PM20D2 Antibody

What is PM20D2 and what cellular functions does it perform?

PM20D2 (peptidase M20 domain containing 2) is a protein that functions as a dipeptidase enzyme involved in proteolysis and regulation of cellular protein metabolic processes. It enables dipeptidase activity and identical protein binding activity and is primarily localized in the nucleoplasm . Also known as ACY1L2 (aminoacylase-1-like protein 2), PM20D2 plays roles in dipeptide metabolism through its enzymatic activities as xaa-Arg dipeptidase, I(2)-alanyl-lysine dipeptidase, and beta-alanyl-lysine dipeptidase . The protein has a calculated molecular weight of 48 kDa and consists of 436 amino acids .

What validated applications are available for PM20D2 antibodies?

PM20D2 antibodies have been validated for multiple experimental applications with varying dilution requirements:

It is recommended that researchers titrate the antibody in each specific testing system to obtain optimal results for their experimental design .

What is the species reactivity profile of commercially available PM20D2 antibodies?

Commercial PM20D2 antibodies demonstrate different reactivity profiles across species:

When selecting an antibody for cross-species applications, researchers should verify the validation status for their specific experimental model organism .

What are the optimal antigen retrieval conditions for PM20D2 immunohistochemistry?

For successful IHC detection of PM20D2 in tissue samples, the following antigen retrieval protocols have been experimentally validated:

• Primary recommended method: Tris-EDTA (TE) buffer at pH 9.0

• Alternative method: Citrate buffer at pH 6.0

Tissue-specific positive controls for PM20D2 IHC validation include:

• Human placenta tissue

• Human kidney tissue

• Human ovary tissue

• Human testis tissue

Researchers should perform systematic optimization of antigen retrieval parameters including buffer concentration, temperature, and incubation time based on their specific tissue preparation methods.

How should I design Western blot experiments to accurately detect PM20D2?

For optimal Western blot detection of PM20D2, follow this methodological approach:

• Sample preparation: Use appropriate lysis buffers with protease inhibitors

• Protein loading: Load 20-50 μg of total protein per lane

• Gel percentage: Use 10-12% SDS-PAGE gels for optimal separation of the 48 kDa PM20D2 protein

• Transfer conditions: Semi-dry or wet transfer at appropriate voltage/current

• Blocking: 5% non-fat milk or BSA in TBST for 1 hour at room temperature

• Primary antibody: Dilute PM20D2 antibody 1:500-1:1000 in blocking buffer

• Incubation: Overnight at 4°C with gentle rocking

• Secondary antibody: Anti-rabbit IgG-HRP at manufacturer's recommended dilution

• Positive control: Include HeLa cell lysate as validated positive control

• Expected band: Look for a specific band at approximately 48 kDa

Researchers should validate band specificity through additional controls such as knockdown/knockout samples when available.

What are the common causes of weak or absent signal when using PM20D2 antibodies in immunohistochemistry?

When troubleshooting PM20D2 IHC experiments, consider these methodological approaches for common issues:

For challenging samples, consider amplification systems such as tyramide signal amplification if conventional detection methods yield suboptimal results.

How can I validate the specificity of PM20D2 antibodies for my experimental model?

To validate PM20D2 antibody specificity, implement a multi-parameter approach:

• Western blot verification: Confirm single band at 48 kDa in positive control samples

• Molecular controls:

  • Positive control: HeLa cells express detectable levels of PM20D2

  • Negative control: siRNA knockdown or CRISPR/Cas9 knockout of PM20D2

• Peptide competition: Pre-incubate antibody with immunizing peptide to block specific binding

• Multiple antibody validation: Use antibodies targeting different PM20D2 epitopes

• Cross-platform validation: Compare results across multiple detection methods (WB, IHC, IF)

• Bioinformatic confirmation: Analyze tissue/cell-specific expression data from public databases

• Immunoprecipitation: Verify ability to pull down the target protein followed by mass spectrometry identification

This comprehensive validation strategy ensures reliable and reproducible experimental results with PM20D2 antibodies.

How can PM20D2 antibodies be integrated into multi-omics experimental designs?

Integrating PM20D2 antibodies into multi-omics experimental workflows requires careful coordination across platforms:

• Proteomics integration:

  • Use PM20D2 antibodies for immunoprecipitation followed by mass spectrometry to identify interaction partners

  • Validate mass spectrometry-identified PM20D2 peptides with antibody-based detection

  • Correlate post-translational modifications identified in proteomics with antibody-based detection

• Transcriptomics correlation:

  • Compare PM20D2 protein levels (antibody detection) with mRNA expression levels

  • Investigate discrepancies between protein and mRNA levels to identify post-transcriptional regulation

• Cellular localization studies:

  • Use subcellular fractionation followed by Western blot with PM20D2 antibodies

  • Complement with immunofluorescence to visualize nucleoplasmic localization

  • Correlate with proteomics data on organelle-specific protein enrichment

• Functional studies:

  • Use PM20D2 antibodies to monitor protein levels following genetic or pharmacological manipulations

  • Correlate changes in PM20D2 levels with functional readouts related to dipeptidase activity

This integrated approach maximizes the utility of PM20D2 antibodies in comprehensive multi-omics research frameworks.

What methods can be used to study PM20D2 in the context of protein-protein interactions?

For investigating PM20D2 protein interactions, employ these methodological approaches:

• Co-immunoprecipitation (Co-IP):

  • Use affinity-purified PM20D2 antibodies for pull-down experiments

  • Perform in reciprocal direction with antibodies against suspected interaction partners

  • Include appropriate controls (IgG control, input samples)

• Proximity ligation assay (PLA):

  • Use PM20D2 antibody in combination with antibodies against candidate interacting proteins

  • Visualize specific interactions through fluorescent signal generation when proteins are in close proximity (<40 nm)

• FRET/BRET studies:

  • Generate fluorescently tagged PM20D2 constructs

  • Validate tagged constructs against antibody detection patterns

  • Measure energy transfer between PM20D2 and potential interaction partners

• Cross-linking studies:

  • Perform protein cross-linking followed by immunoprecipitation with PM20D2 antibodies

  • Identify cross-linked partners through mass spectrometry analysis

• Yeast two-hybrid validation:

  • Confirm Y2H-identified interactions through antibody-based co-IP approaches

  • Compare interaction profiles in different cellular compartments, focusing on nucleoplasmic interactions

These approaches provide complementary evidence for PM20D2 protein interaction networks and functional relationships.

How can PM20D2 antibodies be adapted for super-resolution microscopy applications?

Adapting PM20D2 antibodies for super-resolution microscopy requires specific methodological considerations:

• Antibody labeling strategies:

  • Direct conjugation with photo-switchable fluorophores for STORM/PALM

  • Use high-quality secondary antibodies with appropriate fluorophores for STED microscopy

  • Validate that fluorophore conjugation does not affect antibody specificity

• Sample preparation optimization:

  • Use thin sections (50-100 nm) for optimal resolution

  • Implement rigorous fixation and permeabilization protocols to preserve nucleoplasmic structures

  • Consider tissue clearing techniques for thick tissue sections

• Validation approaches:

  • Compare conventional confocal with super-resolution imaging

  • Use orthogonal techniques to confirm unusual localization patterns

  • Include appropriate co-localization markers for nucleoplasmic structures

• Technical parameters:

  • Optimize antibody concentration to achieve single-molecule level detection

  • Adjust imaging buffers to enhance fluorophore photostability

  • Implement drift correction algorithms for extended imaging sessions

This methodological framework enables nanoscale visualization of PM20D2 localization and potential interaction domains.

What are the considerations for using PM20D2 antibodies in multiplexed immunofluorescence studies?

For successful multiplexed detection involving PM20D2 antibodies, consider these methodological approaches:

• Antibody panel design:

  • Select PM20D2 antibodies raised in different host species than other targets

  • Verify absence of cross-reactivity between antibodies in the multiplex panel

  • Consider using directly labeled primary antibodies to reduce species conflicts

• Sequential staining protocols:

  • Implement tyramide signal amplification (TSA) with antibody stripping between rounds

  • Validate that antibody stripping does not affect tissue morphology or antigen availability

  • Include controls for incomplete stripping of previous antibodies

• Spectral considerations:

  • Select fluorophores with minimal spectral overlap

  • Implement spectral unmixing for closely overlapping fluorophores

  • Include single-color controls for spectral fingerprinting

• Validation strategies:

  • Compare multiplexed with single-plex staining patterns

  • Include tissues with known expression patterns as positive controls

  • Implement computational analysis for co-localization quantification

• Data analysis approaches:

  • Use machine learning algorithms for cell segmentation and phenotyping

  • Implement spatial analysis tools to identify interaction patterns

  • Correlate with single-cell RNA-seq data for comprehensive analysis

These approaches enable sophisticated multi-parameter analysis of PM20D2 in complex tissue environments.

How might PM20D2 antibodies be integrated with emerging AI-based protein structure prediction tools?

Recent developments in AI-driven protein structure prediction offer new opportunities for PM20D2 antibody research :

• Epitope mapping enhancement:

  • Use AlphaFold-Multimer for predicting PM20D2-antibody complexes

  • Implement computational workflows combining ESM protein language models with antibody binding predictions

  • Validate computational predictions through experimental epitope mapping

• Structure-guided antibody optimization:

  • Use predicted PM20D2 structures to identify accessible epitopes

  • Design new antibodies targeting functionally important domains

  • Implement Rosetta-based computational approaches for antibody-antigen interaction optimization

• Integration with experimental data:

  • Combine antibody binding data with predicted structural information

  • Use structure prediction to resolve discrepancies in antibody reactivity patterns

  • Implement weighted scoring systems combining ESM log-likelihood ratios, AlphaFold confidence scores, and Rosetta binding energies

• Dynamic structural analysis:

  • Use antibody accessibility data to validate molecular dynamics simulations

  • Identify conformational epitopes through integrated computational-experimental approaches

  • Predict the impact of post-translational modifications on antibody binding

These integrated approaches represent the cutting-edge intersection of computational biology and experimental antibody research.

What methodological approaches can improve the specificity of next-generation PM20D2 antibodies?

To develop more specific PM20D2 antibodies, researchers can implement these advanced strategies:

• Computational antibody design:

  • Apply protein language models (ESM) to optimize antibody sequences

  • Use structure-based computational approaches to identify unique epitopes

  • Implement machine learning algorithms to predict cross-reactivity profiles

• Phage display optimization:

  • Design phage display experiments with negative selection against related proteins

  • Implement deep mutational scanning of antibody CDR loops

  • Use computational models to guide library design for improved specificity

• Advanced validation methodologies:

  • Implement proteome-wide binding profiling to identify potential cross-reactivity

  • Use CRISPR knockout cell lines as gold-standard negative controls

  • Apply multiplexed epitope mapping to characterize binding at single-amino acid resolution

• Performance metrics optimization:

  • Evaluate antibodies using standardized metrics like AUC (Area Under Curve) and APR (Average Precision with Recall)

  • Implement Pearson's correlation coefficient analysis for quantitative applications

  • Use BALM (Binding Antibody Likelihood Model) or similar computational tools for performance prediction

These methodological innovations can significantly enhance the specificity and reliability of next-generation PM20D2 antibodies for research applications.

How should PM20D2 antibodies be validated for use in patient-derived samples?

When adapting PM20D2 antibodies for clinical research with patient samples, implement these rigorous validation protocols:

• Tissue-specific validation:

  • Validate antibody performance in each tissue type of interest

  • Compare fresh vs. fixed tissue performance characteristics

  • Establish tissue-specific dilution and protocol requirements

• Pre-analytical variables assessment:

  • Evaluate impact of fixation time on epitope accessibility

  • Assess effects of storage duration on antigen preservation

  • Determine influence of collection/processing procedures on detection sensitivity

• Analytical validation:

  • Establish inter-laboratory reproducibility through standardized protocols

  • Determine intra- and inter-assay coefficients of variation

  • Implement digital pathology approaches for quantitative analysis

• Clinical correlation:

  • Compare antibody-based detection with clinical parameters

  • Evaluate diagnostic sensitivity and specificity for relevant applications

  • Implement appropriate statistical approaches for clinical data analysis

• Reference materials:

  • Use well-characterized positive and negative control samples

  • Implement cell line standards with known PM20D2 expression levels

  • Consider synthetic peptide controls for standardization

This comprehensive validation framework ensures reliable performance in translational research applications.

What methodological approaches are recommended for using PM20D2 antibodies in combination with other molecular probes?

For multiparametric analysis combining PM20D2 antibodies with other molecular probes, implement these methodological approaches:

• RNA-protein co-detection:

  • Optimize fixation conditions to preserve both protein epitopes and RNA integrity

  • Implement sequential detection protocols starting with the less robust target

  • Validate that RNA detection does not interfere with PM20D2 antibody binding

• Protein-protein multiplexing:

  • Design antibody panels with complementary host species or isotypes

  • Implement cyclic immunofluorescence with appropriate controls

  • Use spectral imaging and unmixing for closely related fluorophores

• Functional probe integration:

  • Combine PM20D2 antibody detection with activity-based probes

  • Implement live-cell imaging followed by fixation and antibody detection

  • Validate that functional probes do not mask PM20D2 epitopes

• Spatial transcriptomics integration:

  • Establish protocols compatible with both antibody detection and in situ sequencing

  • Implement computational approaches for integrating protein and transcript data

  • Use fiducial markers to align different data modalities

These methodological approaches enable sophisticated multiparametric analysis for complex experimental designs investigating PM20D2 biology.