PATL5 Antibody

What is PATL5 and what types of antibodies are available for its detection?

PATL5 (Patellin-5) is a protein in Arabidopsis thaliana (Mouse-ear cress) with UniProt ID Q9M0R2, encoded by the gene AT4G09160. It belongs to a family of proteins characterized by a Golgi dynamics (GOLD) domain in tandem with a Sec14p-like domain, which may play roles in membrane trafficking and lipid transfer .

Currently available antibody types include:

These antibodies are primarily designed for basic research applications and are not intended for diagnostic or therapeutic use .

How should researchers validate PATL5 antibodies for experimental applications?

Antibody validation is crucial for ensuring reliable experimental results. For PATL5 antibodies, implement the following multi-tiered validation strategy:

• Molecular specificity testing:

  • Western blot analysis showing single band at expected molecular weight (~74 kDa)

  • ELISA titration against recombinant PATL5 protein vs. related proteins

  • Peptide competition assays to confirm epitope specificity

• Biological validation:

  • Expression correlation between protein (antibody signal) and mRNA levels

  • Signal reduction in RNA interference or CRISPR knockout models

  • Comparison of staining patterns across multiple antibodies targeting different epitopes

• Application-specific validation:

  • For immunohistochemistry: Include positive and negative tissue controls

  • For immunofluorescence: Confirm subcellular localization with organelle markers

  • For immunoprecipitation: Verify pulled-down protein by mass spectrometry

• Cross-validation with orthogonal methods:

  • Compare protein detection with transcript detection (RT-PCR, RNA-seq)

  • Verify subcellular localization using GFP-fusion proteins

  • Confirm protein interactions using multiple independent methods

The Validated Antibody Database (VAD) includes documentation for 377,245 antibody applications based on 56,635 publications, emphasizing the importance of proper validation strategies .

What are optimal storage and handling conditions for maintaining PATL5 antibody activity?

Proper storage and handling are essential for maintaining antibody performance:

• Long-term storage:

  • Store concentrated antibody at -20°C or -80°C

  • Avoid repeated freeze-thaw cycles by preparing small aliquots

  • Some preparations may contain 50% glycerol to prevent freezing damage

• Buffer composition:

  • Typical storage buffers include preservatives (0.03% Proclin 300)

  • Buffer components often include 0.01M PBS, pH 7.4

  • Some preparations may include stabilizing proteins (BSA)

• Working solution handling:

  • Briefly centrifuge antibody vials before opening to collect liquid at bottom

  • Use sterile techniques and DNase/RNase-free consumables

  • For lyophilized antibodies, reconstitute according to manufacturer instructions

• Shipping and receiving:

  • Upon receipt, immediately transfer to recommended storage temperature

  • Document lot numbers and receipt dates for troubleshooting

  • Perform functional testing when switching to new lots

• Documentation practices:

  • Maintain records of freeze-thaw cycles

  • Document dilution preparations and dates

  • Record antibody performance across experiments for consistency monitoring

Following these guidelines will help maintain antibody activity and ensure reproducible experimental results.

How can researchers distinguish between PATL family members when using antibodies?

Distinguishing between PATL family members requires careful experimental design due to potential cross-reactivity:

• Epitope analysis and antibody selection:

  • Choose antibodies targeting unique regions rather than conserved domains

  • N-terminal regions often show greater sequence divergence among PATL proteins

  • Consider using monoclonal antibodies for highest specificity

• Experimental validation approaches:

  • Perform Western blots with recombinant proteins of all PATL family members

  • Create a cross-reactivity matrix showing binding to each family member

  • Include knockout/knockdown controls for each PATL protein

• Advanced resolution techniques:

  • Use high-resolution SDS-PAGE to separate similarly sized PATL proteins

  • Consider two-dimensional electrophoresis for enhanced separation

  • Employ antibody combinations targeting different epitopes simultaneously

• Specificity enhancement strategies:

  • Pre-absorb antibodies with recombinant proteins of other PATL family members

  • Implement competitive binding assays with specific peptides

  • Use bioinformatic analysis to identify unique epitopes for targeted antibody development

The use of both N-terminal and C-terminal targeting antibody combinations, as offered by some manufacturers, can improve discrimination between PATL family members .

What are the most effective experimental protocols for using PATL5 antibodies in Western blot analysis?

For optimal Western blot results with PATL5 antibodies, follow this detailed protocol:

Sample Preparation:

• Extract proteins from plant tissues using buffer containing:

  • 50 mM Tris-HCl (pH 7.5)

  • 150 mM NaCl

  • 1% Triton X-100

  • Protease inhibitor cocktail

• Homogenize thoroughly and clarify by centrifugation (14,000 × g, 15 min, 4°C)

• Determine protein concentration by Bradford or BCA assay

• Mix with Laemmli buffer and heat at 95°C for 5 minutes

Gel Electrophoresis and Transfer:

• Load 20-50 μg protein per lane on 10-12% SDS-PAGE gel

• Run at 100-120V until dye front reaches bottom

• Transfer to PVDF membrane (wet transfer: 100V for 1 hour or 30V overnight at 4°C)

• Verify transfer with Ponceau S staining

Antibody Incubation:

• Block with 5% non-fat milk or 3-5% BSA in TBST (1 hour, room temperature)

• Incubate with primary PATL5 antibody (1:1000-1:2000) overnight at 4°C

• Wash 3 × 10 minutes with TBST

• Incubate with HRP-conjugated secondary antibody (1:5000-1:10000) for 1 hour

• Wash 3 × 10 minutes with TBST

Detection and Troubleshooting:

• Apply ECL substrate and image using appropriate system

• Expected molecular weight for PATL5: ~74 kDa

• Include positive control (known PATL5-expressing tissue) and loading control

For high background: Increase blocking agent concentration or washing stringency
For weak signal: Try increased antibody concentration or enhanced chemiluminescence reagents
For multiple bands: Validate with recombinant PATL5 standard and knockout controls

What controls are essential when performing immunoprecipitation with PATL5 antibodies?

Immunoprecipitation (IP) with PATL5 antibodies requires comprehensive controls to ensure reliable and interpretable results:

• Input controls:

  • Total lysate (pre-IP) to demonstrate starting material

  • Flow-through (post-IP) to assess depletion efficiency

  • Quantitative comparison of both to determine IP efficiency

• Antibody specificity controls:

  • Isotype control antibody from same species

  • Pre-immune serum (for polyclonal antibodies)

  • Peptide competition by pre-incubating antibody with immunizing peptide

• Technical controls:

  • Beads-only control (no antibody) to assess non-specific binding

  • IP from tissues lacking PATL5 expression

  • Reciprocal IP using antibodies to interacting partners

• Validation controls:

  • Western blot verification of immunoprecipitated protein

  • Mass spectrometry confirmation

  • Size verification compared to recombinant standard

• Cross-validation using complementary approaches:

  • Co-immunoprecipitation using antibodies against known interactors

  • Proximity labeling techniques (BioID, APEX)

  • In vitro binding assays with purified components

A study from Jain et al. demonstrated that comprehensive antibody characterization in diverse assays can predict successful progression through development, emphasizing the importance of thorough controls .

How can researchers overcome polyreactivity issues when using PATL5 antibodies?

Polyreactivity (non-specific binding to multiple unrelated targets) can affect experimental outcomes. Address this issue using these approaches:

• Polyreactivity screening methods:

  • Test antibody binding to unrelated proteins (BSA, thyroglobulin)

  • Evaluate binding to different species' tissues (human, mouse)

  • Assess reactivity with different cellular compartments

• Mechanistic understanding of polyreactivity causes:

  • Charge-based: Some antibodies bind based on charge rather than specific epitopes

  • Hydrophobicity-driven: Non-specific interactions with hydrophobic surfaces

  • Structural mimicry: Cross-reactivity with structurally similar epitopes

• Experimental remediation strategies:

  • Increase blocking agent concentration (5% BSA, 10% normal serum)

  • Add competing proteins to reduce non-specific binding

  • Optimize salt concentration in buffers (150-500 mM NaCl)

  • Pre-absorb antibodies with tissue lysates from non-expressing sources

• Advanced purification approaches:

  • Affinity purification against the specific antigen

  • Negative selection against cross-reactive material

  • Competitive elution strategies

Research by Jain et al. demonstrated that polyreactivity can be driven by both charge and hydrophobicity, requiring screening across multiple assay formats to identify these overlapping phenotypes early in research .

What techniques are most effective for mapping PATL5 antibody epitopes?

Epitope mapping provides crucial information about antibody-antigen interaction regions, informing experimental design and interpretation:

• Peptide array analysis:

  • Use overlapping peptides spanning PATL5 sequence

  • Test antibody binding to identify specific linear epitopes

  • Quantify binding affinity to each peptide

  Peptide	Sequence (example)	Position	Binding Signal
  P1	MSQDSATTTPPPPL	1-14	-
  P2	ATTTPPPPLTSDVS	7-20	-
  P3	PPPPLTSDVSMPSG	10-23	++
  P4	LTSDVSMPSGEEDEP	14-28	+++
  P5	SMPSGEEDEPKHVTS	19-33	+

• Truncation/deletion analysis:

  • Generate series of truncated PATL5 constructs

  • Test antibody binding to narrow down epitope region

  • Particularly useful for conformational epitopes

• Hydrogen-deuterium exchange mass spectrometry:

  • Compare exchange patterns of PATL5 alone vs. antibody-bound

  • Identify regions protected from exchange in antibody-bound state

  • Provides structural information about binding interface

• Computational prediction and validation:

  • Use epitope prediction algorithms based on protein structure

  • Generate synthetic peptides of predicted epitopes

  • Validate predictions through binding assays

Recent work by researchers demonstrated that even with sparse binding data from a limited set of peptide sequences, machine learning models can predict antibody binding to any possible peptide sequence with high accuracy .

How should researchers interpret unexpected Western blot results with PATL5 antibodies?

Unexpected Western blot results require systematic investigation to distinguish between technical issues and biological findings:

• Multiple bands:

  • Potential explanations:

    • Post-translational modifications (phosphorylation, glycosylation)

    • Alternative splice variants

    • Protein degradation products

    • Cross-reactivity with related proteins

  • Validation approaches:

    • Compare to recombinant PATL5 standard

    • Test in knockout/knockdown samples

    • Treat with phosphatases or glycosidases to remove modifications

    • Vary sample preparation conditions to minimize degradation

• Unexpected molecular weight:

  • Higher than expected:

    • Post-translational modifications increasing mass

    • Incomplete denaturation causing altered migration

    • Technical issues with gel system calibration

  • Lower than expected:

    • Proteolytic cleavage or degradation

    • Alternative translation start sites

    • Alternative splicing removing portions of protein

• Absence of signal:

  • Technical considerations:

    • Verify transfer efficiency (Ponceau S staining)

    • Check primary and secondary antibody activity

    • Ensure appropriate detection sensitivity

  • Biological considerations:

    • Confirm PATL5 expression in sample type

    • Test multiple tissue/cell types

    • Consider developmental or environmental regulation

• Signal variability between experiments:

  • Standardize:

    • Sample preparation protocol

    • Protein quantification method

    • Gel percentage and running conditions

    • Transfer parameters

    • Antibody dilutions and incubation times

Bumbaca et al. highlighted how antibody humanization can introduce unexpected binding properties, emphasizing the importance of thorough characterization after any antibody modification .

What are the best experimental designs for co-localization studies using PATL5 antibodies?

For robust co-localization experiments investigating PATL5's subcellular distribution:

• Antibody compatibility planning:

  • Use primary antibodies from different host species

  • Select fluorophores with minimal spectral overlap

  • Consider antibody isotypes when using secondary antibodies

• Sample preparation optimization:

  • Test multiple fixation methods (4% PFA, methanol, acetone)

  • Optimize permeabilization conditions (Triton X-100, saponin)

  • Include antigen retrieval if needed for plant tissues

• Imaging controls:

  • Single-labeled samples to detect bleed-through

  • Secondary-only controls to assess background

  • Peptide competition controls to verify specificity

• Co-localization markers selection:

  • Include established markers for relevant organelles

  • For PATL5, consider Golgi, plasma membrane, and vesicular markers

  • Use both overexpressed fluorescent protein markers and antibody-based markers

• Quantitative analysis approach:

  • Calculate Pearson's correlation coefficient

  • Determine Manders' overlap coefficient

  • Perform statistical comparison across multiple cells/images

• Validation with complementary techniques:

  • Subcellular fractionation followed by Western blotting

  • Immuno-electron microscopy for ultrastructural localization

  • Live-cell imaging with fluorescently tagged PATL5

A comprehensive study demonstrated how polyreactivity can lead to unexpected off-target binding, emphasizing the need for thorough validation in co-localization studies .

How do post-translational modifications affect PATL5 antibody recognition?

Post-translational modifications (PTMs) can significantly impact antibody-epitope interactions:

• Common PTMs affecting antibody recognition:

  • Phosphorylation (serine, threonine, tyrosine residues)

  • Glycosylation (asparagine, serine, threonine residues)

  • Ubiquitination (lysine residues)

  • Proteolytic processing (various cleavage sites)

• Mechanism of antibody recognition interference:

  • Direct epitope obstruction: Modification occurs within epitope

  • Conformational changes: Modifications alter protein folding

  • Molecular mass changes: Affect migration patterns in gels

  • Protein-protein interactions: Modifications create binding sites that mask epitopes

• Experimental approaches to address PTM-related detection issues:

  • Generate phospho-specific antibodies for key phosphorylation sites

  • Treat samples with phosphatases before antibody application

  • Use glycosidases to remove carbohydrate moieties

  • Compare reducing vs. non-reducing conditions for disulfide effects

• Investigation strategies for suspected PTM interference:

  • Compare detection in different tissues/conditions with varying PTM levels

  • Analyze protein migration patterns before/after phosphatase treatment

  • Perform immunoprecipitation followed by mass spectrometry to identify PTMs

  • Use bioinformatic prediction to identify potential PTM sites in epitope regions

Research with therapeutic antibodies has demonstrated that post-translational modifications can significantly alter antibody recognition, particularly when modifications occur within or near the epitope region .

What approaches should be used to confirm specificity of PATL5 antibodies?

Confirming antibody specificity requires multiple complementary approaches:

• Genetic validation approaches:

  • Test in CRISPR/Cas9 knockout or knockdown models

  • Compare antibody signal with mRNA expression levels

  • Validate across tissues with varying PATL5 expression levels

• Biochemical validation methods:

  • Peptide competition assays with immunizing antigen

  • Pre-absorption experiments with recombinant protein

  • Western blot against recombinant protein and tissue lysates

  • Immunoprecipitation followed by mass spectrometry identification

• Cross-validation with different antibodies:

  • Compare results from antibodies targeting different epitopes

  • Test both monoclonal and polyclonal antibodies

  • Use antibody combinations for enhanced specificity

• Orthogonal technique confirmation:

  • Compare protein detection with mRNA expression data

  • Correlate findings with GFP-tagged PATL5 localization

  • Verify functional studies with genetic manipulation approaches

• Enhanced validation approaches:

  • Independent reproduction in different laboratories

  • Blinded analysis of control and experimental samples

  • Comprehensive documentation of validation experiments

A study by Bumbaca et al. identified off-target binding to mouse complement component C3 during antibody humanization, highlighting the importance of thorough specificity testing, especially after antibody engineering .

How can researchers optimize immunohistochemistry protocols for PATL5 detection in plant tissues?

Plant tissues present unique challenges for immunohistochemistry that require specialized approaches:

• Tissue fixation optimization:

  • Compare aldehyde-based fixatives (4% paraformaldehyde, glutaraldehyde)

  • Test dual fixation protocols (combining aldehydes with alcohols)

  • Optimize fixation duration based on tissue thickness and density

• Cell wall considerations:

  • Implement enzymatic digestion (cellulase, pectinase) for improved antibody penetration

  • Optimize digestion time to balance antigen preservation with accessibility

  • Consider sectioning thickness based on tissue type and density

• Antigen retrieval methods for plant tissues:

  • Heat-induced epitope retrieval (citrate buffer pH 6.0, EDTA pH 9.0)

  • Enzymatic retrieval (proteinase K, trypsin)

  • Test microwave, pressure cooker, and water bath heating methods

• Plant-specific background reduction:

  • Block endogenous peroxidase with hydrogen peroxide pre-treatment

  • Add plant-specific blocking agents (plant protein extracts)

  • Include specific steps to block endogenous biotin if using biotin-based detection

• Detection system optimization:

  • Compare chromogenic (DAB, AEC) vs. fluorescent detection

  • Use tyramide signal amplification for low-abundance proteins

  • Implement multi-labeling approaches with compatible detection systems

• Plant-specific controls:

  • Include tissues from PATL5 mutant plants as negative controls

  • Use tissues with varying PATL5 expression levels as dynamic range controls

  • Implement absorption controls with recombinant PATL5 protein

• Counterstaining considerations:

  • Select plant cell-specific counterstains (cell wall, nuclei)

  • Ensure compatibility with primary detection system

  • Optimize counterstain dilution to avoid obscuring specific signal

Multiple research groups emphasize that antibody validation across different applications is critical for reliable results, particularly in challenging tissue types .

What are the critical considerations when using PATL5 antibodies for protein-protein interaction studies?

Protein-protein interaction studies with PATL5 antibodies require careful experimental design:

• Antibody selection criteria:

  • Choose antibodies recognizing epitopes outside interaction domains

  • Consider using multiple antibodies targeting different regions

  • Validate that antibody binding doesn't disrupt protein interactions

• Co-immunoprecipitation optimization:

  • Test multiple lysis conditions to preserve interactions:

    • Detergent type and concentration (NP-40, Triton X-100)

    • Salt concentration (100-300 mM NaCl)

    • Buffer pH and composition

  • Include appropriate controls:

    • Non-specific IgG control

    • Beads-only control

    • Peptide competition control

• Proximity ligation assay considerations:

  • Select antibodies from different species for compatibility

  • Optimize fixation to preserve both PATL5 and interacting proteins

  • Include controls for antibody specificity and proximity threshold

• Validation with complementary techniques:

  • Verify interactions with recombinant proteins in vitro

  • Perform reciprocal co-immunoprecipitations

  • Confirm with orthogonal methods (yeast two-hybrid, FRET)

• Advanced MS-based interaction methods:

  • Implement antibody-based proximity labeling (BioID, APEX)

  • Perform quantitative immunoprecipitation followed by MS (q-AP-MS)

  • Compare interaction profiles across different conditions

Research has demonstrated that polyspecificity can affect interaction studies, highlighting the importance of comprehensive validation .

How can researchers use machine learning approaches to predict PATL5 antibody binding epitopes?

Machine learning offers powerful approaches to predict and analyze antibody-epitope interactions:

• Data collection for model training:

  • Generate peptide binding data using arrays of overlapping peptides

  • Test antibody binding against diverse peptide libraries

  • Include both positive (binding) and negative (non-binding) sequences

• Feature selection for epitope prediction:

  • Amino acid physicochemical properties (hydrophobicity, charge)

  • Secondary structure propensities

  • Solvent accessibility predictions

  • Evolutionary conservation scores

• Machine learning model implementation:

  • Support Vector Machines (SVMs) for binary classification

  • Random Forests for feature importance analysis

  • Deep learning approaches for complex pattern recognition

  • Ensemble methods combining multiple algorithms

• Validation and refinement approaches:

  • Cross-validation using held-out test data

  • Experimental validation of predicted epitopes

  • Model refinement with additional binding data

  • Comparison with known structural data when available

• Application to experimental design:

  • Prioritize predicted epitopes for antibody development

  • Design peptides for competitive binding assays

  • Identify potential cross-reactive epitopes in related proteins

  • Guide mutagenesis studies to confirm binding determinants

Recent research demonstrated that machine learning models can predict antibody binding to any possible peptide sequence with high accuracy, even from sparse binding data, enabling more efficient epitope characterization .