DDB_G0268542 is a recombinant protein derived from Dictyostelium discoideum, a social amoeba widely used as a model organism in molecular biology, immunology, and developmental studies . This protein is classified as "putative and uncharacterized," indicating that its functional role, structural motifs, and biochemical interactions remain undefined in the scientific literature. Its recombinant form is commercially available for research purposes, primarily expressed in E. coli with a His-tag for purification .
The recombinant protein is synthesized in E. coli using standard protocols. Key steps include:
Cloning: Insertion of the DDB_G0268542 coding sequence into a plasmid under a promoter compatible with E. coli.
Induction: Protein expression triggered by IPTG or similar inducers.
Purification: Affinity chromatography (e.g., nickel or cobalt columns) to isolate the His-tagged protein .
Commercial vendors like Creative BioMart and CUSABIO TECHNOLOGY LLC provide this protein in lyophilized form, typically stored at -20°C to -80°C .
While DDB_G0268542 is marketed for research, no peer-reviewed studies explicitly investigate its function, interaction partners, or role in Dictyostelium biology. This contrasts with other Dictyostelium proteins, such as the bacteriolytic Bad family (BadA–BadE), which have defined roles in phagosome-mediated bacterial destruction .
Functional Annotation: No data on enzymatic activity, subcellular localization, or involvement in pathways like phagocytosis, autophagy, or DNA repair .
Structural Insights: No crystallography or cryo-EM studies to elucidate its tertiary structure.
Experimental Validation: No reported assays (e.g., Western blot, immunoprecipitation) confirming its expression or activity in native D. discoideum .
Proteomics Research: As a control or bait in pull-down assays to identify interacting partners.
Functional Genomics: Overexpression/knockdown studies to infer biological roles.
Comparative Analysis: Phylogenetic studies to identify orthologs in other organisms.
KEGG: ddi:DDB_G0268542
DDB_G0268542 is a putative uncharacterized protein from the social amoeba Dictyostelium discoideum. It is a relatively small protein consisting of 71 amino acids in its full-length form. The protein is available as a recombinant product with a His-tag for research purposes . While its complete three-dimensional structure has not been fully characterized, researchers typically approach such proteins using a combination of bioinformatic prediction tools and experimental validation.
For initial structural analysis, researchers should consider:
Primary sequence analysis using tools like BLAST and multiple sequence alignments
Secondary structure prediction using software such as PSIPRED
Domain organization prediction through InterPro or SMART
Post-translational modification site prediction using NetPhos or similar tools
The optimal expression of recombinant DDB_G0268542 involves careful consideration of expression systems and growth conditions. Based on established protocols for Dictyostelium proteins, the following expression parameters have been optimized:
For maximum protein yield, initiate expression at mid-log phase (OD600 0.6-0.8) and optimize IPTG concentration through small-scale expression trials. After expression, harvesting cells via centrifugation (4,000-6,000 × g, 15 minutes, 4°C) followed by resuspension in an appropriate lysis buffer yields the best results for downstream purification.
Verification of DDB_G0268542 identity and purity requires a multi-analytical approach:
SDS-PAGE analysis: Run purified protein on a 15-20% gel (appropriate for small proteins) alongside molecular weight markers. DDB_G0268542 should appear at approximately 8-9 kDa (71 amino acids plus His-tag).
Western blotting: Use anti-His antibodies to confirm the presence of the tagged protein. If available, recombinant antibodies specific to Dictyostelium proteins can provide additional verification .
Mass spectrometry analysis: For definitive identification, tryptic digestion followed by LC-MS/MS analysis should be performed. Sample preparation should follow established protocols:
Size exclusion chromatography: To assess oligomeric state and homogeneity of the purified protein.
As an uncharacterized protein, determining the function of DDB_G0268542 requires an integrated experimental strategy:
Genetic Approaches:
CRISPR-Cas9 mediated gene knockout: Generate DDB_G0268542-null mutants and assess phenotypic consequences
Overexpression studies: Create strains with elevated DDB_G0268542 levels to identify gain-of-function phenotypes
Complementation assays: Test if DDB_G0268542 can rescue phenotypes of known mutants in related pathways
Biochemical Approaches:
Protein interaction studies using pull-down assays with His-tagged DDB_G0268542 as bait
Phosphoproteomic analysis using SILAC labeling to determine if DDB_G0268542 is regulated by phosphorylation during key cellular processes
Activity assays based on predicted domains (if any)
Cell Biological Approaches:
Fluorescent tagging (GFP/RFP fusion) to determine subcellular localization
Live cell imaging during different developmental stages of Dictyostelium
Assessment of localization changes in response to stimuli such as DIF-1
The most informative strategy typically combines multiple approaches, starting with localization studies to provide initial functional insights, followed by interaction partner identification and phenotypic analysis of knockout strains.
SILAC (Stable Isotope Labeling with Amino acids in Cell culture) experiments for studying DDB_G0268542 phosphorylation require careful experimental design:
Labeling Strategy:
Culture Conditions:
Experimental Setup (Triplex Design):
Sample Processing:
Mass Spectrometry Analysis:
Employ titanium dioxide enrichment for phosphopeptides
Analyze by LC-MS/MS with high-resolution instruments
Quantify relative phosphorylation levels using intensity ratios of light/medium/heavy peptide forms
This experimental design allows quantitative assessment of phosphorylation dynamics on DDB_G0268542 across multiple time points following stimulation.
Determining protein-protein interactions for DDB_G0268542 requires multiple complementary approaches:
In vitro Approaches:
Affinity purification-mass spectrometry (AP-MS): Use His-tagged DDB_G0268542 as bait to pull down interacting partners
Yeast two-hybrid screening: Test for direct protein-protein interactions using a Dictyostelium cDNA library
Protein microarrays: Probe Dictyostelium protein arrays with labeled DDB_G0268542
In vivo Approaches:
Proximity labeling (BioID/TurboID): Fuse biotin ligase to DDB_G0268542 to label proximal proteins
Fluorescence resonance energy transfer (FRET): For testing specific interaction candidates
Co-immunoprecipitation with antibodies against DDB_G0268542 or candidate interactors
Data Analysis:
Filter interaction data against control datasets to eliminate common contaminants
Validate key interactions through reciprocal pull-downs
Perform functional enrichment analysis on identified interactors to infer biological processes
For a comprehensive interactome, analyzing interactions under different conditions (developmental stages, stress responses) is highly recommended, as protein interactions often depend on cellular context.
The choice of cell lysis method significantly impacts protein integrity and experimental outcomes. For DDB_G0268542, consider these optimized approaches:
For phosphorylation studies, add phosphatase inhibitors (sodium fluoride, sodium orthovanadate, β-glycerophosphate). When studying protein interactions, include reversible crosslinking agents like DSP (dithiobis(succinimidyl propionate)) before lysis to stabilize transient interactions.
The optimal buffer composition for preserving DDB_G0268542 integrity includes:
50 mM Tris-HCl (pH 7.5-8.0)
150 mM NaCl
1 mM EDTA
0.5% NP-40 or 1% Triton X-100
Protease inhibitor cocktail
1 mM PMSF (added fresh)
Optimizing immunoprecipitation (IP) for DDB_G0268542 requires careful consideration of several parameters:
Antibody Selection:
IP Conditions:
Binding Buffer: 25 mM Tris-HCl pH 7.5, 150 mM NaCl, 0.1% NP-40, 1 mM EDTA, 5% glycerol
Antibody Amount: 2-5 μg per mg of total protein
Incubation: 4°C overnight with gentle rotation
Beads: Protein A/G magnetic beads (50 μl slurry per reaction)
Washing Steps:
Perform 4-5 washes with decreasing detergent concentration
Include salt gradient washes to reduce non-specific binding
Final wash with detergent-free buffer
Elution Options:
For His-tagged protein: 250-300 mM imidazole
For antibody-based IP: Glycine buffer (pH 2.8) followed by immediate neutralization
For downstream mass spectrometry: On-bead digestion with trypsin
Controls:
Input sample (10% of lysate used for IP)
Non-specific IgG control
Untagged or knockout cell lysate as negative control
For studying phosphorylation status, maintain samples at 4°C throughout and include phosphatase inhibitors in all buffers. For interaction studies, consider crosslinking before lysis or using formaldehyde fixation to capture transient interactions.
Developing effective recombinant antibodies against DDB_G0268542 involves several critical considerations:
Antigen Design:
Antibody Generation Technologies:
Validation Strategies:
Western blotting against recombinant protein and native Dictyostelium lysates
Immunofluorescence in wild-type vs. DDB_G0268542 knockout cells
Immunoprecipitation followed by mass spectrometry confirmation
Recombinant Expression Formats:
scFv (single-chain variable fragment)
Fab (antigen-binding fragment)
Full-length IgG with appropriate species constant regions
The development of recombinant antibodies is particularly valuable for the Dictyostelium research community given the limited commercial availability of reagents due to the relatively small size of this research field . Recombinant antibodies offer advantages in reproducibility, consistency, and sustainable supply compared to traditional hybridoma-produced antibodies.
Analysis and visualization of phosphoproteomic data for DDB_G0268542 requires a structured approach:
Data Processing Pipeline:
Raw MS data processing using MaxQuant or similar software
Normalization of SILAC ratios (light/medium/heavy)
Statistical analysis to identify significant changes (p-value < 0.05)
Multiple testing correction (Benjamini-Hochberg)
Phosphosite Identification:
Localization probability scoring (>0.75 considered high confidence)
Manual validation of MS/MS spectra for ambiguous sites
Comparison with known phosphorylation motifs
Temporal Dynamics Visualization:
Integrated Analysis:
Comparison with known DIF-1 responsive phosphoproteins
Pathway enrichment analysis of co-regulated phosphoproteins
Kinase prediction analysis to identify potential upstream regulators
For effective visualization, consider using:
Bar graphs for comparing magnitudes of phosphorylation at different sites
Scatter plots to display reproducibility between biological replicates
Violin plots to show distribution of phosphorylation changes across conditions
When presenting phosphoproteomic data in publications, provide both the normalized ratios and statistical significance measures, and clearly indicate the specific phosphorylation sites using standard notation (e.g., Ser45, Thr67).
Statistical analysis of protein interaction networks involving DDB_G0268542 requires specialized approaches:
Filtering and Scoring Interactions:
Apply SAINTexpress or similar algorithms to assign confidence scores
Use empirical Bayes approaches to estimate false discovery rates
Implement COMPASS scoring for quantitative AP-MS data
Set threshold at FDR < 0.01 for high-confidence interactions
Network Construction:
Generate primary networks using high-confidence direct interactors
Expand to secondary networks including interactions between primary interactors
Calculate network metrics (degree, betweenness centrality) to identify hub proteins
Functional Enrichment Analysis:
Apply hypergeometric tests for GO term enrichment
Use permutation-based methods for pathway enrichment
Implement semantic similarity measures to group related functions
Comparative Network Analysis:
Compare networks across different conditions using differential network analysis
Apply GSEA (Gene Set Enrichment Analysis) to ranked interaction lists
Use network alignment algorithms to compare with interactomes of homologous proteins
Visualization recommendations:
Use force-directed layouts for network visualization
Implement edge weights based on interaction confidence
Use node coloring to represent functional categories
Provide subnetwork views focusing on specific biological processes
The integration of interaction data with other omics datasets (transcriptomics, phosphoproteomics) can provide additional context for understanding DDB_G0268542 function within the cellular system.
Establishing both statistical significance and biological relevance for DDB_G0268542 experimental results requires rigorous analytical approaches:
Statistical Significance Assessment:
Power analysis: Determine appropriate sample size before experiments (typically n ≥ 3 biological replicates)
Appropriate statistical tests:
Two-group comparisons: Student's t-test or Mann-Whitney U test
Multiple group comparisons: ANOVA with post-hoc tests (Tukey's HSD)
Time-course data: Repeated measures ANOVA or mixed-effects models
Multiple testing correction: Benjamini-Hochberg procedure for controlling false discovery rate
Effect Size Evaluation:
Calculate Cohen's d or fold changes to quantify magnitude of differences
Establish thresholds for biological significance (e.g., >1.5-fold change)
Compare effect sizes across different experimental conditions
Biological Validation Strategies:
Orthogonal technique confirmation: Verify key findings using independent methods
Genetic validation: Test phenotypic effects in knockout/knockdown models
Dose-response relationships: Establish concentration-dependent effects
Temporal dynamics: Evaluate consistency across different time points
Control Benchmarking:
Positive controls: Compare with well-characterized related proteins
Negative controls: Non-specific proteins of similar size/structure
System perturbation controls: Evaluate responses to known stimuli
When reporting results, present both p-values and effect sizes in tables with appropriate precision (typically 2-3 significant figures). Using visualization methods that simultaneously display statistical significance and effect magnitude, such as volcano plots, can effectively communicate the dual aspects of your findings.