YDL187C Antibody

What is YDL187C and why is it studied in yeast research?

YDL187C is a protein encoded by the YDL187C gene in Saccharomyces cerevisiae (Baker's yeast, strain ATCC 204508/S288c). This protein is studied as part of fundamental research into yeast cellular processes and functions. The antibody against this protein is primarily used to detect and quantify YDL187C in experimental settings. Understanding YDL187C contributes to the broader knowledge base of yeast genetics and protein function, which serves as a model system for eukaryotic cell biology. When designing experiments with this antibody, researchers should consider that it is raised against recombinant YDL187C protein and has been affinity-purified to increase specificity. For optimal results, researchers should use the antibody within the validated applications (ELISA and Western Blot) and confirm experimental conditions before proceeding with critical experiments .

What are the validated applications for YDL187C antibody?

The YDL187C antibody has been specifically validated for ELISA (Enzyme-Linked Immunosorbent Assay) and Western Blot (WB) applications. These techniques allow for detection and semi-quantitative analysis of the target protein in various experimental contexts. For Western Blot applications, researchers should follow standard protocols with particular attention to optimizing blocking conditions (typically 5% non-fat milk or BSA in TBST) and antibody dilutions (starting with manufacturer recommendations, typically 1:1000-1:2000). For ELISA applications, the antibody can be used as a detection or capture antibody depending on the experimental design. Researchers should note that while these are the validated applications, optimization may be needed for specific experimental conditions. When planning to use this antibody for other applications such as immunohistochemistry or immunoprecipitation, preliminary validation experiments should be conducted as cross-application utility cannot be assumed without proper verification .

How should YDL187C antibody be stored and handled for maximum stability?

Proper storage and handling of YDL187C antibody is crucial for maintaining its functionality and specificity. According to product specifications, the antibody should be stored at -20°C or -80°C upon receipt. Repeated freeze-thaw cycles should be avoided as they can cause protein denaturation and loss of antibody activity. To minimize freeze-thaw cycles, it is recommended to aliquot the antibody into smaller volumes before freezing. The antibody is supplied in a storage buffer containing 50% glycerol, 0.01M PBS (pH 7.4), and 0.03% Proclin 300 as a preservative. When working with the antibody, it should be thawed on ice or at 4°C, never at room temperature. For long-term experiments, prepare working dilutions fresh each time rather than storing diluted antibody. Documentation of lot numbers, receipt dates, and aliquoting is recommended as part of good laboratory practice to track antibody performance over time and across experiments .

How can I validate the specificity of YDL187C antibody for my experiments?

Validating antibody specificity is a critical step before using YDL187C antibody in research applications. A comprehensive validation approach should include multiple complementary methods. First, perform a Western blot with positive controls (expressing YDL187C) and negative controls (YDL187C knockout or in species not expected to cross-react). Look for a single band at the expected molecular weight (~37 kDa for YDL187C). Second, conduct peptide competition assays by pre-incubating the antibody with excess purified YDL187C protein or the immunizing peptide; this should eliminate or significantly reduce signal if the antibody is specific. Third, consider orthogonal validation by comparing results with alternative detection methods such as mass spectrometry or a different antibody targeting a separate epitope of YDL187C. For genetic validation, use RNA interference or CRISPR-Cas9 to knock down YDL187C expression and confirm corresponding reduction in antibody signal. Document all validation steps methodically, including experimental conditions, dilutions, and exposure times to ensure reproducibility .

What is the optimal protocol for using YDL187C antibody in Western blot analysis?

For optimal Western blot results with YDL187C antibody, follow this detailed protocol: Begin with proper sample preparation by lysing yeast cells in a buffer containing protease inhibitors (e.g., 50 mM Tris-HCl pH 7.5, 150 mM NaCl, 1% Triton X-100, 1 mM EDTA with complete protease inhibitor cocktail). Determine protein concentration using Bradford or BCA assay. Load 20-30 μg of total protein per well on a 10-12% SDS-PAGE gel. After electrophoresis, transfer proteins to a PVDF membrane (nitrocellulose is an acceptable alternative). Block the membrane with 5% non-fat milk in TBST (TBS + 0.1% Tween-20) for 1 hour at room temperature. Dilute YDL187C antibody according to manufacturer recommendations (typically 1:1000-1:2000) in blocking solution and incubate overnight at 4°C with gentle rocking. Wash the membrane 3-4 times with TBST, 5 minutes each. Incubate with HRP-conjugated secondary antibody (anti-rabbit IgG) at 1:5000-1:10000 dilution for 1 hour at room temperature. Wash 3-4 times with TBST. Develop using enhanced chemiluminescence (ECL) substrate and image using an appropriate detection system. For quantitative analysis, include loading controls (e.g., actin or GAPDH) and use image analysis software to normalize band intensities. If background is high or bands are unclear, optimize by adjusting antibody dilutions, extending washing steps, or using alternative blocking agents such as BSA .

How can I optimize immunoprecipitation experiments using YDL187C antibody?

While YDL187C antibody has not been specifically validated for immunoprecipitation (IP), researchers can attempt optimization with the following approach: First, conduct a pilot IP experiment using 2-5 μg of antibody per 500 μg of total protein lysate. Prepare yeast lysate in a gentle lysis buffer (e.g., 50 mM Tris-HCl pH 7.5, 150 mM NaCl, 0.5% NP-40, 1 mM EDTA with protease inhibitors) to preserve protein-protein interactions. Pre-clear the lysate with Protein A/G beads for 1 hour at 4°C to reduce non-specific binding. Incubate pre-cleared lysate with YDL187C antibody overnight at 4°C with gentle rotation. Add fresh Protein A beads (for rabbit polyclonal antibodies) and incubate for 2-3 hours at 4°C. Perform sequential washes with decreasing salt concentrations (e.g., start with 500 mM NaCl and reduce to 150 mM NaCl) to remove non-specific interactions while preserving specific ones. Elute bound proteins by boiling in SDS sample buffer or using a gentle elution buffer (0.1 M glycine-HCl, pH 2.5-3.0) and immediately neutralize with 1 M Tris-HCl, pH 8.0. Confirm successful IP by Western blot using a portion of the IP sample. If initial results are suboptimal, optimize by cross-linking the antibody to beads using dimethyl pimelimidate (DMP) to prevent antibody contamination in the eluate, adjusting antibody amounts, or modifying buffer compositions .

How can I address weak or absent signal when using YDL187C antibody?

When encountering weak or absent signals with YDL187C antibody, implement a systematic troubleshooting approach. First, verify protein expression and loading by staining with Ponceau S or using an alternative housekeeping protein antibody. Check antibody viability by dot blot with purified antigen or known positive control. Increase protein concentration (40-50 μg per lane) or adjust antibody concentration (try 1:500 instead of 1:1000). Optimize incubation conditions by extending primary antibody incubation to overnight at 4°C and increasing secondary antibody incubation to 2 hours at room temperature. Modify detection sensitivity by using a more sensitive ECL substrate or increasing exposure time. For particularly challenging detections, consider signal amplification methods such as biotin-streptavidin systems. If using yeast strains different from the immunogen (Saccharomyces cerevisiae strain ATCC 204508/S288c), sequence variation might affect epitope recognition; in this case, confirm target protein sequence homology. Finally, evaluate experimental conditions that might affect protein expression levels, such as growth phase, media composition, or stress conditions. Document all modifications systematically to establish optimal conditions for future experiments .

What strategies can address non-specific binding and high background with YDL187C antibody?

Non-specific binding and high background are common challenges when working with polyclonal antibodies like YDL187C antibody. To address these issues, implement multiple optimization strategies: First, increase blocking stringency by using 5% BSA instead of non-fat milk, or try commercial blocking solutions specifically designed for yeast applications. Extend blocking time to 2 hours at room temperature or overnight at 4°C. Optimize antibody dilution by performing a dilution series (1:500, 1:1000, 1:2000, 1:5000) to identify the optimal concentration that maximizes specific signal while minimizing background. Add 0.1-0.5% Tween-20 or 0.1% Triton X-100 to antibody dilution buffers to reduce non-specific hydrophobic interactions. Increase washing stringency with additional wash steps (5-6 washes for 10 minutes each) and higher detergent concentration in wash buffers (0.1-0.2% Tween-20). For Western blots specifically, pre-adsorb the antibody with proteins from a YDL187C knockout strain to remove antibodies that recognize non-specific epitopes. Consider using more specific detection methods like fluorescent secondary antibodies, which often provide better signal-to-noise ratios than HRP-based detection. If background persists, perform a peptide competition assay to distinguish between specific and non-specific signals. Document all optimization steps methodically to establish a reproducible protocol .

How can I quantitatively analyze Western blot results using YDL187C antibody?

Quantitative analysis of Western blot results using YDL187C antibody requires careful experimental design and standardized analysis methods. Begin by ensuring linear range detection: perform a dilution series of your protein sample to determine the range where signal intensity correlates linearly with protein amount. Include appropriate loading controls such as Pgk1 or Tdh1 for yeast samples, preferably with molecular weights distinctly different from YDL187C to avoid signal overlap. Use technical replicates (minimum three) and biological replicates (minimum three) to account for technical and biological variability. For image acquisition, use a digital imaging system with a wide dynamic range rather than X-ray film. Avoid saturated pixels during image capture as they prevent accurate quantification. For data analysis, use specialized software such as ImageJ, ImageLab, or similar to measure integrated density of bands. Subtract local background from each band. Normalize YDL187C signal to loading control signals within each lane. For time-course or comparative studies, include an internal reference sample on each blot to allow inter-blot normalization. Apply appropriate statistical analysis based on experimental design (t-test, ANOVA, etc.). When reporting results, include both normalized data and representative images showing all technical repeats. Consider the following table format for presenting quantitative results :

Does YDL187C antibody cross-react with homologous proteins in other yeast species?

Cross-reactivity assessment is essential when applying YDL187C antibody across different yeast species. The antibody was raised against Saccharomyces cerevisiae (strain ATCC 204508/S288c) YDL187C protein, which serves as the reference species. Cross-reactivity depends primarily on sequence homology in the epitope region(s) recognized by the polyclonal antibody. Based on bioinformatic analysis of homologous proteins, potential cross-reactivity may exist with closely related Saccharomyces species (S. paradoxus, S. mikatae, S. bayanus) which typically share 80-95% sequence identity with S. cerevisiae proteins. More distant yeasts such as Candida albicans, Schizosaccharomyces pombe, or Kluyveromyces lactis share lower homology (typically 40-60%), making cross-reactivity less likely but still possible. To experimentally determine cross-reactivity, perform Western blot analysis using protein extracts from multiple yeast species in parallel with S. cerevisiae controls. Look for bands at the expected molecular weight for homologous proteins (which may differ slightly from the S. cerevisiae protein). Confirm any potential cross-reactivity through mass spectrometry identification of the detected protein. If cross-reactivity with a specific species is critical for your research, consider pre-adsorption tests to determine antibody specificity. Document all cross-reactivity findings to build a comprehensive specificity profile for the antibody .

How can I identify epitope regions recognized by YDL187C polyclonal antibody?

Identifying the epitope regions recognized by YDL187C polyclonal antibody provides valuable information for experimental design and interpretation. As a polyclonal antibody, it likely recognizes multiple epitopes within the YDL187C protein. To identify these regions, employ a combination of experimental and computational approaches. Begin with epitope mapping using peptide arrays: synthesize overlapping peptides (typically 15-20 amino acids with 5-10 amino acid overlaps) spanning the entire YDL187C sequence and test antibody binding to each peptide via ELISA or peptide array platforms. Alternatively, perform limited proteolysis of the YDL187C protein followed by immunoblotting to identify which fragments retain antibody recognition. For computational prediction, use epitope prediction algorithms (e.g., BepiPred, DiscoTope) to identify potential linear and conformational epitopes based on the YDL187C protein sequence and structure (if available). To determine if the antibody recognizes conformational epitopes, compare binding under reducing versus non-reducing conditions. For a more precise mapping, consider hydrogen-deuterium exchange mass spectrometry (HDX-MS) to identify regions protected from exchange upon antibody binding. Document the identified epitopes and their conservation across related proteins, as this information will help predict potential cross-reactivity with homologous proteins and inform experimental design when studying protein variants or truncations .

How can I leverage antibody data repositories to find alternative antibodies against YDL187C?

Navigating antibody data repositories efficiently can save considerable time and resources when searching for YDL187C antibodies. Start by using specialized antibody search engines that aggregate information from multiple vendors. Some recommended platforms include CiteAb, Antibodypedia, and Biocompare, which allow searching by target protein name or gene identifier. When searching, use multiple identifiers including "YDL187C," the UniProt accession number (Q07613), and alternative protein names if available. Compare antibodies based on validation data, applications, and host species. For yeast protein antibodies specifically, repositories focused on model organisms may provide additional resources. Beyond commercial repositories, consider academic resources like the Yeast Resource Center or community platforms where researchers share validation data for antibodies used in published work. When evaluating potential alternatives, prioritize antibodies with experimental validation in applications matching your intended use. The table below outlines key repositories and their features for finding YDL187C antibodies :

How can I use YDL187C antibody in combination with other techniques for comprehensive protein analysis?

Integrating YDL187C antibody-based methods with complementary techniques creates a powerful multi-dimensional approach to protein analysis. Begin with immunoprecipitation using YDL187C antibody followed by mass spectrometry (IP-MS) to identify interaction partners and post-translational modifications. This combination provides both specificity from the antibody and comprehensive protein characterization from MS. For spatial analysis, combine immunofluorescence using YDL187C antibody with subcellular fractionation to determine both localization and biochemical distribution of the protein. To study protein dynamics, implement Fluorescence Recovery After Photobleaching (FRAP) using fluorescently-tagged secondary antibodies against the YDL187C antibody in fixed cells. For functional studies, correlate Western blot quantification of YDL187C using the antibody with phenotypic assays or RNA-seq data to establish relationships between protein levels and cellular functions. To investigate protein structure-function relationships, use the antibody to detect various truncated or mutated versions of YDL187C, correlating epitope recognition with functional assays. For systems-level analysis, combine ChIP-seq (if YDL187C has DNA-binding properties) or RNA immunoprecipitation (RIP) if it binds RNA with antibody-based protein quantification to create integrated models of protein function. Document all methodological details when combining techniques to ensure reproducibility and proper interpretation of multi-dimensional data .

What are the considerations for using YDL187C antibody in high-throughput screening approaches?

Adapting YDL187C antibody for high-throughput screening (HTS) requires careful optimization of protocols for automation, scalability, and reproducibility. First, determine the minimum antibody concentration that provides reliable signal detection through a systematic dilution series (typically ranging from 1:500 to 1:5000) to optimize cost-efficiency without compromising data quality. For plate-based assays, validate signal linearity across a wide range of antigen concentrations and establish Z-factor values (aim for Z' > 0.5) to ensure assay robustness. Implement automated liquid handling for consistent antibody dispensing and washing steps to minimize well-to-well variability. Consider using detection methods compatible with HTS automation such as fluorescence-based or luminescence-based readouts rather than traditional colorimetric methods. For cell-based screens, optimize fixation and permeabilization protocols to ensure consistent antibody accessibility to intracellular targets across all samples. Implement quality control measures such as including standard curves and positive/negative controls on each plate, calculating coefficient of variation between technical replicates (<15% is typically acceptable for HTS), and using statistical methods to identify and handle outliers. For image-based high-content screening using the antibody, optimize staining protocols for automated microscopy and develop reliable image analysis algorithms to extract quantitative data. When scaling up, conduct pilot studies with a subset of conditions to estimate variability and determine the number of replicates needed for statistical power. Document all parameters including reagent batches, incubation times, and temperature conditions to ensure reproducibility across screening runs .

How can computational approaches enhance antibody design and specificity for YDL187C-related research?

Computational approaches offer powerful tools for optimizing antibody design and specificity in YDL187C research. Modern biophysics-informed models can disentangle different binding modes associated with specific ligands, even when working with structurally similar targets. For YDL187C antibody research, start by applying structural bioinformatics to predict potential epitopes based on the protein's three-dimensional structure or sequence features. Use molecular dynamics simulations to understand the flexibility and accessibility of these epitopes in various experimental conditions. When developing new antibodies against YDL187C, machine learning approaches can help predict antibody-antigen binding affinities based on sequence information. This allows for virtual screening of potential antibody candidates before experimental validation, significantly reducing development time and resources. For existing antibodies, computational modeling can predict cross-reactivity with homologous proteins by analyzing structural similarities in the epitope regions. When encountering specificity issues, computational design can identify key amino acid substitutions to increase antibody specificity, as demonstrated in recent antibody engineering studies. These approaches have successfully redesigned antibodies against viral targets by identifying a minimal set of mutations that restore or enhance binding specificity. For researchers developing custom antibodies against YDL187C, collaboration with computational biology groups can lead to the development of antibodies with tailored specificity profiles, either with high specificity for YDL187C alone or with controlled cross-reactivity to homologous proteins in different yeast species. This computational-experimental synergy represents the cutting edge of antibody engineering for research applications .

How should I interpret contradictory results between different antibody-based techniques when studying YDL187C?

When faced with contradictory results across different antibody-based techniques studying YDL187C, adopt a systematic reconciliation approach. First, carefully document all experimental conditions for each technique, including sample preparation methods, antibody dilutions, detection systems, and quantification approaches. Consider technique-specific limitations: Western blot denatures proteins and may expose epitopes hidden in native conditions, while immunofluorescence preserves cellular context but may suffer from fixation artifacts. ELISA detects soluble proteins but may miss membrane-bound forms. Evaluate antibody performance in each context by conducting epitope availability analysis under different conditions (native vs. denatured, reduced vs. non-reduced). For polyclonal antibodies like YDL187C antibody, different subpopulations of antibodies within the polyclonal mixture may perform differently across techniques. To resolve contradictions, implement orthogonal methods that don't rely on antibodies, such as mass spectrometry, RNA expression analysis, or tagged protein approaches. Consider biological explanations for discrepancies, such as post-translational modifications, protein isoforms, or context-dependent protein interactions that might mask epitopes in specific cellular compartments. When reporting contradictory results, present all data transparently with appropriate controls and discuss potential technical and biological explanations. Compile results in a comparison table highlighting conditions and outcomes across techniques to facilitate systematic troubleshooting and interpretation .

What statistical approaches are most appropriate for analyzing data generated using YDL187C antibody?

Selecting appropriate statistical methods for analyzing YDL187C antibody data ensures robust and meaningful interpretation of results. For Western blot quantification, begin with descriptive statistics (mean, median, standard deviation) to characterize central tendency and variability. For comparative analysis between experimental groups, use parametric tests (t-test for two groups, ANOVA for multiple groups) if data follows normal distribution, or non-parametric alternatives (Mann-Whitney U test, Kruskal-Wallis test) if normality cannot be assumed. Always check data for normality using Shapiro-Wilk or Kolmogorov-Smirnov tests before selecting the appropriate method. For immunofluorescence quantification, consider the hierarchical nature of the data (multiple cells within fields, multiple fields within samples) and use nested ANOVA or mixed-effects models to account for this structure. When analyzing co-localization data, use specialized statistical measures such as Pearson's correlation coefficient, Manders' overlap coefficient, or object-based methods depending on the specific question. For ELISA data, use four-parameter logistic regression for standard curve fitting rather than linear regression to account for the sigmoidal nature of dose-response relationships. When dealing with high-throughput data, implement multiple testing correction methods (Bonferroni, Benjamini-Hochberg) to control false discovery rates. For all analyses, calculate and report effect sizes (Cohen's d, partial eta-squared) alongside p-values to communicate biological significance beyond statistical significance. Document all statistical parameters including test selection criteria, significance thresholds, software packages, and versions used for analysis to ensure reproducibility .

How can I integrate YDL187C antibody-based findings with other omics data for systems biology approaches?

Integrating YDL187C antibody-based data with multi-omics datasets creates comprehensive systems-level understanding of yeast biology. Begin by standardizing quantification methods for YDL187C protein levels across experiments to ensure comparability with other data types. For integration with transcriptomics, correlate protein abundance (measured by Western blot or ELISA) with mRNA levels (from RNA-seq or microarray data) to identify post-transcriptional regulation mechanisms. Conduct time-course experiments measuring both transcript and protein levels to establish temporal relationships and regulatory dynamics. When integrating with proteomics data, use YDL187C antibody for targeted validation of mass spectrometry findings, particularly for confirming protein interactions or post-translational modifications. For metabolomics integration, correlate YDL187C protein levels with metabolite profiles under various conditions to identify functional connections to metabolic pathways. Employ network analysis approaches such as weighted gene correlation network analysis (WGCNA) or Bayesian networks to identify modules of co-regulated genes/proteins that include YDL187C. For visualization and interpretation, implement multi-omics data integration platforms such as Cytoscape with specialized plugins or R/Bioconductor packages designed for integrative analysis. Consider using machine learning approaches such as random forests or support vector machines to identify patterns across data types that predict specific phenotypes. Document all data processing steps, normalization methods, and statistical approaches used for integration to ensure reproducibility and facilitate meta-analysis. When publishing integrated analyses, provide access to processed datasets through appropriate repositories to enable community-wide use of the integrated data resources .