DRP1D Antibody

What is DRP1 and what cellular functions does it regulate?

DRP1 (Dynamin-related protein 1, also known as DNM1L) functions in mitochondrial and peroxisomal division. It mediates membrane fission through oligomerization into membrane-associated tubular structures that wrap around scission sites to constrict and sever mitochondrial membranes through a GTP hydrolysis-dependent mechanism .

DRP1 plays several critical cellular roles:

• Mediates mitochondrial fission during mitosis

• Ensures survival of postmitotic neurons by suppressing oxidative damage

• Required for normal brain development, including cerebellum development

• Acts downstream of PINK1 to promote mitochondrial fission in a PRKN-dependent manner

• Facilitates developmentally regulated apoptosis

What experimental techniques are compatible with DRP1 antibodies?

DRP1 antibodies have been validated for multiple experimental applications:

The specific application compatibility varies by antibody clone. For instance, the EPR19274 clone has been cited in over 220 publications and is validated for multiple applications including Western blot, IHC, ICC/IF, Flow cytometry, and IP .

How should I select between monoclonal and polyclonal DRP1 antibodies for my research?

Selection should be based on your specific experimental needs:

Monoclonal antibodies (e.g., EPR19274, 3B5, OTI4F6):

• Provide higher specificity targeting a single epitope

• Offer better reproducibility between batches

• Ideal for specific detection of DRP1 in applications requiring precise epitope recognition

• Recommended for studies focusing on specific DRP1 modifications or interactions

Polyclonal antibodies:

• Can recognize multiple epitopes on DRP1

• May provide stronger signals through binding multiple sites

• Useful when protein conformation might hide some epitopes

• Better for applications like immunoprecipitation where binding multiple epitopes is advantageous

When selecting antibodies, consider whether the experiment requires detection of specific phosphorylation states (e.g., p-Drp1 S616) which would necessitate phospho-specific antibodies rather than total DRP1 antibodies .

What are the recommended protocols for validating DRP1 antibody specificity?

Antibody validation is crucial for ensuring experimental reliability. A comprehensive validation protocol should include:

• Western blot analysis with positive and negative controls:

  • Use known DRP1-expressing cell lines (e.g., HeLa, NIH/3T3, PC-12)

  • Include knockdown/knockout samples as negative controls

  • Verify the correct molecular weight bands (reported bands at 83-86 kDa)

• Immunoprecipitation validation:

  • Perform IP followed by Western blot detection

  • Include appropriate IgG controls (e.g., rabbit IgG monoclonal for EPR19274)

  • Confirm specific pulldown of DRP1 from cellular lysates

• Immunohistochemistry/Immunofluorescence controls:

  • Use tissues with known DRP1 expression patterns (e.g., mouse cerebrum)

  • Include blocking peptide controls

  • Validate subcellular localization patterns

• Multi-tissue microarray (TMA) validation for IHC applications to confirm specificity across different tissue types .

• Biophysical characterization to confirm antibody identity at the molecular level for batch-to-batch consistency .

How can I troubleshoot inconsistent results when using DRP1 antibodies in Western blotting?

Inconsistent Western blot results with DRP1 antibodies can occur for several reasons:

• Antibody batch variation:

  • Different batches of antibodies, even from the same supplier, can yield different staining patterns

  • Solution: Validate each new antibody batch against a standard sample

• Post-translational modifications:

  • DRP1 can be SUMOylated, which affects band patterns

  • Phosphorylation at sites like S616 alters protein migration

  • Solution: Include phosphatase/dephosphorylation controls when needed

• Sample preparation issues:

  • Mitochondrial proteins can be sensitive to extraction methods

  • Solution: Use standardized lysis buffers optimized for mitochondrial proteins

• Multiple isoforms:

  • DRP1 has multiple isoforms (predicted bands at 82, 86, 94 kDa)

  • Solution: Use positive control samples with known isoform expression

• Aggregation during sample preparation:

  • Solution: Add fresh protease inhibitors and avoid freeze-thaw cycles

To systematically troubleshoot, run a validation panel with known positive controls (e.g., HeLa, NIH/3T3, PC-12 cell lysates) alongside your experimental samples to identify where the inconsistency occurs.

How can I effectively study DRP1 phosphorylation states in different experimental contexts?

DRP1 activity is regulated by post-translational modifications, particularly phosphorylation at sites like S616. For effective phosphorylation analysis:

• Antibody selection:

  • Use phospho-specific antibodies (e.g., anti-p-Drp1 S616) alongside total DRP1 antibodies

  • Verify the specificity of phospho-antibodies with dephosphorylation controls

• Preservation of phosphorylation status:

  • Add phosphatase inhibitors to all lysis buffers

  • Maintain samples at 4°C during processing

  • Avoid repeated freeze-thaw cycles

• Quantification approach:

  • Always normalize phospho-DRP1 signal to total DRP1 levels

  • Use immunofluorescence to examine spatial distribution of phosphorylated DRP1

• Experimental design considerations:

  • Include appropriate positive controls (e.g., treatments known to induce specific phosphorylation)

  • For studying the PD1-ERK/Drp1 pathway, measure both p-Drp1 S616 and p-ERK1/2 T202Y204

Research data indicates that the expression levels of p-Drp1 S616 and p-ERK1/2 T202Y204 can be significantly altered in different experimental conditions, such as T cell activation. For instance, in one study, the expression levels of p-Drp1 S616 were significantly higher (9.67 ± 3.21) in the oeDT plus pembrolizumab group compared to the shDT plus pembrolizumab group (5.33 ± 1.53) .

How does DRP1 expression impact T cell function in immunotherapy research?

Research indicates that DRP1 plays a critical role in T cell immune responses, particularly relevant for immunotherapy applications:

• T cell activation and function:

  • High expression levels of Drp1 positively regulate T cell activation

  • Drp1 overexpression enhances T cell-induced suppression of lung cancer cells

  • Drp1-mediated mitochondrial fission is important for T cell activation, proliferation, and migration

• Cytokine production:

  • T cells with high Drp1 expression (oeDT) secrete higher levels of INF-γ, granzyme B, perforin, and TNF-α compared to wild-type or Drp1-knockdown T cells

  • The table below shows cytokine production by different T cell groups:

• Synergy with immunotherapy:

  • Drp1 overexpression significantly enhances the antitumor immune response of PD-1 inhibitors like pembrolizumab

  • The mechanism involves the PD-1-ERK/Drp1 pathway in T cells

• Methodological considerations:

  • Use flow cytometry to assess T cell activation markers alongside Drp1 expression

  • Include appropriate controls when manipulating Drp1 expression (e.g., shNT and oeNT controls)

  • Consider the effects of PD-1 pathway activation on Drp1 phosphorylation and function

What methodological approaches can I use to study DRP1's role in mitochondrial dynamics?

To effectively investigate DRP1's function in mitochondrial dynamics:

• Live-cell imaging approaches:

  • Use fluorescent protein-tagged DRP1 for real-time visualization

  • Combine with mitochondrial markers to track fission events

  • Consider photoactivatable fluorescent proteins for pulse-chase experiments

• Genetic manipulation strategies:

  • Use siRNA/shRNA for transient knockdown experiments

  • CRISPR-Cas9 for complete knockout studies

  • Site-directed mutagenesis to study specific phosphorylation sites

  • Overexpression systems for gain-of-function studies

• Biochemical fractionation:

  • Separate mitochondrial and cytosolic fractions to assess DRP1 recruitment

  • Use antibodies specific to different phosphorylation states (e.g., p-Drp1 S616)

  • Quantify the ratio of mitochondrial to cytosolic DRP1 as a measure of recruitment

• Super-resolution microscopy:

  • Techniques like STORM or PALM can visualize DRP1 oligomerization at fission sites

  • Use DRP1-specific antibodies for immunofluorescence staining

  • Co-stain for interaction partners like MFF, MIEF1, and MIEF2

• Functional assays:

  • Measure mitochondrial network morphology after manipulating DRP1 expression/activity

  • Assess mitochondrial membrane potential and respiratory function

  • Correlate with cellular phenotypes (e.g., apoptosis, proliferation)

How can I address anti-drug antibody (ADA) responses that might interfere with DRP1 antibody detection in patients receiving biologics?

In research involving patients receiving biologic therapies, anti-drug antibodies (ADAs) can potentially interfere with experimental detection of proteins like DRP1:

• Multi-tiered ADA testing approach:

  • Implement a hierarchical testing scheme for ADA screening, confirmation, and characterization

  • Use appropriate controls to distinguish between ADAs and target protein antibodies

• Sample preparation considerations:

  • Pre-clear samples with protein A/G to remove interfering antibodies

  • Consider acid dissociation methods to disrupt immune complexes

  • Use blocking reagents specific to human antibodies when working with patient samples

• Data management and interpretation:

  • Map ADA data to standardized CDISC formats for consistent analysis

  • Distinguish between neutralizing antibodies (NAbs) and non-neutralizing antibodies (non-NAbs)

  • Consider the timing of sample collection relative to biologic administration

• Impact assessment:

  • Analyze how ADAs affect pharmacokinetics (PK) and pharmacodynamics (PD) parameters

  • Correlate ADA titers with experimental measures of DRP1

  • Consider stratifying analyses based on ADA status

How do I correctly interpret DRP1 antibody data in the context of cross-reactivity and epitope specificity?

Accurate interpretation of DRP1 antibody results requires careful consideration of specificity issues:

• Epitope mapping:

  • Identify which region of DRP1 your antibody recognizes

  • Consider whether post-translational modifications might affect epitope recognition

  • Use bioinformatics tools to predict potential cross-reactive proteins

• Cross-reactivity assessment:

  • Test antibodies against multiple cell/tissue types with varying DRP1 expression

  • Include knockout/knockdown controls to confirm specificity

  • Consider testing against related proteins (e.g., other dynamin family members)

• Binding mode analysis:

  • Different antibodies may have different binding modes to DRP1

  • These binding modes can affect detection of specific conformational states

  • Computational models can help predict and disentangle these modes

• Advanced validation approaches:

  • Use high-throughput sequencing to assess antibody specificity profiles

  • Employ computational design approaches to enhance antibody specificity

  • Consider the use of biophysics-informed modeling to improve specificity

• Data interpretation guidelines:

  • Always include appropriate positive and negative controls

  • Consider complementary methods for validation (e.g., mass spectrometry)

  • Be cautious about interpreting results from a single antibody or technique

What emerging technologies are enhancing DRP1 antibody design and specificity?

Several cutting-edge approaches are revolutionizing antibody development for research applications:

• AI-driven antibody design:

  • RFdiffusion technology has been fine-tuned to design human-like antibodies

  • This approach can generate complete single chain variable fragments (scFvs)

  • The model produces new antibody blueprints unlike any seen during training that bind user-specified targets

• Computational modeling for specificity prediction:

  • Biophysics-informed models can predict antibody binding modes

  • These models can disentangle binding modes even for chemically similar ligands

  • They enable the design of antibodies with customized specificity profiles

• High-throughput sequencing for epitope mapping:

  • Tools like ExpoSeq simplify analysis of high-throughput sequencing data

  • Rarefaction curves can assess if sequencing depth is sufficient to cover the majority of antibody sequences

  • These approaches enable more comprehensive epitope mapping

• Structure-based design approaches:

  • Using protein structural information to predict effects of amino acid substitutions

  • Design of rational amino acid substitutions to "de-immunize" proteins

  • Attachment of epitope-masking moieties to reduce immunogenicity

• T-cell epitope prediction methods:

  • In silico approaches to predict T-cell epitopes

  • Experimental validation using T-cell proliferation assays and cytokine secretion assays

  • These methods help design antibodies with reduced immunogenicity

How can advanced computational methods improve DRP1 antibody specificity and research applications?

Computational approaches are increasingly important for enhancing antibody research:

• Sequence optimization algorithms:

  • Optimize antibody sequences for specificity to DRP1 over related proteins

  • Generate cross-specific sequences by jointly minimizing energy functions

  • Create specific sequences by minimizing energy for desired targets while maximizing for undesired targets

• Predictive modeling for batch-to-batch consistency:

  • Models that predict how manufacturing variations affect antibody performance

  • Quality control algorithms to ensure consistent antibody function

  • Early identification of potential specificity issues

• Immunogenicity prediction:

  • Computational methods to predict and reduce protein immunogenicity

  • Rational approaches to "de-immunize" proteins through amino acid substitutions

  • These methods can improve effectiveness and safety profiles of antibody-based tools

• Epitope binning and mapping:

  • Computational approaches to identify antibody binding sites

  • Prediction of conformational epitopes based on protein structure

  • Tools to distinguish between overlapping and non-overlapping epitopes

• Integration with experimental data:

  • Machine learning approaches that combine computational predictions with experimental validation

  • Feedback loops to improve model accuracy based on experimental outcomes

  • Hybrid approaches that maximize the strengths of both computational and experimental methods