RRT2 Antibody

What is RRT2/DPH7 and why is it relevant to study using antibodies?

RRT2 (also known as DPH7 or WDR85) is a protein that catalyzes the demethylation of diphthine methyl ester to form diphthine, an intermediate in diphthamide biosynthesis. This post-translational modification occurs in translation elongation factor 2 (EEF2), which can be ADP-ribosylated by diphtheria toxin and Pseudomonas exotoxin A .

In humans, the DPH7 gene is located on chromosome 9 (C9orf112) and encodes the WD repeat-containing protein 85. The protein plays crucial roles in:

• Cancer research pathways

• Metabolic signaling

• Signal transduction mechanisms

Studying RRT2/DPH7 using antibodies allows researchers to:

• Detect and quantify protein expression levels

• Examine subcellular localization

• Investigate protein interactions

• Assess post-translational modifications

How do I select the appropriate RRT2 antibody for my specific research application?

When selecting an RRT2/DPH7 antibody for your research, consider the following methodological approach:

• Define your experimental application first:

  • For protein localization studies: Choose antibodies validated for immunohistochemistry (IHC) or immunofluorescence (IF)

  • For protein quantification: Select antibodies validated for ELISA or Western blotting

  • For protein interaction studies: Use antibodies suitable for co-immunoprecipitation

• Consider antibody formats based on application:

  • Unconjugated antibodies: Versatile for multiple applications with secondary detection

  • Direct conjugates (FITC, HRP, PE, Biotin): Reduce steps in workflows and eliminate cross-reactivity issues

• Verify species reactivity:

  • Human-reactive antibodies for clinical or human cell line research

  • Yeast-reactive antibodies for S. cerevisiae studies

  • Consider cross-reactivity if working with multiple model organisms

• Check antibody clonality:

  • Polyclonal antibodies: Better for detecting native proteins and providing robust signals

  • Monoclonal antibodies: Offer higher specificity for particular epitopes

• Review validation data:

  • The Antibody Registry provides an open database that enables citation of antibodies through Research Resource Identifiers (RRIDs)

  • Check if antibodies have been previously validated in published literature

What validation strategies should I employ to confirm RRT2 antibody specificity?

Antibody validation is critical for ensuring experimental reproducibility. For RRT2/DPH7 antibodies, employ these methodological approaches:

• Positive and negative controls:

  • Use cell lines or tissues with known DPH7 expression levels

  • Include knockout/knockdown samples as negative controls

  • Consider overexpression systems for positive controls

• Multiple antibody validation:

  • Test multiple antibodies targeting different epitopes of RRT2/DPH7

  • Compare antibodies recognizing N-terminal versus C-terminal regions

  • Cross-validate results between antibodies from different sources

• Epitope-specific validation:

  • Consider antibodies targeting specific regions (e.g., AA 23-51, AA 210-325)

  • For phospho-specific research, validate with dephosphorylation treatments

• Independent method correlation:

  • Correlate protein detection with mRNA expression

  • Compare results with mass spectrometry data

  • Validate subcellular localization with GFP-tagged constructs

• Use Research Resource Identifiers (RRIDs):

  • RRIDs improve research reproducibility by ensuring antibodies are clearly identifiable

  • The Antibody Registry has cataloged 343,126 antibody uses in literature from February 2014 to August 2022

  • Journals with active RRID requirements show >90% compliance, while passive requests result in only ~1% compliance

How should I properly store and handle RRT2 antibodies to maintain their functionality?

Proper storage and handling are crucial for maintaining antibody functionality. Follow these methodological guidelines:

• Storage conditions:

  • Upon receipt, store at -20°C or -80°C as recommended

  • Avoid repeated freeze-thaw cycles by preparing small aliquots

  • For short-term storage (less than one week), some antibodies can be stored at 4°C

• Buffer considerations:

  • Most RRT2/DPH7 antibodies are supplied in buffers containing:

    • 50% Glycerol

    • 0.01M PBS, pH 7.4

    • 0.03% Proclin 300 as preservative

  • Avoid introducing contaminants into the stock

• Handling protocols:

  • Centrifuge vials briefly before opening to collect liquid that may be trapped in the cap

  • Use sterile technique when handling antibody solutions

  • Never vortex antibodies; mix gently by inverting or flicking the tube

• Working dilution preparation:

  • Prepare fresh working dilutions on the day of the experiment

  • Use appropriate diluents compatible with your application

  • For IHC applications, typical working dilutions range from 1:20-1:200

  • For IF applications, typical working dilutions range from 1:50-1:200

• Quality control measures:

  • Document lot numbers, receipt dates, and aliquoting dates

  • Periodically test antibody activity with positive controls

  • Be aware that antibody performance can vary between lots

How can RRT2/DPH7 antibodies be used to investigate cardiac rhythm disorders in autoimmune diseases?

Recent research has established connections between certain antibodies and cardiac rhythm disorders in systemic autoimmune diseases (SADs). While this specific research focused on anti-Ro/SSA antibodies rather than RRT2/DPH7 antibodies directly, the methodological approach provides valuable insights:

• Experimental design considerations:

  • A cross-sectional single-center study conducted in a tertiary hospital between January 2021 and March 2022 demonstrated significant associations between antibody presence and cardiac rhythm disorders

  • Key inclusion criteria: Adult patients with confirmed SAD diagnosis and previous antibody testing

  • Comprehensive follow-up using 12-lead electrocardiograms for all participants

• Statistical analysis methodology:

  • The study found a statistically significant relationship between anti-Ro52 positivity and cardiac rhythm disorders (relative risk = 2.007 [1.197–3.366])

  • Specifically, QTc prolongation showed strong association (relative risk = 4.248 [1.553–11.615])

  • Multivariate regression revealed diabetes mellitus as a significant comorbidity factor

• Research application for RRT2/DPH7 antibodies:

  • Similar methodological approaches could be applied to investigate potential associations between RRT2/DPH7 and disease states

  • Patient stratification based on antibody status (positive/negative)

  • Correlation of antibody titers with clinical manifestations

  • Analysis of antibody strength and target specificity in relation to disease severity

This research paradigm demonstrates how antibodies can be used beyond simple detection to establish clinical associations with specific disease manifestations. Similar approaches could be adapted for RRT2/DPH7 investigations in relevant disease contexts.

What are the optimal protocols for generating and validating phospho-specific antibodies against proteins like RRT2/DPH7?

Generating phospho-specific antibodies requires specialized methodologies, as illustrated by research on tau phosphorylation. These approaches can be adapted for RRT2/DPH7:

• Peptide design strategy:

  • Design synthetic peptides representing specific segments of RRT2/DPH7 containing the targeted phosphorylation sites

  • Consider including both phosphorylated and non-phosphorylated versions of the same peptide for comparison

  • Example: In tau protein research, peptides of varying lengths (163-176, 165-176, and 163-179) with phosphorylated threonine residues were tested

• Antibody production optimization:

  • Initial attempts with shorter peptides (165-176, 163-176) were unsuccessful

  • Success was achieved only after expanding the peptide length to 163-179

  • This suggests that adequate epitope context is crucial for phospho-specific antibody generation

• Validation through multiple assays:

  • ELISA testing against both phosphorylated and non-phosphorylated peptides

  • Western blotting against native and dephosphorylated protein samples

  • Immunoprecipitation followed by mass spectrometry to confirm target specificity

• Potential challenges:

  • The research noted: "Owing to the apparent difficulty in producing phosphorylation-specific antibodies targeting these epitopes..." suggesting inherent technical challenges

  • Even with carefully designed peptides, antibodies may recognize the regional epitope but not necessarily the phosphorylation state

• Alternative approaches:

  • Consider developing antibodies that recognize specific protein conformations resulting from phosphorylation

  • Use mass spectrometry as a complementary technique to verify phosphorylation states

Understanding these complexities is essential for researchers attempting to develop phospho-specific antibodies against RRT2/DPH7 or using such antibodies in their research protocols.

How can advanced statistical methods improve antibody selection for predictive biomarker studies involving RRT2?

Advanced statistical approaches can significantly enhance antibody selection strategies for biomarker studies, as demonstrated in recent research:

• Hybrid parametric/non-parametric approach:

  • Apply Box-Cox transformations to normalize antibody measurement distributions

  • Maximize evidence for normal distributions with homogeneous variances between comparison groups

  • Search for optimal transformation parameters within defined intervals (e.g., -4 to 4)

• Optimal cut-point determination methodology:

  • Calculate a test statistic for each possible cut-point value

  • Select the cut-point that maximizes discriminatory ability between patient groups

  • Calculate associated p-values using χ² tests for statistical significance

• Ensemble machine learning implementation:

  • Combine multiple classifier methods for robust predictions:

    • Logistic regression models (LRM) with main effects

    • Random Forest (RF)

    • Linear discriminant analysis (LDA)

    • Quadratic discriminant analysis (QDA)

    • Extreme gradient boosting (XGB)

  • Pool individual predictions using weighted averages calculated by cross-validation

• Application to RRT2 antibody research:

  • These methods could identify optimal RRT2 antibody concentration thresholds for disease classification

  • Help determine which specific RRT2 epitopes provide the most discriminatory power

  • Enable selection of the most predictive antibody formats for specific clinical applications

• Software implementation:

  • Statistical packages like "OptimalCutpoints" can minimize distance to ideal sensitivity/specificity

  • "pROC" can estimate ROC curves for antibody performance

  • "sn" can perform linear regression models with Skew-Normal or Skew-t distributions

This methodological framework provides a rigorous approach to antibody selection that goes beyond traditional methods, potentially improving the predictive value of RRT2 antibodies in biomarker studies.

How can I integrate RRT2 antibodies into multi-parameter analysis of cell-free DNA in disease models?

Recent research on cell-free DNA (cfDNA) in sepsis patients provides methodological insights that could be applied to integrating RRT2 antibody data:

• Comprehensive sampling protocol:

  • Collect samples at multiple timepoints (e.g., screening visit, in-hospital visits)

  • Store samples appropriately for downstream molecular analysis

  • Record relevant clinical parameters through electronic patient data management systems

• Multi-parameter statistical analysis:

  • Binary logistic regression models can integrate antibody data with other biomarkers

  • Example models demonstrated in sepsis research:

    • Model A: Single parameter (SCr)

    • Model B: Multiple parameters (mt-ND1, mt-CO3, n-Rps18)

    • Model C: Combined parameters (SCr, mt-ND1, mt-CO3, n-Rps18)

• Evaluation of multicollinearity:

  • Calculate variance inflation factor (VIF) to assess multicollinearity

  • Values below 10 indicate acceptable levels of multicollinearity

  • This ensures reliable interpretation of antibody contribution to prediction models

• ROC curve analysis methodology:

  • Calculate model-based predicted probabilities for each patient

  • Generate ROC curves to assess discriminatory performance

  • Report sensitivity and specificity at optimal cut-offs based on Youden's index

• Adaptation for RRT2 antibody research:

  • Integrate RRT2 antibody measurements with other biomarkers

  • Assess incremental value of RRT2 data to existing predictive models

  • Develop multi-parameter panels that include RRT2 alongside other disease-relevant markers

This methodological framework provides a robust approach to incorporating antibody data into complex biomarker panels, potentially enhancing the diagnostic or prognostic value of RRT2 antibodies in various disease contexts.

What are the best practices for using computational modeling to predict how mutations affect RRT2 antibody binding?

Advanced computational methods can predict how antigen mutations impact antibody binding, as demonstrated in recent research:

• Structural modeling approach:

  • Generate high-quality protein structure models using state-of-the-art methods like ABodyBuilder2

  • ABodyBuilder2 incorporates deep learning for improved antibody structure prediction

  • Run multiple modeling iterations to capture statistical uncertainty in side chain positioning

• Interface property calculation:

  • Calculate approximately 40 features of interfaces between antibodies and antigens using protocols like Rosetta Interface Analyzer

  • Determine differences between wildtype and mutant values for each feature

  • Apply partial least squares regression (PLSR) to develop predictive models

• Model refinement methodology:

  • Initial PLSR models achieved R² of 0.6869 in predicting experimental ΔΔG values

  • Removing 25 features that contributed least to the model resulted in a final model with R² of 0.6403

  • This approach balances model complexity with predictive power

• Dynamic simulation considerations:

  • Molecular dynamics simulations can validate static predictions

  • Ensemble of repeat static predictions shows high consistency with simulation-average results

  • This approach is several orders of magnitude faster than running full molecular dynamics

• Application to RRT2 antibody research:

  • Predict how mutations in RRT2/DPH7 might affect antibody recognition

  • Identify critical binding residues for epitope mapping

  • Design improved antibodies with enhanced specificity or affinity

These computational approaches provide powerful tools for understanding and optimizing antibody-antigen interactions, facilitating more rational design and selection of RRT2 antibodies for specific research applications.

How can I address inconsistent results when using RRT2 antibodies across different experimental platforms?

Inconsistent results across platforms can arise from multiple factors. Apply these methodological troubleshooting approaches:

• Systematic platform comparison:

  • Document all experimental variables across platforms (buffers, incubation times, detection methods)

  • Run controlled experiments with identical samples across platforms

  • Establish correlation coefficients between methods

• Epitope accessibility assessment:

  • Different experimental conditions can affect epitope exposure

  • For Western blotting: Test multiple denaturation conditions

  • For immunohistochemistry: Compare various fixation and antigen retrieval methods

  • For flow cytometry: Optimize permeabilization protocols

• Validation through orthogonal methods:

  • Verify findings using independent detection methods

  • Consider mass spectrometry confirmation of key findings

  • Use genetic approaches (knockdown/knockout) to confirm specificity

• Record-keeping best practices:

  • Document antibody lot numbers, as performance can vary between lots

  • Record exact experimental conditions including buffers, incubation times, and temperatures

  • Implement Research Resource Identifiers (RRIDs) in laboratory notebooks and publications

• Standardization protocol:

  • Develop a standard operating procedure (SOP) for each application

  • Include positive and negative controls in every experiment

  • Consider using calibration standards when applicable

What metrics should I use to evaluate and report RRT2 antibody performance in publications?

Proper reporting of antibody performance is crucial for research reproducibility. Follow these guidelines:

• Essential documentation elements:

  • Always include catalog number and supplier information

  • Report the Research Resource Identifier (RRID) for the antibody

  • Provide lot number when available, especially if inconsistencies are noted

  • Specify the host species, clonality, and isotype

• Application-specific performance metrics:

  • Western Blot: Include molecular weight markers, exposure times, and full blot images

  • ELISA: Report standard curves, limits of detection, and coefficients of variation

  • IHC/IF: Document fixation method, antigen retrieval, dilution factors, and exposure settings

  • Flow cytometry: Include gating strategies, compensation controls, and fluorophore information

• Validation reporting standards:

  • Describe all validation experiments performed

  • Include controls used (positive, negative, isotype)

  • Report quantitative measures of specificity and sensitivity when possible

• Statistical approach:

  • Provide appropriate statistical analyses of antibody performance

  • Include technical and biological replication information

  • Report confidence intervals for quantitative measurements

• Journal-specific requirements:

  • Many journals now require Key Resources Tables including RRIDs

  • Journals with active RRID requirements show >90% compliance

  • Some journals use checklists or personal emails from editors to ensure compliance

How does antibody target specificity influence experimental design and data interpretation for RRT2 research?

Understanding antibody specificity is fundamental to experimental design and interpretation:

• Epitope-based experimental design:

  • Map experiments to specific domains of RRT2/DPH7 protein

  • Consider antibodies targeting different regions (e.g., AA 23-51 vs. AA 210-325)

  • Design experiments to account for potential cross-reactivity with related proteins

• Specificity validation protocol:

  • Implement a multi-step validation workflow:

    1. In silico analysis of potential cross-reactivity

    2. Western blot analysis for off-target binding

    3. Immunoprecipitation followed by mass spectrometry

    4. Testing with knockout/knockdown systems

• Cross-reactivity assessment:

  • Test antibodies against closely related proteins

  • Perform epitope conservation analysis across species

  • Consider potential reactivity with post-translationally modified forms

• Data interpretation framework:

  • Always interpret results in context of antibody specificity limitations

  • Consider alternative explanations for unexpected findings

  • Verify key findings with multiple antibodies targeting different epitopes

• Documentation standards:

  • Record all specificity data in laboratory notebooks

  • Include detailed specificity information in publications

  • Follow field-specific reporting guidelines for antibody characterization

How can RRT2 antibodies be integrated into systems biology approaches for understanding diphthamide synthesis pathways?

Systems biology integration requires comprehensive methodological approaches:

• Multi-omics experimental design:

  • Combine RRT2 antibody-based proteomics with transcriptomics and metabolomics

  • Map protein-protein interactions using co-immunoprecipitation with RRT2 antibodies

  • Correlate RRT2/DPH7 protein levels with diphthamide pathway intermediates

• Network analysis methodology:

  • Generate protein interaction networks centered on RRT2/DPH7

  • Identify key regulatory nodes using centrality measures

  • Apply machine learning to predict functional relationships

• Temporal dynamics assessment:

  • Design time-course experiments to track RRT2/DPH7 expression

  • Use pulse-chase approaches to determine protein turnover rates

  • Correlate RRT2/DPH7 dynamics with diphthamide synthesis efficiency

• Cellular heterogeneity analysis:

  • Apply single-cell approaches using RRT2 antibodies in flow cytometry

  • Correlate RRT2/DPH7 expression with cellular phenotypes

  • Investigate cell-type specific regulation of diphthamide synthesis

• Integration with structural biology:

  • Use antibody epitope mapping to inform structural models

  • Correlate computational structure predictions with experimental data

  • Apply molecular dynamics simulations to understand functional mechanisms

What emerging technologies are enhancing the development and application of RRT2 antibodies in research?

Several emerging technologies are transforming antibody research:

• Deep learning for structure prediction:

  • ABodyBuilder2 represents a state-of-the-art deep-learning based approach for antibody structure prediction

  • This enables better understanding of antibody-antigen interactions

  • Repeat modeling captures statistical uncertainty in side chain positioning

• High-throughput antibody validation platforms:

  • Organizations like YCharOS rapidly disseminate antibody validation data

  • F1000 Antibody Validations gateway contains articles supporting antibody specificity

  • The RRID portal allows filtering by whether users have submitted validation data

• TAP (Therapeutic Antibody Profiling) methodology:

  • Incorporates computational metrics to assess antibody developability

  • Evaluates parameters such as Total IMGT CDR Length, Patches of Surface Hydrophobicity, and Structural Fv Charge Symmetry

  • Provides green/amber/red risk classification based on threshold values

• Antibody engineering advancements:

  • Site-specific conjugation technologies for more homogeneous antibody reagents

  • Recombinant antibody fragments with enhanced tissue penetration

  • Bispecific formats that can simultaneously target RRT2/DPH7 and other proteins

• Single-cell antibody secretion profiling:

  • Microfluidic approaches for isolating antibody-secreting cells

  • Rapid screening of antibody specificity and affinity

  • Direct sequencing of antibody genes from individual cells

These technologies provide researchers with powerful new tools for developing, validating, and applying RRT2 antibodies in increasingly sophisticated research contexts.

What are the most reliable repositories and databases for RRT2 antibody information?

Several key repositories provide validated information on antibodies:

• The Antibody Registry:

  • Public, open database providing persistent records for antibody reagents

  • Authority for antibody Research Resource Identifiers (RRIDs)

  • Most comprehensive listing of persistently identified antibody reagents

  • Contains 343,126 documented antibody uses in literature (2014-2022)

  • Website: https://antibodyregistry.org

• RRID Portal:

  • Allows searching for antibodies with filtering options

  • Includes validation data submitted by users or organizations

  • Improves research reproducibility through clear resource identification

  • Can access computationally via API with .json suffix

• F1000 Antibody Validations Gateway:

  • Contains validation data supporting antibody specificity

  • Includes reviews about antibody validation methodologies

  • Provides peer-reviewed antibody characterization data

• UniProt:

  • Contains comprehensive protein sequence and function information

  • Provides RRT2/DPH7 protein data under accession numbers:

    • Human: Q9BTV6

    • Yeast: P38332

• Hypothes.is Database:

  • Contains annotations on papers using specific antibodies

  • Accessible via API for programmatic access

  • Supports the SciBot semi-automated workflow for tracking RRIDs

These resources provide researchers with validated information on antibodies, supporting reproducible and reliable research practices.

How can I contribute to improving RRT2 antibody standardization in the research community?

Researchers can significantly improve antibody standardization through several methodological approaches:

• Implement RRID citation practices:

  • Always include RRIDs when referencing antibodies in publications

  • Follow journal guidelines for antibody reporting

  • Include complete antibody information in methods sections

• Submit validation data to repositories:

  • Share antibody validation results with the research community

  • Upload data to the Antibody Registry or F1000 Antibody Validations gateway

  • Include both positive and negative validation results

• Develop and share standardized protocols:

  • Create detailed SOPs for RRT2 antibody applications

  • Include optimization parameters and troubleshooting guides

  • Make protocols available through protocol repositories

• Participate in community standardization efforts:

  • Join collaborative projects focused on antibody validation

  • Contribute to multi-laboratory validation studies

  • Participate in the development of reporting standards

• Address behavioral factors in antibody selection:

  • Consider the determinants of end-user antibody choice

  • Implement practices that prioritize validation over convenience

  • Recognize that antibody choice is a critical behavioral driver of reproducibility problems