mtpn Antibody

What is MTPN and why is it a target for antibody-based research?

Myotrophin (MTPN) is a ubiquitously expressed 12 kDa cytoplasmic protein first isolated from hearts of spontaneously hypertensive rats. It consists of 118 amino acids and contains two and a half internal 33 amino acid ankyrin repeats. MTPN plays significant roles in cardiac and skeletal muscle physiology by stimulating protein synthesis and increasing the expression of cardiac marker genes such as atrial natriuretic factor and β-myosin heavy chain, which can lead to hypertrophy. In skeletal muscle, MTPN functions as a positive growth factor promoting muscle growth .

The protein's involvement in cardiac hypertrophy pathways and cellular growth regulation makes it a valuable target for research into cardiovascular conditions and muscle development disorders.

What applications are most effective for MTPN antibody detection?

MTPN antibodies can be utilized across multiple experimental platforms with varying effectiveness. Based on validated protocols, the following applications have demonstrated successful detection:

For optimal results, it's advisable to test several antibody concentrations with your specific samples as reactivity can vary based on sample preparation and experimental conditions .

How do I properly store and handle MTPN antibodies to maintain activity?

Proper storage and handling of MTPN antibodies are crucial for maintaining their activity and specificity:

• Store at -20°C for long-term preservation. Antibodies are typically stable for one year after shipment when stored correctly.

• For frequent use and short-term storage (up to one month), storing at 4°C is acceptable.

• Avoid repeated freeze-thaw cycles as they can diminish antibody activity and increase non-specific binding.

• Most MTPN antibodies are supplied in PBS with 0.02% sodium azide and 50% glycerol at pH 7.3, which helps maintain stability.

• Small aliquots (20μL) may contain 0.1% BSA as a stabilizer.

• Aliquoting is generally unnecessary for -20°C storage unless frequent access is required .

Before use, allow the antibody to equilibrate to room temperature and gently mix to ensure homogeneity without introducing bubbles or denaturing the protein.

How should I validate the specificity of MTPN antibodies for my experimental system?

Validating antibody specificity is critical for reliable results. For MTPN antibodies, implement this multi-step validation strategy:

• Positive and negative control tissues/cells: Test the antibody on samples known to express MTPN (human brain, HEK-293T, HeLa, Jurkat cells) and on negative controls (tissue-specific, based on literature).

• Molecular weight verification: Confirm that the detected band appears at the expected molecular weight (~12 kDa for MTPN).

• Knockdown/knockout validation: Use siRNA knockdown or CRISPR knockout of MTPN and demonstrate decreased or absent signal.

• Peptide competition assay: Pre-incubate the antibody with the immunizing peptide to verify that the signal disappears when the antibody is neutralized.

• Multiple antibody verification: Use multiple antibodies targeting different epitopes of MTPN to confirm consistent detection patterns.

• Correlation with mRNA expression: Where possible, correlate protein detection with mRNA expression data.

When publishing, include validation data and clearly specify the antibody used (catalog number, lot, manufacturer) to enhance reproducibility .

What factors affect the sensitivity of MTPN detection in ELISA-based assays?

The sensitivity of MTPN detection in ELISA assays can be influenced by multiple factors that researchers should optimize:

• Antibody quality and affinity: High-affinity antibodies with equilibrium dissociation constants (KD) in the 10^-10 range provide superior sensitivity. Commercial MTPN ELISA kits report sensitivities around 2.57 pg/mL .

• Epitope accessibility: Ensure proper sample preparation to expose epitopes. For MTPN, sample denaturation requirements depend on whether the target epitope is conformational or linear.

• Detection system: Sandwich ELISA formats typically offer higher sensitivity than direct or indirect formats. For MTPN, sandwich ELISAs use a capture antibody specific to MTPN and a biotin-conjugated detection antibody .

• Sample matrix effects: Matrix components can interfere with antibody binding. Recovery rates for MTPN in different matrices vary:

• Sample dilution linearity: Proper dilution ensures measurements fall within the linear range:

• Assay standardization: Using properly characterized standards and implementing rigorous quality control measures with intra-assay (CV<8%) and inter-assay (CV<10%) precision assessment .

What troubleshooting approaches should I use for weak or absent MTPN antibody signals in Western blots?

When encountering weak or absent MTPN signals in Western blot experiments, implement this systematic troubleshooting approach:

• Sample preparation optimization:

  • Ensure complete protein extraction using appropriate lysis buffers (RIPA or NP-40 with protease inhibitors)

  • Verify protein concentration using reliable methods (BCA/Bradford)

  • Increase loading amount (MTPN is expressed at moderate levels in most tissues)

• Transfer efficiency verification:

  • Use reversible staining methods (Ponceau S) to confirm successful protein transfer

  • Optimize transfer conditions for small proteins (<15 kDa) - consider using PVDF membranes with 0.2μm pore size for better retention of small proteins like MTPN

• Antibody conditions optimization:

  • Test increased antibody concentration (up to 1:500 for primary antibody)

  • Extend primary antibody incubation (overnight at 4°C)

  • Reduce washing stringency slightly while maintaining specificity

  • Consider using signal enhancers or more sensitive detection systems

• Epitope accessibility improvement:

  • For MTPN, some epitopes may require specific antigen retrieval for IHC (TE buffer pH 9.0 has shown better results than citrate buffer pH 6.0)

  • Ensure samples are properly denatured if targeting linear epitopes

• Blocking optimization:

  • Test different blocking agents (BSA vs. non-fat milk) as some antibodies perform better with specific blockers

  • Adjust blocking time and concentration

• Positive control inclusion:

  • Run samples from tissues known to express MTPN (human brain tissue, HEK-293T, HeLa, or Jurkat cells)

How do I accurately map epitope specificity for novel anti-MTPN antibodies?

Epitope mapping for novel anti-MTPN antibodies requires a strategic combination of techniques:

• Deletion mutant analysis: Generate a series of MTPN deletion mutants and test antibody binding through ELISA or Western blot. This approach has successfully identified binding regions for anti-MTPN antibodies, with documented epitopes at regions 210-231, 335-348, and 382-397 amino acids .

• Peptide array analysis: Synthesize overlapping peptides spanning the entire MTPN sequence and test antibody binding to identify linear epitopes.

• Site-directed mutagenesis: For fine epitope mapping, create site-directed mutations with single-amino-acid substitutions and examine reactivity changes. This approach has successfully identified critical specificity residues, such as Ala217, that differentiate between closely related proteins .

• Substitution analysis with homologous proteins: When deletion mutants result in protein misfolding, use chimeric proteins where regions of MTPN are replaced with homologous regions from related proteins (e.g., SARS-CoV vs. SARS-CoV-2 NP epitope mapping) .

• Cross-species conservation analysis: Perform multiple sequence alignments across species to identify conserved and variable regions, which can help predict cross-reactivity.

• Structural analysis: If structural data is available, use computational modeling to predict surface-exposed regions likely to serve as epitopes.

For quantitative assessment of antibody-antigen binding strength, determine the equilibrium dissociation constant (KD) using Bio-Layer interferometry, which allows comparison of different antibodies. High-affinity antibodies typically have KD values in the 10^-10 range .

What considerations should guide the selection of MTPN antibodies for immunoprecipitation of protein complexes?

Selecting antibodies for immunoprecipitation (IP) of MTPN-containing complexes requires careful consideration of multiple factors:

For studying MTPN's role in cardiac hypertrophy pathways, consider IP conditions that preserve weak or transient interactions that might be physiologically relevant .

How do species-specific differences in MTPN impact antibody selection for cross-species studies?

Species-specific differences in MTPN protein can significantly impact antibody selection for comparative studies:

• Conservation analysis: MTPN is highly conserved across mammalian species, but subtle differences exist. Perform sequence alignment across target species (human, mouse, rat, etc.) to identify regions of high conservation that may serve as universal epitopes versus divergent regions that may affect species-specific binding.

• Epitope-specific considerations: Antibodies targeting highly conserved regions, such as the ankyrin repeats, are more likely to show cross-reactivity across species. For example, the 13508-1-AP antibody shows reactivity with human, mouse, and rat MTPN .

• Validation across species: Even when sequence homology suggests potential cross-reactivity, empirical validation is essential:

  • Test the antibody on positive control samples from each target species

  • Confirm detection at the correct molecular weight (MTPN is ~12-13 kDa across species)

  • Verify specificity using knockout/knockdown controls when available

• Application-specific testing: An antibody that cross-reacts in Western blot may not work in IHC across species due to differences in epitope accessibility in fixed tissues.

• Surrogate antibody considerations: For in vivo studies or when studying species-specific functions, consider using species-specific antibodies (also called homologous or surrogate antibodies) that recognize the appropriate species target with similar potency to the clinical molecule .

• Affinity considerations: Even among cross-reactive antibodies, binding affinity may vary across species. Quantitative binding assays (SPR, Bio-Layer interferometry) can determine if affinity differences will affect experimental outcomes .

When publishing cross-species studies, document the validation performed for each species to ensure reproducibility and proper interpretation of results.

What immunotoxicity assessments should be conducted when developing therapeutic antibodies targeting MTPN?

While MTPN itself is not currently a major therapeutic antibody target, the principles of immunotoxicity assessment apply to any therapeutic antibody development program:

• Target biology risk assessment:

  • Conduct literature review on MTPN immunobiology, examining expression patterns in immune cells

  • Assess potential unintended modulation of related immune mechanisms

  • Determine tissue expression in both healthy and diseased states across species

  • Evaluate data from genetic models (knockout mice, humans with mutations)

• In vitro immunopharmacology studies:

  • Determine binding specificity across immune system components using flow cytometry and competitive immunoassays

  • Assess tissue cross-reactivity through immunohistochemistry

  • Measure relative affinity and immunopharmacological activity in both human and animal cells

  • Characterize complete dose-response curves to identify potential bell-shaped responses that could indicate complex pharmacology

• Cytokine release assessment:

  • Test antibody candidates in whole blood cytokine release assays

  • Consider testing under various conditions (soluble, immobilized) as demonstrated with TGN1412 testing

  • Assess for pro-inflammatory cytokine production (TNF, IL-6, IL-8)

• Immunogenicity prediction:

  • Analyze for T-cell epitopes that might trigger anti-drug antibody responses

  • Consider deimmunization strategies if necessary

• In vivo toxicology species selection:

  • Confirm that the selected species expresses MTPN with similar tissue distribution to humans

  • Verify that the test antibody binds with comparable affinity to the animal target

  • Ensure similar immunopharmacological effects in the test species

A tiered approach to assessing effects on immune status, immune function, and risk of infection and cancer should be implemented, governed by the antibody's mechanism of action and structural features .

How can computational approaches improve the design of high-specificity anti-MTPN antibodies?

Recent advances in computational biology offer powerful approaches to enhance the design of highly specific anti-MTPN antibodies:

• Sequence-based specificity prediction:

  • Machine learning models trained on phage display data can predict antibody specificity profiles without requiring experimental testing of all variants

  • These models can disentangle different binding modes associated with specific ligands, even for chemically similar epitopes

  • This approach enables computational design of antibodies with customized specificity profiles—either with high specificity for a particular target or with cross-specificity for multiple targets

• Structural design for specificity:

  • If structural data exists for MTPN, computational modeling can identify unique surface features to target

  • Molecular dynamics simulations can predict conformational epitopes and design antibodies with complementary binding surfaces

  • Energy minimization algorithms can optimize binding interfaces for both affinity and specificity

• Machine learning for developability prediction:

  • Models can predict antibody properties such as percentage of monomer after purification

  • Input features include amino acid sequences of variable regions, molecular formats, germlines and germline pairings, and calculated physicochemical properties

  • This approach can identify candidates with improved stability without extensive experimental testing

• Epitope-specific targeting:

  • Computational analysis of multiple sequence alignments can identify regions unique to MTPN versus related proteins

  • Models can predict epitopes that would generate antibodies with minimal cross-reactivity to similar proteins

  • This is particularly valuable when high specificity is required to discriminate between closely related protein family members

• Integration with experimental validation:

  • Most successful approaches combine computational prediction with experimental validation

  • High-throughput sequencing data from selection experiments can be used to train models for improved accuracy

  • Iterative design-build-test cycles incorporating both computational and experimental data yield superior antibodies

These computational approaches can significantly reduce the time and resources required for antibody development while producing candidates with superior specificity and developability profiles.