SPCC777.06c Antibody

What is SPCC777.06c protein and why is it significant for research?

SPCC777.06c (UniProt: O74545) encodes a hydrolase enzyme conserved across fungi, bacteria, plants, and protists. Its significance lies in its fundamental role in cellular metabolism and enzymatic processes. The protein demonstrates dual subcellular localization in both the cytoplasm and nucleus, suggesting it functions in both metabolic regulation and potentially transcriptional control.

Primary research applications include:

• Investigation of fungal metabolism pathways

• Studies of hydrolase-mediated enzymatic processes

• Comparative biology across species with homologous proteins

• Gene regulation studies focused on nuclear activities

What are the key specifications of commercially available SPCC777.06c antibodies?

The standard SPCC777.06c antibody specifications include:

This antibody is specifically developed for research applications involving Schizosaccharomyces pombe .

How should I optimize Western blotting protocols for SPCC777.06c detection?

For optimal Western blot detection of SPCC777.06c:

• Sample preparation:

  • Extract proteins using a buffer containing protease inhibitors

  • Load 20-40 μg of total protein per lane

  • Include positive control (recombinant SPCC777.06c protein) when possible

• Electrophoresis and transfer:

  • Use 10-12% SDS-PAGE gels for optimal separation around 30 kDa

  • Transfer to PVDF membranes (preferred over nitrocellulose for hydrolases)

  • Verify transfer efficiency with reversible protein staining

• Antibody incubation:

  • Block with 5% non-fat milk or BSA in TBST for 1 hour at room temperature

  • Dilute primary antibody 1:500-1:2000 (optimize for your specific lot)

  • Incubate with primary antibody overnight at 4°C

  • Use HRP-conjugated anti-rabbit secondary antibody at 1:5000-1:10000

  • Include appropriate washing steps (3-5× with TBST for 5-10 minutes each)

• Detection:

  • Use enhanced chemiluminescence (ECL) substrate

  • For low abundance samples, consider using signal enhancers or longer exposure times

These recommendations should be optimized based on your specific experimental conditions and antibody lot .

What validation steps are essential before using SPCC777.06c antibody in new experimental conditions?

Before employing SPCC777.06c antibody in new experimental conditions, consider these critical validation steps:

• Specificity confirmation:

  • Perform Western blot with recombinant SPCC777.06c protein

  • Include wild-type vs. SPCC777.06c knockout/knockdown samples if available

  • Check for cross-reactivity with closely related proteins

• Optimization for specific applications:

  • Titrate antibody concentrations (typically starting with manufacturer's recommendation)

  • Test multiple blocking agents (BSA vs. milk vs. commercial blockers)

  • Optimize incubation conditions (time, temperature, buffer composition)

• Positive and negative controls:

  • Include tissues/cells known to express or not express SPCC777.06c

  • Consider tagged recombinant SPCC777.06c as positive control

  • Use pre-immune serum as negative control for polyclonal antibodies

• Cross-validation:

  • Confirm findings with orthogonal methods (qPCR, mass spectrometry)

  • If possible, use a second antibody targeting a different epitope

• Documentation:

  • Record antibody lot number, dilutions, and experimental conditions

  • Maintain consistent protocols for reproducibility

How can SPCC777.06c antibody be used to investigate protein-protein interactions in fission yeast?

SPCC777.06c antibody can be effectively employed to study protein-protein interactions through several methodological approaches:

• Co-immunoprecipitation (Co-IP):

  • Lyse cells under non-denaturing conditions to preserve protein complexes

  • Incubate lysate with SPCC777.06c antibody coupled to Protein A/G beads

  • Elute bound complexes and analyze interacting partners via mass spectrometry

  • Confirm interactions by reciprocal Co-IP with antibodies against putative partners

• Proximity ligation assay (PLA):

  • Fix and permeabilize cells on slides

  • Incubate with SPCC777.06c antibody and antibody against suspected interacting protein

  • Apply species-specific PLA probes followed by ligation and amplification

  • Analyze fluorescent signals indicating protein proximity (<40 nm)

• Chromatin immunoprecipitation (ChIP) for nuclear interactions:

  • Given SPCC777.06c's nuclear localization, ChIP can identify DNA-binding partners

  • Cross-link protein-DNA complexes in vivo

  • Immunoprecipitate with SPCC777.06c antibody

  • Analyze associated proteins by Western blot or mass spectrometry

• FRET-based approaches:

  • Express SPCC777.06c with fluorescent tag (e.g., CFP)

  • Express putative interacting protein with complementary tag (e.g., YFP)

  • Use antibody-based detection for FRET analysis of protein proximity

Consider implementing the inferential approaches described in the paper by Fernandez-de-Cossio-Diaz and colleagues to design experiments that can effectively map interaction networks .

What strategies can help distinguish between different functional states of SPCC777.06c protein in experimental settings?

Distinguishing between functional states of SPCC777.06c requires sophisticated experimental design:

• Phosphorylation-specific detection:

  • Develop or acquire phospho-specific antibodies targeting known regulatory sites

  • Use phosphatase treatments as controls to confirm specificity

  • Employ Phos-tag™ gels to separate phosphorylated from non-phosphorylated forms

  • Compare patterns under different cellular conditions (stress, cell cycle phases)

• Activity-based profiling:

  • Since SPCC777.06c is a predicted hydrolase, use activity-based probes to label active enzyme

  • Compare active fraction to total protein detected by standard antibody

  • Design experiments with known hydrolase inhibitors to validate specificity

• Subcellular localization analysis:

  • Perform fractionation studies to separate nuclear and cytoplasmic pools

  • Use immunofluorescence with SPCC777.06c antibody to track localization changes

  • Correlate localization with functional readouts under various conditions

• Structural conformation detection:

  • Consider epitope accessibility assays to determine conformational changes

  • Use limited proteolysis followed by Western blotting to identify structural transitions

  • Compare detection patterns in native vs. denaturing conditions

• Complex formation analysis:

  • Use native PAGE followed by Western blotting to identify different complex states

  • Apply size exclusion chromatography prior to immunodetection

  • Correlate complex formation with functional activity

What are the most common issues when using SPCC777.06c antibody and how can they be resolved?

Methodological approaches to resolve these issues should be systematic, changing only one variable at a time and maintaining detailed records of optimization efforts .

How does antibody validation differ between techniques when working with SPCC777.06c?

Validation requirements differ significantly based on the intended application:

• Western Blotting validation:

  • Focuses on molecular weight specificity (band at ~30 kDa)

  • Requires demonstration of single predominant band

  • Often uses recombinant protein as positive control

  • Benefits from knockout/knockdown controls when available

  • May include peptide competition assays

• Immunoprecipitation (IP) validation:

  • Evaluates ability to concentrate target protein from complex mixtures

  • Requires demonstration of enrichment compared to input

  • Should include non-specific IgG control

  • May require optimization of lysis conditions to maintain protein interactions

  • Often coupled with mass spectrometry validation

• Immunofluorescence/Immunohistochemistry validation:

  • Focuses on subcellular localization pattern consistency

  • Requires appropriate fixation optimization

  • Benefits from blocking peptide controls

  • Should match known localization pattern (cytoplasmic and nuclear for SPCC777.06c)

  • May include comparison with tagged protein expression

• ELISA validation:

  • Establishes dynamic range and detection limits

  • Requires demonstration of concentration-dependent signal

  • Benefits from spike-and-recovery experiments

  • Should establish specificity against related proteins

Based on current research practice, each application requires dedicated validation rather than assuming cross-application reliability .

How can SPCC777.06c antibody contribute to understanding evolutionary conservation of hydrolases across species?

SPCC777.06c antibody provides a valuable tool for comparative evolutionary studies of hydrolases through these methodological approaches:

• Cross-reactivity analysis:

  • Test antibody against lysates from evolutionarily related species

  • Identify conserved epitopes through Western blotting

  • Quantify relative binding affinities to homologs from different organisms

  • Map conservation patterns to functional domains

• Structural conservation studies:

  • Use the antibody to immunoprecipitate SPCC777.06c and its homologs

  • Perform mass spectrometry analysis to identify conserved post-translational modifications

  • Compare functional activity of immunoprecipitated proteins across species

  • Map epitope recognition to conserved structural elements

• Functional complementation experiments:

  • Express SPCC777.06c homologs from diverse species in S. pombe

  • Use the antibody to confirm expression levels

  • Correlate detection strength with functional complementation

  • Identify critical conserved regions through mutation analysis

• Phylogenetic analysis:

  • Combine antibody-based detection with sequence analysis

  • Correlate epitope conservation with phylogenetic distance

  • Use antibody cross-reactivity to validate in silico predictions

  • Apply machine learning approaches to predict cross-reactivity based on sequence

This approach mirrors methods used in antibody specificity studies such as those described for nanobody development, though applied to evolutionary questions .

What methodological approaches can overcome the challenge of detecting low-abundance SPCC777.06c in different cell cycle phases?

Detecting low-abundance SPCC777.06c across cell cycle phases requires specialized methodological approaches:

• Cell synchronization and enrichment:

  • Synchronize S. pombe cultures using established methods (nitrogen starvation, hydroxyurea block, etc.)

  • Collect cells at specific cell cycle phases (validated by flow cytometry)

  • Enrich for SPCC777.06c through subcellular fractionation based on its known localization

  • Use immunoprecipitation to concentrate protein before detection

• Signal amplification strategies:

  • Implement tyramide signal amplification (TSA) for immunofluorescence

  • Use ultra-sensitive ECL substrates for Western blotting

  • Consider quantum dot-conjugated secondary antibodies for enhanced sensitivity

  • Apply proximity ligation assay (PLA) for in situ detection

• Quantitative imaging approaches:

  • Employ high-sensitivity confocal microscopy with photon counting

  • Use deconvolution algorithms to enhance signal-to-noise ratio

  • Apply automated image analysis for objective quantification

  • Implement super-resolution techniques for detailed localization

• Complementary approaches:

  • Correlate antibody detection with RNA expression (smFISH)

  • Consider expressing tagged versions under native promoter for validation

  • Use mass spectrometry with targeted methods (PRM/MRM) for validation

  • Apply computational modeling to predict expression patterns

These approaches can be integrated within the framework used for detecting other low-abundance proteins in yeast cell cycle studies .

How can SPCC777.06c antibody be used to facilitate structural studies of the protein?

The SPCC777.06c antibody can be strategically employed in structural biology through several methodological approaches:

• Crystallography assistance:

  • Use antibody fragments (Fab) to stabilize flexible regions of SPCC777.06c

  • Co-crystallize antibody-protein complexes to enhance crystal packing

  • Employ antibody to pull down native protein for structural studies

  • Validate predicted structural models through epitope mapping

• Cryo-EM applications:

  • Use antibody binding to increase effective molecular weight for better particle detection

  • Apply antibody labeling to identify specific domains in low-resolution maps

  • Stabilize preferred conformations through antibody binding

  • Validate structural models through antibody-based domain mapping

• Hydrogen-deuterium exchange mass spectrometry (HDX-MS):

  • Use antibody to study conformational changes through differential protection

  • Compare HDX patterns in free vs. antibody-bound states

  • Map epitopes through protection from deuterium exchange

  • Identify allosteric effects induced by antibody binding

• Small-angle X-ray scattering (SAXS):

  • Label specific domains with antibody fragments for domain identification

  • Use antibody binding to validate solution structure models

  • Compare scattering profiles with and without antibody to identify flexible regions

  • Analyze conformational ensembles through modeling of antibody-bound states

These approaches utilize antibodies as structural tools while avoiding potential interference with native function .

What considerations are important when using SPCC777.06c antibody for mapping protein domains and functional motifs?

When using SPCC777.06c antibody for domain mapping, researchers should consider:

• Epitope characterization:

  • Determine the antibody's precise epitope through peptide arrays or hydrogen-deuterium exchange

  • Assess whether the epitope is linear or conformational

  • Map the epitope to known domains or predicted functional regions

  • Consider generating multiple antibodies targeting different regions

• Functional interference assessment:

  • Test whether antibody binding affects enzymatic activity

  • Evaluate if antibody blocks protein-protein interactions

  • Determine if subcellular localization is altered by antibody binding

  • Consider using antibody fragments (Fab, scFv) to minimize steric hindrance

• Structural accessibility analysis:

  • Compare antibody binding under native vs. denaturing conditions

  • Use limited proteolysis with antibody detection to identify protected regions

  • Apply in situ proximity labeling to identify exposed domains

  • Correlate epitope accessibility with functional states

• Mutational analysis integration:

  • Generate point mutations in predicted epitope regions

  • Correlate loss of antibody binding with specific amino acid changes

  • Map functional consequences of mutations to antibody binding sites

  • Use antibody as a tool to detect conformational changes in mutants

• Domain interaction studies:

  • Use antibody to block specific domains and assess functional consequences

  • Apply FRET-based approaches with domain-specific antibodies

  • Combine with cross-linking mass spectrometry for comprehensive mapping

  • Validate domain models through competitive binding studies

These approaches build on methodologies used in epitope mapping studies while focusing specifically on domain functionality .

How does the specificity and utility of SPCC777.06c antibody compare with other methods for studying this protein?

Each method offers complementary insights:

• Antibody advantages:

  • Detects endogenous protein without genetic manipulation

  • Can distinguish post-translational modifications with specific antibodies

  • Applicable across multiple techniques (WB, IP, IF, etc.)

  • Allows protein quantification in complex samples

• Antibody limitations:

  • Epitope accessibility may vary with protein conformation

  • Cannot track dynamic changes in live cells

  • May have cross-reactivity with closely related proteins

  • Cannot directly assess enzymatic activity

• Ideal complementary approaches:

  • Combine antibody detection with activity-based probes for functional analysis

  • Validate antibody findings with genetically tagged versions

  • Use mass spectrometry to confirm antibody-based discoveries

  • Integrate with structural studies for comprehensive understanding

This comparative framework helps researchers select appropriate tools based on specific research questions .

How can emerging technologies like nanobodies enhance SPCC777.06c research beyond traditional antibodies?

Emerging nanobody technologies offer significant advantages for SPCC777.06c research:

• Structural advantages:

  • Smaller size (~15 kDa vs. ~150 kDa) allows access to sterically hindered epitopes

  • Enhanced stability enables more stringent experimental conditions

  • Single-domain nature facilitates recombinant production and modification

  • Greater solubility improves performance in various buffer conditions

• Methodological applications:

  • Super-resolution microscopy with minimal linkage error

  • Intracellular expression for live-cell imaging ("intrabodies")

  • Crystallization chaperones for structural studies

  • Affinity reagents for microfluidic and biosensor applications

• Implementation strategies:

  • Generate SPCC777.06c-specific nanobodies through camelid immunization

  • Apply phage display with synthetic libraries for selection

  • Create multivalent constructs targeting different epitopes

  • Develop intracellular expression systems for live S. pombe studies

• Potential research advancements:

  • Real-time tracking of SPCC777.06c localization during cell cycle

  • Inhibition of specific functional domains in living cells

  • Enhanced co-crystallization for structural determination

  • Development of biosensors to track hydrolase activity

The llama nanobody approach described for HIV research demonstrates the potential of this technology when applied to challenging research targets .

What methodological strategies can integrate SPCC777.06c antibody data with broader -omics approaches?

Integrating SPCC777.06c antibody data with systems biology requires sophisticated methodological frameworks:

• Multi-level data integration:

  • Combine antibody-based protein quantification with transcriptomics

  • Correlate post-translational modifications with phosphoproteomics

  • Integrate localization data with interactome studies

  • Link functional readouts with metabolomics data

• Network analysis approaches:

  • Use antibody-based co-IP data as input for protein interaction networks

  • Apply machine learning to identify patterns across multiple datasets

  • Implement Bayesian networks to infer causal relationships

  • Develop mathematical models incorporating antibody-derived parameters

• Temporal dynamics integration:

  • Synchronize cells and collect time-series data with antibody detection

  • Correlate protein abundance changes with transcriptional dynamics

  • Track modifications throughout cellular processes

  • Implement computational models to predict dynamic behaviors

• Spatial organization analysis:

  • Use immunofluorescence data as input for spatial interaction models

  • Implement image-based systems biology approaches

  • Correlate subcellular localization with local interactome data

  • Develop spatial computational models based on microscopy findings

• Practical implementation:

  • Standardize antibody-based quantification for systems-level analysis

  • Implement internal controls for cross-experiment normalization

  • Develop data processing pipelines specific for antibody-derived data

  • Apply appropriate statistical methods for integrative analysis

This approach builds on frameworks used in integrative systems biology while focusing on antibody-derived data .

How can computational approaches enhance interpretation of SPCC777.06c antibody experimental data?

Computational approaches significantly enhance SPCC777.06c antibody data interpretation:

• Image analysis automation:

  • Implement machine learning for automated identification of subcellular localization

  • Apply computer vision algorithms to quantify colocalization patterns

  • Develop deep learning approaches for pattern recognition in complex tissues

  • Create custom analysis pipelines for high-content screening applications

• Structural modeling integration:

  • Use antibody epitope mapping data to validate protein structure predictions

  • Apply molecular dynamics simulations to predict epitope accessibility

  • Implement docking studies to model antibody-antigen interactions

  • Create integrated structural models incorporating antibody binding data

• Network-based interpretation:

  • Place antibody-derived interaction data in the context of known networks

  • Apply graph theory algorithms to identify key nodes and modules

  • Implement Bayesian approaches to infer causal relationships

  • Develop predictive models for functional outcomes based on network perturbations

• Temporal data analysis:

  • Apply hidden Markov models to antibody-derived time-series data

  • Implement signal processing techniques for pattern detection

  • Develop mathematical models incorporating antibody-quantified parameters

  • Use clustering algorithms to identify temporal response patterns

• Multi-omics data integration:

  • Implement dimensionality reduction techniques for visualization

  • Apply correlation analyses across different data types

  • Develop custom statistical frameworks for integrated hypothesis testing

  • Create predictive models combining antibody data with other molecular profiles

These approaches mirror computational methods used in antibody specificity prediction and multi-omics integration as seen in the referenced research .

What emerging technologies might enhance the specificity and application range of SPCC777.06c antibodies?

Several emerging technologies show promise for advancing SPCC777.06c antibody research:

• Single-cell antibody-based proteomics:

  • Adaptation of CyTOF/mass cytometry for yeast cellular heterogeneity studies

  • Development of microfluidic antibody-based single-cell protein quantification

  • Integration of spatial transcriptomics with antibody detection

  • Implementation of imaging mass cytometry for subcellular resolution

• Engineered antibody variants:

  • Development of bispecific antibodies targeting SPCC777.06c and interacting proteins

  • Creation of switchable antibodies responsive to experimental conditions

  • Engineering of antibody-enzyme fusions for proximity labeling

  • Production of conformation-specific antibodies for functional states

• In situ structural analysis:

  • Application of proximity labeling with SPCC777.06c antibodies for interaction mapping

  • Development of FRET-based conformational sensors using antibody fragments

  • Implementation of correlative light-electron microscopy with antibody detection

  • Adaption of in-cell NMR approaches with antibody labeling

• Antibody-based cellular engineering:

  • Creation of intracellular antibody-based degradation systems

  • Development of optogenetic antibody tools for temporal control

  • Implementation of antibody-based synthetic biology circuits

  • Engineering of antibody-mediated protein localization control systems

These approaches build upon advanced antibody engineering techniques similar to those employed in therapeutic antibody development .

What are the most promising research questions that could be addressed using SPCC777.06c antibody in combination with genetic manipulation techniques?

Integrating SPCC777.06c antibody with genetic manipulation opens several promising research avenues:

• Structure-function relationship studies:

  • Generate domain deletion mutants and use antibody to track expression/localization

  • Implement CRISPR-mediated point mutations to map functional residues

  • Create chimeric proteins with related hydrolases and track with domain-specific antibodies

  • Develop split-protein complementation systems with antibody validation

• Regulatory network mapping:

  • Perform systematic gene deletion/overexpression and monitor SPCC777.06c with antibody

  • Implement synthetic genetic arrays with antibody-based readouts

  • Create reporter strains with antibody-validated expression systems

  • Develop CRISPR activation/inhibition screens with antibody-based phenotyping

• Environmental response characterization:

  • Subject genetically modified strains to environmental stressors and track SPCC777.06c

  • Create biosensors using antibody-validated reporter systems

  • Implement optogenetic control with antibody-based monitoring

  • Develop microfluidic systems for dynamic perturbation with real-time antibody readouts

• Evolutionary conservation studies:

  • Replace endogenous gene with homologs from related species and track with antibody

  • Implement ancestral sequence reconstruction and expression with antibody validation

  • Create chimeric proteins from evolutionary divergent domains with domain-specific detection

  • Develop comprehensive mutation libraries with antibody-based functional screening

These approaches combine the specificity of antibody detection with the precision of genetic manipulation, enabling detailed mechanistic studies of SPCC777.06c function .