GDA Human, Active
Description
Enzymatic Activity Profile
Catalytic Function
Converts guanine to xanthine and ammonia via hydrolytic deamination (EC 3.5.4.3) .
Quantitative Metrics
| Parameter | Value | Source |
|---|---|---|
| Specific Activity | >2,000 pmol/min/μg at pH 8.0, 37°C | |
| Optimal pH | 8.0 | |
| Zinc Dependency | Confirmed (zinc-based hydrolase) |
Assay Methods
Research Applications
Key Biological Roles
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Neuronal Development: Regulates microtubule assembly and dendritic spine morphology
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Disease Associations: Differentially expressed in schizophrenia thalamic tissues
Experimental Uses
Recent Research Findings
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Fluorescent Assay Development: A 2021 study demonstrated real-time tracking of GDA activity using modified guanine analogs, enabling inhibitor discovery .
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Inhibitor Identification: Two novel inhibitors were identified alongside a previously reported compound, expanding therapeutic targeting options .
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Structural Insights: The His-tagged recombinant form retains native conformation, confirmed through analytical SEC and functional assays .
Product Specs
Q&A
What experimental methods are used to detect GDA enzymatic activity in human tissues?
GDA activity is typically quantified using chromatographic separation (HPLC or UPLC) or UV-spectrophotometric assays. Chromatographic methods resolve guanine (λ_max = 275 nm) from xanthine (λ_max = 250 nm) based on retention times and absorption profiles, enabling precise substrate-to-product ratio calculations . For real-time monitoring, fluorogenic substrates such as 8-azaguanine derivatives are emerging, though their specificity for human GDA requires validation against isoforms like adenosine deaminase (ADA) .
Table 1: Comparison of GDA Detection Methods
| Method | Sensitivity (nM) | Throughput | Specificity Challenges |
|---|---|---|---|
| HPLC | 10–50 | Low | Co-elution with purine analogs |
| UV Spectrophotometry | 100–500 | Medium | Interference from hemoproteins |
| Fluorogenic Assays | 1–10 | High | Cross-reactivity with ADA |
What cell lines are appropriate for studying human GDA pharmacology?
The NCI-60 panel remains the gold standard for GDA-related drug screening, as it includes transcriptional and mutational profiles for 73 cancer cell lines . For neurobiological studies, SH-SY5Y (neuroblastoma) or iPSC-derived neurons are preferred due to endogenous GDA expression. Critical parameters include:
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Baseline guanine/xanthine ratios (validate via LC-MS)
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CRISPR-Cas9 knockout controls to confirm on-target effects
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Cross-referencing with the GDA web tool (http://gda.unimore.it/) for drug-genomic correlations
How can active learning algorithms optimize GDA inhibitor screening campaigns?
Active learning (AL) frameworks reduce experimental burden by iteratively selecting the most informative compounds for testing. A retrospective analysis of the NCI-60 dataset achieved 89% prediction accuracy for GDA-inhibitor interactions using a hybrid AL strategy:
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Initialization: Train a matrix factorization model (ALS) on 10% of the drug-response matrix.
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Querying: Prioritize compounds with high expected loss minimization (ELM) scores, which balance uncertainty and representativeness .
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Validation: Update deep neural networks (DNNs) with newly labeled data to refine IC50 predictions.
Equation 1: ELM Score for Compound :
where is the prediction variance and governs exploration-exploitation trade-offs .
How should researchers resolve contradictions between genomic and biochemical data in GDA studies?
A 2018 integrative analysis of 50,816 compounds revealed three common discordance scenarios:
Case Study: Discrepancy in 5-azacytidine response
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Genomic Data: CCLE profiles suggested TP53-mutant lines should resist 5-azacytidine.
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Experimental Data: 40% of TP53-mutant lines showed sensitivity (GI50 < 1 µM).
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Resolution: The GDA platform identified overexpression of DNMT3B (a methylation regulator) as a compensatory resistance mechanism, detectable only through paired RNA-seq and methylation arrays .
Table 2: Data Reconciliation Workflow
| Step | Action | Tool/Technique |
|---|---|---|
| 1 | Confirm assay reproducibility | Bland-Altman analysis |
| 2 | Stratify samples by co-variates | PCA + hierarchical clustering |
| 3 | Identify confounding genomic alterations | GDA’s “Drug-to-Gene” module |
How to model GDA’s conformational dynamics during catalysis?
Molecular dynamics (MD) simulations at µs-scale resolution reveal that human GDA adopts a hinged-loop conformation upon substrate binding. Key parameters for accurate modeling:
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Force Field: CHARMM36m with TIP3P explicit solvent.
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Enhanced Sampling: Apply metadynamics to accelerate transition state sampling.
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Validation: Compare simulated B-factors with X-ray crystallography (PDB: 6V7X).
Equation 2: Free Energy Landscape:
where is the reaction coordinate (e.g., Cα RMSD of the active site), and is the probability density from MD trajectories .
What criteria define “active” vs. “inactive” GDA in functional studies?
Operational definitions vary by experimental context:
Product Science Overview
Recombinant human guanine deaminase is typically produced in Escherichia coli (E. coli) expression systems. The recombinant protein often includes a His-tag at the N-terminus to facilitate purification through affinity chromatography . The enzyme consists of 454 amino acids and has a molecular weight of approximately 53 kDa . The His-tagged version of the protein allows for easy identification and isolation during experimental procedures.
Guanine deaminase is responsible for the deamination of guanine to xanthine, a reaction that is part of the purine degradation pathway. This pathway is crucial for maintaining the balance of purine nucleotides within the cell. The specific activity of recombinant human guanine deaminase is typically greater than 2,000 pmol/min/μg, which is defined as the amount of enzyme that converts guanine to xanthine per minute at pH 8.0 and 37°C .
The enzyme’s activity is not only important for nucleotide metabolism but also has implications in various physiological processes. Studies in rat orthologs suggest that guanine deaminase may play a role in microtubule assembly . This indicates potential involvement in cellular processes such as cell division and intracellular transport.
Recombinant human guanine deaminase is widely used in biochemical and physiological research. Its high purity and specific activity make it suitable for various applications, including enzyme kinetics studies, structural biology, and drug discovery. The enzyme’s role in purine metabolism also makes it a target for research into metabolic disorders and potential therapeutic interventions.
For optimal stability, recombinant human guanine deaminase should be stored at 4°C for short-term use and at -20°C for long-term storage. It is important to avoid repeated freeze-thaw cycles to maintain the enzyme’s activity .
In summary, guanine deaminase is a vital enzyme in purine metabolism with significant roles in cellular processes. The recombinant form, produced in E. coli, provides a valuable tool for scientific research, offering insights into enzyme function and potential therapeutic applications.
