with the best algorithm substantially reducing the number of required antigen-antibody measurements .

Benefits of computational approaches:

• Reduction in experimental costs through targeted testing

• Ability to handle many-to-many relationships between antibodies and antigens

• Improved prediction accuracy for novel antibody-antigen pairs

The integration of computational prediction with experimental validation represents a powerful approach for antibody research and development.

What approaches are employed for structural characterization of antibody-antigen complexes?

Structural characterization is essential for understanding antibody mechanisms:

Crystallographic approaches:
Research on anti-A33 monoclonal antibodies demonstrated how crystallography can reveal binding epitopes and mechanisms. Seven murine anti-A33 monoclonal antibodies were produced by immunizing mice with live vaccinia virus, followed by boosting with soluble A33 homodimeric ectodomain. Crystal structures of three representative neutralizing antibodies in complex with A33 revealed different binding modes :

• MAbs A2C7 and A20G2: Binding to a single A33 subunit

• MAb A27D7: Binding to both A33 subunits simultaneously, demonstrating resistance to single alanine substitutions

Binding kinetics analysis:
Binding kinetics determined for wild-type A33 and engineered A33 with alanine substitutions revealed that MAb A27D7 had high-affinity binding with recombinant A33 protein mimicking other orthopoxvirus strains, suggesting its potential as a cross-neutralizer .

These structural approaches provide critical insights into antibody function and can guide the rational design of therapeutic antibodies with enhanced properties.

How can antibodies be effectively utilized as biomarkers in cancer research?

Antibodies play a pivotal role in cancer biomarker research:

Application in biomarker identification:
Primary antibodies enable visualization of biomarker presence, localization, and abundance within cancer tissues through techniques like immunohistochemistry and immunofluorescence. This targeted binding facilitates the precise identification of molecular signatures, aiding in tumor characterization and the development of diagnostic and therapeutic strategies .

Approaches for antibody selection in cancer research:

• Target selection based on prognostic value (favorable/unfavorable prognosis genes)

• Validation across multiple cancer types

• Correlation with clinical outcomes

• Specificity for cancer-specific modifications

Atlas Antibodies offers primary antibodies targeting markers relevant to 17 human cancer types, with extensive validation for research applications. These tools enable researchers to explore genes with favorable and unfavorable prognoses in various cancer types .

What strategies can resolve discrepancies in antibody performance across different experimental platforms?

When antibodies show inconsistent performance across platforms (e.g., works in WB but not IHC):

Systematic troubleshooting approaches:

• Epitope accessibility assessment: Fixation, sample preparation, and protein conformation can affect epitope exposure differently across techniques

• Buffer optimization: Modifying blocking reagents, detergents, or salts can reduce background or enhance specific binding

• Target protein modification analysis: Post-translational modifications may differ between native and denatured states

• Cross-platform validation protocol:

  • Start with application where antibody shows best performance

  • Systematically vary conditions to match successful application

  • Document all protocol variations and outcomes

For example, the ATL3 antibody (16921-1-AP) shows positive results across multiple applications but requires different dilutions and conditions for each: WB (1:2000-1:12000), IP (0.5-4.0 μg), IHC (1:50-1:500), and IF/ICC (1:50-1:500) .

How can researchers determine the optimal antibody concentration for novel experimental conditions?

When establishing new experimental conditions:

Titration approach:

• Start with a broad dilution range (e.g., 1:50, 1:100, 1:500, 1:1000, 1:5000)

• Narrow the range based on signal-to-noise ratio

• Perform replicate experiments at the optimal concentration to ensure reproducibility

Experimental design considerations:

• Include both positive and negative controls in titration experiments

• Evaluate both signal intensity and background levels

• Consider sample-specific factors (protein abundance, tissue type)

• Document all experimental variables for reproducibility

This methodical approach is recommended for all antibodies, including those with established protocols, when applying them to new experimental systems or sample types .

What role do antibodies play in understanding neurodegenerative diseases like Alzheimer's?

Antibodies are becoming increasingly important in neurodegenerative disease research:

CD33-targeting antibodies in Alzheimer's research:
Alchemab Therapeutics has unveiled ATLX-1088, a potential first-in-class human antibody targeting CD33, a cell surface protein with a key role in Alzheimer's disease. Studies have found that higher CD33 expression correlates with more advanced cognitive decline and worsening disease status. CD33 inhibits the normal function of microglia, which are brain-resident immune cells responsible for maintaining neural networks and repairing damage .

Novel discovery approach:
Rather than starting with a target-led drug discovery process, Alchemab identified common antibodies unique to individuals resilient to Alzheimer's disease, which led to the identification of CD33 as a target. This approach inverts traditional discovery processes by using the immune system as a search function to identify the most important disease-modifying targets .

Therapeutic implications:
The antibody's novel mechanism of action affects microglial cell function broadly, offering potential advantages in addressing Alzheimer's disease through a different pathway than existing therapies .

How are machine learning methods transforming antibody research and development?

Machine learning is revolutionizing antibody research:

Active learning for antibody-antigen binding prediction:
Recent research has developed active learning strategies for antibody-antigen binding prediction in library-on-library settings. These approaches address the challenge of out-of-distribution prediction, where models must predict interactions for antibodies and antigens not represented in training data .

Methodology and results:

• Starting with a small labeled dataset and iteratively expanding it

• Evaluating fourteen novel active learning strategies

• Identifying three algorithms that significantly outperformed random sampling

• Achieving reduction in required antigen-antibody measurements for accurate prediction

Future implications:
These computational approaches could substantially reduce experimental costs and accelerate antibody development by focusing experimental resources on the most informative measurements .

What are the key considerations for selecting antibodies for reproducible research?

For reproducible antibody-based research:

• Validation status: Select antibodies with comprehensive validation data in the specific application and sample type of interest

• Reporting standards: Document complete antibody information:

  • Catalog number and vendor

  • Clone ID for monoclonals

  • Host species and isotype

  • Target protein and species reactivity

  • RRID (Research Resource Identifier)

• Protocol standardization: Maintain detailed records of:

  • Dilution factors and incubation conditions

  • Sample preparation methods

  • Detection systems and reagents

  • Control samples used

• Independent validation: Confirm key findings with:

  • Alternative antibodies targeting the same protein

  • Complementary techniques

  • Genetic approaches (knockout/knockdown)

These practices are essential for addressing the reproducibility challenges in antibody-based research and ensuring scientific rigor.