YGR067C Antibody

What is YGR067C and why is it important in yeast research?

YGR067C is a gene in Saccharomyces cerevisiae (baker's yeast) identified in various genomic studies. It is cataloged in the Uniprot database with the accession number P53243 . The protein encoded by this gene has been studied in the context of cellular processes and potentially plays a role in yeast cell cycle regulation, as suggested by clustering analysis of expression profiles . Though not fully characterized compared to other well-studied yeast proteins, YGR067C provides researchers with opportunities to explore novel aspects of yeast biology, particularly in relation to transcriptional regulation and cell cycle pathways. Its study contributes to our broader understanding of eukaryotic cellular mechanisms, making antibodies against this protein valuable tools for fundamental research.

What types of YGR067C antibodies are currently available for research?

The predominant type of YGR067C antibody available for research is polyclonal antibody raised in rabbits, such as the one described in the product data (CSB-PA346443XA01SVG) . These antibodies are typically:

• Generated using recombinant YGR067C protein from Saccharomyces cerevisiae strain ATCC 204508/S288c as immunogen

• Purified via antigen affinity methods

• Supplied in liquid form

• Non-conjugated (requiring secondary detection methods)

• Stored in preservative buffer containing 50% Glycerol, 0.01M PBS (pH 7.4), and 0.03% Proclin 300

Unlike some more widely studied proteins, monoclonal alternatives for YGR067C may be limited, making polyclonal antibodies the standard choice for most research applications.

What are the validated applications for YGR067C antibody?

Based on the available information, YGR067C antibodies have been validated for the following applications:

Researchers should note that optimal dilutions must be determined empirically for each application and specific experimental setup . Novel applications beyond those listed should be validated thoroughly before implementation in critical experiments.

What storage and handling practices ensure optimal YGR067C antibody performance?

For maximum stability and performance of YGR067C antibody:

• Store upon receipt at -20°C or -80°C for long-term storage

• Avoid repeated freeze-thaw cycles that can degrade antibody quality

• When working with the antibody, keep on ice and minimize exposure to room temperature

• For short-term storage (1-2 weeks), 4°C is acceptable if the antibody contains proper preservatives

• Ensure sterile handling to prevent microbial contamination

• For diluted working solutions, prepare fresh when possible or add carrier proteins (e.g., BSA) for stability

Improper storage can lead to aggregation, degradation, or loss of specificity, compromising experimental outcomes.

What controls should be included when using YGR067C antibody in experiments?

Rigorous experimental design with appropriate controls is essential:

Additionally, comparing wild-type and YGR067C deletion strains can provide definitive evidence of antibody specificity in functional studies.

How can YGR067C antibody be optimized for chromatin immunoprecipitation (ChIP) studies?

While not explicitly validated for ChIP in the product documentation, YGR067C antibody can potentially be adapted for chromatin immunoprecipitation studies using methodologies similar to those employed with other yeast transcription factors:

• Crosslinking optimization: Test both formaldehyde concentrations (0.75-1.5%) and crosslinking times (10-20 minutes) to preserve protein-DNA interactions while maintaining antibody accessibility

• Sonication parameters: Optimize sonication conditions to generate DNA fragments between 200-500bp, which is ideal for downstream analysis

• Antibody amount determination: Titrate antibody quantities (2-10μg per reaction) to identify the optimal concentration that maximizes signal-to-noise ratio

• Bead selection: Compare protein A and protein G beads for maximum recovery of rabbit polyclonal YGR067C antibodies

• Validation approaches: Confirm enrichment using qPCR targeting regions with predicted YGR067C binding sites versus control regions

For analysis, consider comparing results with known DNA-binding protein datasets as referenced in the literature , where techniques for identifying target genes and binding motifs have been established. Success may be evaluated by the clear identification of "putative" target genes versus "diffuse" signals that indicate failure of target prediction .

How does YGR067C relate to cell cycle regulation based on clustering analysis?

Superparamagnetic clustering analysis of gene expression data has been used to study cell cycle genes in yeast, potentially including YGR067C. Key insights include:

• The modified Superparamagnetic Clustering algorithm (SPCTF) incorporates biological information about transcription factor regulation, which could help understand YGR067C's role

• This algorithm weighs gene relationships based on both expression profiles and shared transcription factors that bind to their promoters

• When analyzing YGR067C using this method, researchers should look for:

  • Co-clustering with known cell cycle genes

  • Shared transcription factor binding sites with cell cycle genes

  • Temporal expression patterns matching specific cell cycle phases

• If YGR067C clusters with unidentified genes, techniques like MUSA (motif finding using an unsupervised approach) can identify shared regulatory elements that may indicate functional relationships

• Genes co-clustering with YGR067C that contain cell cycle transcription factor binding sites would be prime candidates for further experimental validation

This approach has successfully identified previously unclassified cell cycle genes, suggesting its utility for characterizing YGR067C's functional role in cell cycle processes.

What are the methodological considerations for using YGR067C antibody in protein-protein interaction studies?

When investigating YGR067C protein interactions:

• Co-immunoprecipitation optimization:

  • Use gentle lysis buffers (containing 0.1-0.5% NP-40 or Triton X-100) to preserve native protein complexes

  • Include protease inhibitors and phosphatase inhibitors if phosphorylation states are relevant

  • Consider crosslinking approaches for transient or weak interactions

  • Pre-clear lysates thoroughly to reduce non-specific binding

• Bead selection and washing stringency:

  • Titrate washing stringency to balance between preserving genuine interactions and eliminating background

  • Consider a gradient of salt concentrations in wash buffers (150mM to 300mM NaCl)

  • Test detergent concentrations to find optimal signal-to-noise ratio

• Confirmation strategies:

  • Reciprocal co-IP using antibodies against predicted interaction partners

  • Mass spectrometry validation of co-immunoprecipitated proteins

  • Yeast two-hybrid or proximity ligation assays as orthogonal validation methods

• Controls to include:

  • IgG control from the same species as the YGR067C antibody

  • Lysates from YGR067C knockout strains

  • Competition with recombinant YGR067C protein

The methods used for other yeast proteins, like Gal4 myc-tagging and immunoprecipitation followed by expression analysis , provide a template for investigating YGR067C interactions with DNA and other proteins.

How can Western blot protocols be optimized for YGR067C detection?

For optimal Western blot detection of YGR067C:

• Sample preparation:

  • Extract proteins using methods that preserve YGR067C integrity (e.g., glass bead lysis for yeast)

  • Include phosphatase inhibitors if phosphorylation state is relevant

  • Optimize loading amount (typically 20-50μg total protein)

• Gel selection and transfer parameters:

  • Choose appropriate percentage acrylamide gel based on YGR067C's molecular weight

  • Optimize transfer conditions (wet transfer at 30V overnight often yields best results for yeast proteins)

  • Consider PVDF membranes for improved protein retention and signal

• Blocking and antibody dilution optimization:

  Blocking Agent	Starting Dilution	Incubation Time	Temperature
  5% BSA in TBST	1:1000	2h to overnight	4°C
  5% non-fat milk	1:500 - 1:2000	1-2h	Room temperature

• Signal detection strategies:

  • For low abundance proteins, consider enhanced chemiluminescence (ECL) with longer exposure times

  • For quantitative analysis, use fluorescent secondary antibodies and imaging systems

  • If background is problematic, increase washing duration and stringency

• Troubleshooting common issues:

  • Multiple bands: Test blocking with immunizing peptide to identify specific signal

  • Weak signal: Increase antibody concentration or protein loading

  • High background: Increase washing steps or try alternative blocking agents

Since YGR067C is a yeast protein, extra attention should be paid to effective cell lysis and protein extraction steps to ensure complete recovery from the yeast cell wall.

How can YGR067C antibody be integrated with microarray data analysis?

Integrating YGR067C antibody studies with microarray data can provide comprehensive insights:

• Correlating binding and expression:

  • Perform ChIP with YGR067C antibody followed by microarray analysis (ChIP-chip)

  • Compare YGR067C binding sites with gene expression changes using algorithms like the superparamagnetic clustering algorithm (SPC)

  • Identify direct targets where YGR067C binding correlates with expression changes

• Pathway analysis workflow:

  • Use microarray data to identify co-expressed genes

  • Apply clustering algorithms that incorporate transcription factor information

  • Validate clusters using YGR067C antibody in targeted ChIP experiments

  • Confirm functional relationships through genetic interaction studies

• Data integration approaches:

  • Combine antibody-based binding data with expression profiles using approaches similar to SPCTF (SPC with transcription factor information)

  • Apply motif finding algorithms like MUSA to identify regulatory elements in YGR067C-bound regions

  • Create network models incorporating both physical interactions and expression correlations

• Validation of microarray findings:

  • Select candidate genes from microarray clusters for targeted ChIP-qPCR validation

  • Use RT-qPCR to confirm expression changes of putative YGR067C targets

  • Perform genetic perturbation experiments to establish causality

This integrated approach has successfully identified previously uncharacterized cell cycle genes in yeast , suggesting its applicability for understanding YGR067C function.

What methods can be used to assess YGR067C antibody specificity in complex experimental systems?

Thorough validation of YGR067C antibody specificity is crucial for reliable research outcomes:

• Genetic validation approaches:

  • Compare signal between wild-type and YGR067C deletion strains

  • Use strains with tagged YGR067C (e.g., epitope tags) to confirm co-localization of signals

  • Employ siRNA/CRISPR knockdown in relevant systems to demonstrate signal reduction

• Biochemical validation methods:

  • Peptide competition assays using the immunizing antigen

  • Western blot analysis looking for a single band of appropriate molecular weight

  • Immunoprecipitation followed by mass spectrometry identification

• Cross-reactivity assessment:

  • Test antibody against closely related proteins or in related yeast species

  • Perform epitope mapping to identify the specific region recognized

  • Check for signal in fractionation experiments (e.g., nuclear vs. cytoplasmic fractions)

• Quantitative specificity metrics:

  Validation Method	Acceptance Criteria	Notes
  Western blot	Single band at expected MW	May see additional bands if modified forms exist
  IP-MS	>70% of peptides match YGR067C	Background proteins should be minimal
  ChIP-qPCR	>4-fold enrichment over IgG control	Target vs. non-target regions
  KO validation	>90% signal reduction	Complete elimination in true knockouts

• Orthogonal detection methods:

  • Compare results from different antibody clones or from different host species

  • Validate with alternative techniques (e.g., MS detection of the protein)

Similar approaches have been used for validating other yeast transcription factors, as demonstrated in chromatin immunoprecipitation studies that confirmed binding to regulated genes .

How can YGR067C antibody be used to investigate post-translational modifications?

Investigating post-translational modifications (PTMs) of YGR067C requires specialized approaches:

• Sample preparation for PTM detection:

  • Include appropriate inhibitors (phosphatase, deacetylase, etc.) during extraction

  • Consider enrichment strategies for specific modifications (e.g., phosphopeptide enrichment)

  • Use gentle extraction methods to preserve labile modifications

• PTM-specific experimental design:

  • Test multiple antibody combinations: anti-YGR067C followed by PTM-specific antibodies

  • Consider using PTM-specific antibodies for IP followed by YGR067C detection

  • For phosphorylation studies, treat samples with/without phosphatase

• Analytical techniques:

  • 2D gel electrophoresis to separate modified forms

  • Phos-tag gels for phosphorylation analysis

  • Mass spectrometry for comprehensive PTM mapping

• Functional correlation studies:

  • Compare PTM status across cell cycle stages or stress conditions

  • Correlate modifications with protein-protein interactions or DNA binding

  • Use site-directed mutagenesis to confirm functional significance of modified residues

• Temporal dynamics assessment:

  • Develop time-course experiments after stimulation or stress

  • Use synchronized yeast cultures to track cell cycle-dependent modifications

  • Employ live-cell imaging with split fluorescent reporters to monitor modification-dependent interactions

PTM investigation can provide crucial insights into regulatory mechanisms controlling YGR067C function, particularly if it plays a role in cell cycle regulation as suggested by clustering analyses .

What are the considerations for using YGR067C antibody in multi-omics experimental design?

Integrating YGR067C antibody-based experiments into multi-omics studies requires careful planning:

• Sample synchronization across platforms:

  • Process parallel samples for ChIP-seq, RNA-seq, and proteomics from the same culture

  • Use identical treatment conditions and timepoints across all analyses

  • Implement consistent normalization strategies across data types

• Integrated experimental design:

  • Sequence ChIP samples from YGR067C antibody pulldowns alongside input controls

  • Correlate binding sites with expression changes from RNA-seq

  • Identify protein interaction networks via IP-MS

  • Integrate with metabolomic changes if metabolic functions are suspected

• Data integration framework:

  Data Type	Method	Integration Approach
  ChIP-seq	YGR067C antibody	Map binding sites genome-wide
  RNA-seq	Transcript profiling	Correlate binding with expression
  Proteomics	IP-MS with YGR067C antibody	Identify protein complexes
  Metabolomics	Targeted or untargeted	Connect to metabolic changes

• Computational analysis pipeline:

  • Apply similar clustering approaches to those used in microarray studies

  • Incorporate transcription factor information into network models

  • Use motif finding algorithms to identify DNA binding preferences

  • Develop integrated visualization of multi-omics data centered on YGR067C function

• Validation strategy:

  • Select key findings for targeted experimental validation

  • Use genetic perturbation to confirm causal relationships

  • Apply CRISPR screening to identify synthetic interactions

This multi-omics approach builds upon traditional clustering methods while incorporating modern genomics and proteomics techniques to provide a comprehensive understanding of YGR067C function in cellular processes.

What are the key considerations for experimental design with YGR067C antibodies?

When designing experiments with YGR067C antibody, researchers should prioritize:

• Rigorous validation of antibody specificity using genetic controls

• Careful optimization of protocols for each application

• Inclusion of appropriate positive and negative controls

• Integration of multiple approaches to build a comprehensive understanding

• Consideration of the protein's potential role in cell cycle regulation

• Application of clustering algorithms that incorporate transcription factor information