YOL164W-A Antibody

What is YOL164W-A and why is it studied in yeast research?

YOL164W-A is a gene in Saccharomyces cerevisiae (baker's yeast) that encodes a protein found in the laboratory strain S288C . While the specific function of this protein is still being investigated, antibodies against YOL164W-A serve as important tools for studying protein expression, localization, and function in yeast models. Yeast serves as an excellent model organism due to its genetic tractability and conservation of fundamental cellular processes with higher eukaryotes.

What are the recommended storage conditions for YOL164W-A antibody?

The YOL164W-A antibody should be stored at -20°C or -80°C upon receipt . It's important to avoid repeated freeze-thaw cycles, which can compromise antibody functionality. The antibody is typically supplied in a storage buffer containing 50% glycerol, 0.01M PBS (pH 7.4), and 0.03% Proclin 300 as a preservative . For working aliquots, small volumes can be prepared and stored separately to minimize freeze-thaw cycles of the stock solution.

What applications has the YOL164W-A antibody been validated for?

The YOL164W-A antibody (CSB-PA662965XA01SVG) has been validated for ELISA and Western blot (WB) applications . When using this antibody in research, it's essential to perform appropriate validation in your specific experimental system. Validation is particularly important as standardized antibody testing shows that performance can vary significantly between different applications, even for antibodies targeting the same protein .

What controls should I use when working with YOL164W-A antibody?

For rigorous experimental design, multiple controls should be employed:

• Positive control: Wild-type S. cerevisiae S288C strain expressing YOL164W-A

• Negative control: Consider using a YOL164W-A knockout strain if available

• Antibody controls: Include secondary antibody-only controls and isotype controls (rabbit IgG)

• Loading controls: When performing Western blots, include housekeeping proteins like actin

Recent standardized antibody validation studies highlight that using knockout cell lines as negative controls provides the most rigorous validation approach . While creating knockout yeast strains requires additional resources, they provide definitive evidence of antibody specificity.

What is the optimal protocol for Western blotting with YOL164W-A antibody?

Based on general antibody validation principles and the specific characteristics of this antibody:

• Sample preparation:

  • Harvest yeast cells in logarithmic growth phase

  • Lyse cells using glass bead disruption in buffer containing protease inhibitors

  • Clear lysate by centrifugation (14,000 × g, 10 min, 4°C)

  • Determine protein concentration (Bradford or BCA assay)

• SDS-PAGE and transfer:

  • Load 20-50 μg protein per lane

  • Separate proteins on 12-15% SDS-PAGE gels

  • Transfer to PVDF or nitrocellulose membrane (wet transfer recommended)

• Antibody incubation:

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

  • Incubate with YOL164W-A antibody (recommended dilution: start with 1:1000)

  • Incubate overnight at 4°C

  • Wash 3× with TBST

  • Incubate with HRP-conjugated anti-rabbit secondary antibody

  • Develop using enhanced chemiluminescence

• Controls:

  • Include wild-type and knockout (if available) samples

  • Include molecular weight markers

Standardized Western blot protocols similar to those used in comprehensive antibody validation studies are recommended for optimal results .

How can I optimize immunoprecipitation experiments with YOL164W-A antibody?

While this specific antibody hasn't been explicitly validated for immunoprecipitation , polyclonal antibodies often perform well in this application. Based on general principles for yeast immunoprecipitation:

• Lysate preparation:

  • Use non-denaturing lysis buffer (e.g., 50 mM Tris-HCl pH 7.5, 150 mM NaCl, 1% NP-40, 0.5% sodium deoxycholate, with protease inhibitors)

  • Clear lysate by centrifugation (14,000 × g, 10 min, 4°C)

• Pre-clearing:

  • Incubate lysate with Protein A/G beads for 1 hour at 4°C

  • Remove beads by centrifugation

• Immunoprecipitation:

  • Add 2-5 μg YOL164W-A antibody to 500 μg-1 mg pre-cleared lysate

  • Incubate overnight at 4°C with gentle rotation

  • Add protein A/G beads and incubate for 2-4 hours at 4°C

  • Wash beads 4× with lysis buffer

  • Elute in sample buffer and analyze by Western blot

• Validation:

  • Confirm specific immunoprecipitation through Western blot detection

  • Include IgG control to identify non-specific binding

Comprehensive antibody validation studies indicate that only 41% of antibodies that perform well in Western blot also perform well in immunoprecipitation, emphasizing the importance of validation for each application .

How can I determine the specificity of YOL164W-A antibody in my experimental system?

Determining antibody specificity is critical for result interpretation. A systematic approach includes:

• Genetic validation:

  • Compare signal between wild-type and YOL164W-A knockout strains

  • Use CRISPR/Cas9 or traditional yeast genetic approaches to generate knockouts

  • Analyze by Western blot and other relevant techniques

• Cross-reactivity assessment:

  • Test antibody against recombinant YOL164W-A protein

  • Test antibody in different yeast strains and species

  • Perform peptide competition assay to confirm epitope-specific binding

• Orthogonal validation:

  • Compare antibody results with orthogonal methods (e.g., mass spectrometry)

  • Use epitope-tagged YOL164W-A constructs as positive controls

Research has shown that genetic approaches using knockout controls provide the most reliable validation compared to orthogonal approaches . For the 65 proteins systematically studied in recent research, 80% of antibodies recommended based on orthogonal strategies and 89% of antibodies recommended based on genetic strategies could detect their intended target in Western blot .

What approaches can be used to study YOL164W-A protein interactions in yeast?

To investigate protein-protein interactions:

• Co-immunoprecipitation with YOL164W-A antibody:

  • Perform immunoprecipitation as described earlier

  • Analyze precipitates by mass spectrometry to identify binding partners

  • Confirm interactions by reciprocal co-IP with antibodies against potential partners

• Proximity-based approaches:

  • BioID or TurboID tagging of YOL164W-A

  • APEX2 proximity labeling

  • Compare results between methods to identify high-confidence interactions

• Yeast two-hybrid screening:

  • Use YOL164W-A as bait to screen yeast libraries

  • Validate hits using co-IP with YOL164W-A antibody

• Functional validation:

  • Generate knockouts of identified interaction partners

  • Assess phenotypic consequences and compare to YOL164W-A knockout

When analyzing protein interactions, it's important to consider both direct and indirect interactions that may be captured in different experimental systems.

What are common issues when working with YOL164W-A antibody in Western blots and how can they be resolved?

Studies on antibody validation indicate that even well-performing antibodies can show non-specific bands, with only a subset showing perfect specificity in Western blot applications . For YOL164W-A antibody, careful optimization of experimental conditions is essential for distinguishing specific from non-specific signals.

How can I quantitatively assess YOL164W-A protein levels in different yeast strains or conditions?

For quantitative analysis:

• Western blot quantification:

  • Use a dilution series of recombinant YOL164W-A protein as standard curve

  • Include consistent loading controls (e.g., actin, GAPDH)

  • Analyze band intensity using software like ImageJ

  • Normalize to loading controls for relative quantification

• ELISA development:

  • Develop sandwich ELISA using YOL164W-A antibody

  • Generate standard curve using recombinant protein

  • Validate assay linearity and dynamic range

• Flow cytometry (if using GFP-tagged constructs):

  • Combine with immunostaining using YOL164W-A antibody

  • Quantify mean fluorescence intensity

• Statistical analysis:

  • Perform at least three biological replicates

  • Apply appropriate statistical tests based on data distribution

  • Consider normality tests before applying parametric statistics

When interpreting quantitative data, consider the dynamic range of detection methods and ensure measurements fall within the linear range of the assay.

How can the YOL164W-A antibody be used in studying post-translational modifications?

For PTM analysis:

• Phosphorylation studies:

  • Treat samples with lambda phosphatase controls

  • Use phos-tag gels to separate phosphorylated forms

  • Compare migration patterns before and after treatment

• Ubiquitination analysis:

  • Perform immunoprecipitation under denaturing conditions

  • Probe with anti-ubiquitin antibodies

  • Use deubiquitinating enzyme inhibitors during lysis

• Glycosylation assessment:

  • Treat samples with glycosidases

  • Observe mobility shifts by Western blot

• Mass spectrometry validation:

  • Immunoprecipitate YOL164W-A

  • Perform LC-MS/MS analysis to identify PTMs

  • Validate using PTM-specific antibodies if available

Recent standardized antibody validation approaches demonstrate that identification of post-translational modifications requires careful experimental design and appropriate controls to distinguish specific antibody recognition of modified forms .

What considerations are important when using YOL164W-A antibody across different yeast species or strains?

Cross-species applications require careful validation:

• Sequence homology analysis:

  • Perform sequence alignments between S. cerevisiae YOL164W-A and homologs

  • Identify conserved regions that may contain the antibody epitope

  • Predict cross-reactivity based on epitope conservation

• Empirical validation:

  • Test antibody against recombinant proteins from different species

  • Include knockout controls in each species where possible

  • Optimize protocols separately for each species

• Strain variations:

  • Compare detection across laboratory strains (S288C, W303, etc.)

  • Consider genetic background effects on expression and modification

• Alternative approaches:

  • Consider epitope tagging in species where antibody validation is challenging

  • Use targeted mass spectrometry as an antibody-independent detection method

Antibody validation studies highlight that cross-species reactivity cannot be assumed without experimental verification, even when target proteins share high sequence homology .

How can machine learning improve antibody selection for YOL164W-A and similar targets?

Machine learning approaches are advancing antibody research:

• Prediction of antibody-antigen binding:

  • Library-on-library approaches can identify specific interacting pairs

  • Machine learning models can predict target binding by analyzing many-to-many relationships between antibodies and antigens

  • Active learning can reduce costs by prioritizing which experiments to perform

• Epitope prediction:

  • Computational tools can predict epitopes in YOL164W-A sequence

  • This information can guide selection of antibodies targeting different regions

  • Structural modeling can improve prediction accuracy

• Cross-reactivity assessment:

  • Algorithms can identify potential cross-reactive proteins

  • This guides validation experiments to confirm specificity

• Performance optimization:

  • Machine learning can identify optimal conditions for specific antibody-antigen pairs

  • Models trained on comprehensive validation datasets improve prediction accuracy

Recent research demonstrates that active learning approaches can significantly reduce experimental costs while maintaining prediction accuracy for antibody-antigen interactions .

What are the most promising approaches for generating improved YOL164W-A-targeting antibodies?

Advanced antibody engineering approaches include:

• Minimally mutated antibodies:

  • Engineering antibodies with fewer mutations while maintaining specificity

  • Structure-guided design to identify essential binding residues

  • Yeast display methods to assess importance of mutations

• Recombinant antibody development:

  • Creating synthetic antibody libraries

  • Selection through phage or yeast display

  • Site-directed mutagenesis to improve affinity and specificity

• Bispecific antibody approaches:

  • Generating antibodies that simultaneously recognize YOL164W-A and another target

  • Useful for co-localization or functional studies

• Single-domain antibodies:

  • Developing smaller antibody formats with improved tissue penetration

  • Camelid-derived nanobodies for challenging epitopes

Research on minimally mutated broadly neutralizing antibodies demonstrates how structural analysis coupled with functional screening can produce antibodies with improved properties while maintaining target specificity .