Recombinant Mouse Arylacetamide deacetylase-like 3 (Aadacl3)

What is Arylacetamide deacetylase-like 3 (Aadacl3) and how is it classified?

Arylacetamide deacetylase-like 3 (Aadacl3) is a protein belonging to the 'GDXG' lipolytic enzyme family. In mice, it is encoded by the Aadacl3 gene and consists of 408 amino acids. The enzyme is believed to possess lipolytic activity, potentially functioning in the hydrolysis of specific lipid substrates. As a member of the 'GDXG' lipolytic enzyme family, it shares structural and functional characteristics with other serine hydrolases, including the presence of a catalytic triad critical for enzymatic activity .

What are the key differences between AADAC and AADACL3?

Although both AADAC (Arylacetamide deacetylase) and AADACL3 (Arylacetamide deacetylase-like 3) belong to the same enzyme family, they differ in several important aspects:

AADAC has been shown to play a significant role in the hydrolysis of abiraterone acetate, an important prodrug used in treating metastatic castration-resistant prostate cancer . The specific physiological and pharmacological roles of AADACL3 require further investigation.

What are the optimal conditions for reconstitution and storage of recombinant Aadacl3 protein?

For optimal reconstitution and storage of recombinant mouse Aadacl3 protein, researchers should follow these guidelines:

• Prior to opening, briefly centrifuge the vial to bring contents to the bottom

• Reconstitute the lyophilized protein in deionized sterile water to a concentration of 0.1-1.0 mg/mL

• For long-term storage, add glycerol to a final concentration of 5-50% (with 50% being the recommended default)

• Aliquot the reconstituted protein to avoid repeated freeze-thaw cycles

• Store working aliquots at 4°C for up to one week

• For longer storage, keep aliquots at -20°C/-80°C

The reconstituted protein will be in Tris/PBS-based buffer with 6% Trehalose at pH 8.0, which maintains protein stability while preserving its native conformation .

How should researchers approach designing expression systems for producing recombinant Aadacl3?

When designing expression systems for recombinant Aadacl3 production, researchers should consider multiple options based on their specific experimental requirements:

For structural studies requiring larger quantities of protein, E. coli expression with optimization of solubility tags (such as the His-tag mentioned in search result ) might be more suitable. For functional studies where post-translational modifications might be critical, mammalian or insect cell expression systems may be preferable.

What methodologies are recommended for investigating post-translational modifications of Aadacl3?

Based on information about human AADACL3 phosphorylation sites , researchers investigating post-translational modifications (PTMs) of mouse Aadacl3 should consider these methodologies:

• Mass Spectrometry-Based Approaches:

  • Enrichment strategies for specific PTMs (e.g., TiO2 for phosphopeptides)

  • High-resolution LC-MS/MS for identification of modification sites

  • Quantitative proteomics using SILAC or TMT labeling to compare PTM levels under different conditions

• Western Blotting and Immunodetection:

  • Using PTM-specific antibodies (e.g., anti-phospho, anti-acetyl)

  • Mobility shift assays to detect modifications that significantly alter protein migration

• PTM-Specific Staining Techniques:

  • Pro-Q Diamond for phosphorylation detection

  • Periodic acid-Schiff (PAS) staining for glycosylation

• Bioinformatic Prediction and Analysis:

  • Tools like NetPhos, GPS, or PHOSIDA for phosphorylation site prediction

  • NetOGlyc and NetNGlyc for glycosylation site prediction

  • Comparative analysis with human AADACL3 known modification sites (T90, T108, S398)

These approaches can be combined to create a comprehensive PTM profile of mouse Aadacl3 under various physiological and experimental conditions.

What are the most effective methods for measuring Aadacl3 enzymatic activity in vitro?

For measuring the enzymatic activity of Aadacl3 in vitro, researchers should consider these methodological approaches:

• Fluorogenic Substrate Assays:

  • Using fluorescent substrates like 4-methylumbelliferyl esters that release detectable products upon hydrolysis

  • Monitoring fluorescence increase over time to determine reaction kinetics

  • Advantages include high sensitivity and real-time monitoring capabilities

• Colorimetric Assays:

  • Utilizing p-nitrophenyl ester hydrolysis, which produces a colored product measurable spectrophotometrically

  • Implementing coupled enzyme assays where Aadacl3 activity initiates a cascade leading to chromogenic product formation

  • Benefits include simplicity and accessibility with standard laboratory equipment

• Radiometric Assays:

  • Employing radiolabeled substrates and measuring labeled product release

  • Particularly useful for detecting low enzymatic activity

  • Offers exceptional sensitivity but requires specialized facilities

• Mass Spectrometry-Based Analysis:

  • Direct detection and quantification of substrate depletion and product formation

  • Allows identification of specific bonds being cleaved in complex natural substrates

  • Provides detailed structural information about reaction products

For standardized assessment, activity should be expressed as units per milligram (U/mg), where one unit represents the amount of enzyme catalyzing the conversion of 1 μmol of substrate per minute under defined temperature and pH conditions.

How can researchers identify potential physiological substrates for Aadacl3?

To identify potential physiological substrates for Aadacl3, researchers should implement a multi-faceted approach:

• In vitro Substrate Screening:

  • Systematic testing of candidate lipid substrates based on structural similarity to known lipolytic enzyme substrates

  • Development of substrate libraries containing diverse ester-containing compounds

  • High-throughput screening using activity-based detection methods

• Metabolomic Profiling:

  • Comparative metabolomics between wild-type and Aadacl3-deficient biological samples

  • Identification of accumulated metabolites in knockout models (potential substrates)

  • Detection of decreased metabolites (potential products)

• Activity-Based Protein Profiling (ABPP):

  • Using activity-based probes that specifically label active Aadacl3

  • Competition assays with potential substrates to identify those that bind to the active site

  • Structural analysis of enzyme-substrate complexes

• Computational Approaches:

  • Molecular docking of potential substrates in homology models of Aadacl3

  • Virtual screening of metabolite databases

  • Machine learning predictions based on substrates of related enzymes

This integrated approach can help identify the most likely physiological substrates for further validation through detailed enzymological studies.

How should researchers design Aadacl3 knockout experiments to assess physiological function?

When designing Aadacl3 knockout experiments to assess physiological function, researchers should consider this comprehensive experimental framework:

• Knockout Strategy Design:

  • Whole-body versus tissue-specific knockout approaches

  • Constitutive versus inducible knockout systems to distinguish developmental from functional effects

  • CRISPR/Cas9-mediated deletion with careful consideration of potential off-target effects

  • Verification of knockout efficiency at genomic, transcriptomic, and proteomic levels

• Phenotypic Characterization Pipeline:

  • Detailed baseline phenotyping (growth, behavior, tissue morphology)

  • Metabolic parameter analysis (energy expenditure, glucose tolerance, insulin sensitivity)

  • Tissue-specific analyses focusing on lipid metabolism in relevant organs

  • Comprehensive lipidomic profiling to identify altered lipid species

• Challenge Testing Protocols:

  • Response to metabolic stressors (high-fat diet, fasting)

  • Age-related phenotypic changes

  • Response to inflammatory challenges

  • Pharmacological challenges with drugs metabolized by related enzymes

• Molecular Mechanism Investigation:

  • Multi-omics analysis (transcriptomics, proteomics, metabolomics)

  • Pathway analysis to identify compensatory mechanisms

  • Cell-type specific responses in heterogeneous tissues

  • Integration with human genetic data when available

This systematic approach can help establish the physiological role of Aadacl3 while minimizing confounding factors and misinterpretation of results.

What approaches can distinguish between direct and indirect effects in Aadacl3 functional studies?

To distinguish between direct and indirect effects in Aadacl3 functional studies, researchers should implement these methodological approaches:

• Controlled Experimental Systems:

  • In vitro assays with purified recombinant Aadacl3 protein to establish direct enzymatic activity

  • Reconstituted systems with defined components to minimize variables

  • Rapidly inducible expression or inhibition systems to capture immediate effects

• Causal Analysis Methods:

  • Time-course experiments with high temporal resolution to establish sequence of events

  • Dose-response relationships to demonstrate proportionality of effects

  • Rescue experiments in knockout models using wild-type versus catalytically inactive Aadacl3

• Specific Molecular Tools Development:

  • Site-directed mutagenesis of catalytic residues to create activity-deficient controls

  • Development of selective inhibitors or substrate analogs

  • Domain-swapping experiments to isolate functional regions

• Systems Biology Approaches:

  • Metabolic flux analysis using stable isotope labeling

  • Network perturbation analysis to map direct versus propagated effects

  • Computational modeling to predict system-wide consequences of Aadacl3 modulation

By systematically applying these approaches, researchers can more confidently attribute observed phenotypes to direct Aadacl3 activity rather than secondary consequences or compensatory mechanisms.

How do structural variations in Aadacl3 correlate with substrate specificity and enzyme function?

Understanding the relationship between Aadacl3 structural variations and its substrate specificity requires a systematic structure-function analysis approach:

• Structural Analysis Methods:

  • Homology modeling based on related lipolytic enzymes

  • Structural comparison with human AADACL3 and mouse AADAC

  • Identification of putative catalytic triad and substrate binding residues

  • Analysis of potential regulatory domains that might influence activity

• Mutagenesis Strategy:

  • Systematic site-directed mutagenesis of:

    • Predicted catalytic residues (likely in the GDXG motif)

    • Substrate binding pocket residues

    • Interface regions that might be involved in oligomerization

    • Potential regulatory sites identified from PTM analysis

• Functional Characterization Pipeline:

  • Activity assays with mutant variants against diverse substrate panels

  • Determination of kinetic parameters (Km, kcat, kcat/Km) for different substrates

  • Thermal stability analysis to distinguish catalytic from structural effects

  • Protein-protein interaction analysis for mutant variants

This comprehensive approach can establish clear correlations between specific structural features and the functional properties of Aadacl3.

What statistical approaches are recommended for analyzing Aadacl3 enzymatic kinetics data?

For rigorous analysis of Aadacl3 enzymatic kinetics data, researchers should implement these statistical methods:

• Enzyme Kinetics Model Fitting:

  • Non-linear regression for fitting Michaelis-Menten, allosteric, or other kinetic models

  • Global fitting approaches for analyzing multiple datasets simultaneously

  • Model selection using Akaike Information Criterion (AIC) or Bayesian Information Criterion (BIC)

• Robust Parameter Estimation Techniques:

  • Bootstrap resampling for confidence interval determination

  • Jackknife methods for identifying influential data points

  • Bayesian parameter estimation for incorporating prior knowledge

  • Monte Carlo methods for propagating measurement uncertainty

• Comparative Statistical Frameworks:

  • ANOVA with appropriate post-hoc tests for comparing activity across multiple conditions

  • Mixed-effects models for handling repeated measurements and batch effects

  • Multivariate approaches for analyzing multiple kinetic parameters simultaneously

How can researchers reconcile discrepancies in Aadacl3 activity measurements across different experimental platforms?

When faced with discrepancies in Aadacl3 activity measurements across different experimental platforms, researchers should implement this systematic reconciliation approach:

• Standardization of Experimental Parameters:

  • Uniform protein quantification methods (e.g., BCA, Bradford, amino acid analysis)

  • Consistent buffer compositions, pH, temperature, and ionic strength

  • Standardized substrate preparations and verified purity

  • Common reference standards across laboratories

• Technical Variable Assessment:

  • Evaluation of the impact of different protein tags (e.g., His-tag as used in recombinant protein )

  • Comparison of protein produced in different expression systems

  • Analysis of storage conditions and freeze-thaw effects on enzyme stability

  • Verification of enzyme homogeneity through analytical techniques

• Statistical Integration Methods:

  • Meta-analysis techniques to combine data from multiple sources

  • Normalization procedures to account for systematic differences between platforms

  • Bayesian hierarchical modeling to incorporate between-study variability

  • Sensitivity analysis to identify sources of heterogeneity

• Validation Through Orthogonal Methods:

  • Confirmation of key findings using fundamentally different detection principles

  • Correlation of in vitro activity measurements with cellular or in vivo effects

  • Independent replication in different laboratories

This systematic approach enables researchers to distinguish between genuine biological variations and technical artifacts, ultimately leading to more reliable and reproducible Aadacl3 activity assessments.