Recombinant Saccharomyces cerevisiae Putative UPF0479 protein YLR467C-A (YLR467C-A)

What is the basic structure and characteristics of the YLR467C-A protein?

The YLR467C-A protein is a full-length (1-160 amino acids) putative UPF0479 protein from Saccharomyces cerevisiae. The protein has a specific amino acid sequence: MMPAKLQLDVLRTLQSSARHGTQTLKNSNFLERFHKDRIVFCLPFFPALFLVPVQKVLQHLCLRFTQVAPYFIIQLFDLPSRHAENLAPLLASCRIQYTNCFSSSSNGQVPSIISLYLRVDLSPFYAKIFQISYRVPMIWLDVFQVFFVFLVISQHSLHS. This sequence reveals several hydrophobic regions that may suggest membrane association or specific structural domains that could be relevant to its function. The protein is cataloged in UniProt with the identifier P0CL39, indicating it has been characterized at the sequence level, though its precise biological function remains under investigation .

What expression systems are most effective for producing recombinant YLR467C-A protein?

The recombinant production of YLR467C-A protein can be achieved using various expression systems, with E. coli being a well-documented host. When expressing this yeast protein in a bacterial system, researchers typically use an N-terminal His-tag to facilitate purification through affinity chromatography. The expression construct should contain the full coding sequence (1-160 amino acids) with appropriate regulatory elements for the chosen expression system.

For optimal expression in E. coli, consider the following methodological approach:

• Clone the YLR467C-A gene into a vector with an inducible promoter (e.g., T7)

• Transform the construct into an expression strain optimized for recombinant proteins

• Induce expression under controlled conditions (temperature, inducer concentration)

• Harvest cells and lyse using appropriate buffers

• Purify using Ni-NTA or similar affinity chromatography

• Verify purity through SDS-PAGE (>90% purity should be achievable)

What are the recommended storage conditions for maintaining YLR467C-A protein stability?

The stability of purified YLR467C-A protein requires careful consideration of storage conditions. The protein should be stored at -20°C/-80°C upon receipt, with aliquoting necessary for multiple use to avoid repeated freeze-thaw cycles, which can significantly reduce protein activity and integrity. The recommended storage buffer is Tris/PBS-based with 6% Trehalose at pH 8.0.

For working aliquots, storage at 4°C for up to one week is acceptable. When reconstituting lyophilized protein, use deionized sterile water to achieve a concentration of 0.1-1.0 mg/mL. For long-term storage, the addition of glycerol (5-50% final concentration) is recommended before aliquoting and storing at -20°C/-80°C. The standard final concentration of glycerol used is typically 50% .

How can I design experiments to elucidate the functional role of YLR467C-A in Saccharomyces cerevisiae?

Designing experiments to investigate the functional role of YLR467C-A requires a systematic approach combining multiple techniques. Given that this is a putative UPF0479 protein with undefined function, the experimental design should address the following key aspects:

• Gene knockout/knockdown studies: Create YLR467C-A deletion strains using CRISPR-Cas9 or traditional homologous recombination methods. Compare phenotypic differences between wild-type and mutant strains under various growth conditions.

• Protein localization: Develop GFP-tagged versions of YLR467C-A to determine subcellular localization through fluorescence microscopy. This can provide initial clues about function based on where the protein accumulates within the cell.

• Protein-protein interaction analysis: Implement affinity purification-mass spectrometry (AP-MS) or yeast two-hybrid screens to identify interaction partners, which may reveal functional pathways.

• Transcriptomic analysis: Perform RNA-Seq comparing wild-type and YLR467C-A mutant strains to identify differentially expressed genes, potentially revealing pathways affected by this protein.

These approaches should be structured as controlled experiments with appropriate independent and dependent variables. For instance, when studying phenotypic effects of YLR467C-A deletion, the independent variable would be the presence/absence of the protein, while dependent variables could include growth rate, stress resistance, or specific metabolic outputs .

What methods can be used to investigate potential post-translational modifications of YLR467C-A?

Investigating post-translational modifications (PTMs) of YLR467C-A requires a multi-faceted analytical approach:

• Mass spectrometry-based proteomics: Employ high-resolution LC-MS/MS analysis of purified YLR467C-A protein to identify potential modifications. This should include:

  • In-gel or in-solution digestion with multiple proteases (trypsin, chymotrypsin) to ensure comprehensive sequence coverage

  • Enrichment techniques for specific PTMs (phosphopeptide enrichment using TiO2 or IMAC, glycopeptide enrichment using lectin affinity)

  • Data analysis using software capable of detecting mass shifts corresponding to known PTMs

• Site-directed mutagenesis: Once potential modification sites are identified, create mutant versions (e.g., S→A for phosphorylation sites) to assess functional consequences.

• Western blotting: Use modification-specific antibodies (anti-phospho, anti-ubiquitin, etc.) to confirm the presence of specific PTMs.

• In vitro modification assays: Incubate purified YLR467C-A with known modifying enzymes (kinases, acetyltransferases) to determine if the protein can serve as a substrate.

These approaches should be implemented in a systematic manner, with proper controls including unmodified recombinant protein and appropriate standards for each type of PTM being investigated .

How can I develop a quantitative assay to measure YLR467C-A activity if its function is currently unknown?

Developing a quantitative assay for a protein with unknown function requires an exploratory approach that systematically tests multiple potential activities based on structural predictions and homology:

• Sequence and structure-based predictions: Analyze the YLR467C-A sequence using bioinformatic tools to identify conserved domains or motifs that might suggest enzymatic or binding functions. Utilize tools like Pfam, PROSITE, and comparative modeling to generate hypotheses about potential activities.

• Activity screening panel: Develop a matrix of potential biochemical assays based on predicted functions, including:

  • Enzymatic activities (hydrolase, transferase, isomerase)

  • Nucleic acid binding (EMSA with DNA/RNA substrates)

  • Protein binding (SPR or BLI with candidate interactors)

  • Membrane interaction (liposome binding/disruption assays)

• Comparative phenotypic assays: Measure cellular phenotypes in wild-type vs. knockout strains under various conditions (stress, nutrient limitation) and identify quantifiable outputs (metabolite levels, gene expression changes).

The experimental design should include appropriate positive and negative controls, dose-response relationships where applicable, and replicate measurements to ensure statistical validity. Each potential function should be tested with a specific hypothesis, clear independent and dependent variables, and controlled conditions that minimize extraneous variables .

What are the optimal conditions for expressing and purifying recombinant YLR467C-A protein?

The optimization of expression and purification conditions for recombinant YLR467C-A requires systematic evaluation of multiple parameters:

Expression Optimization:

• Host selection: While E. coli is commonly used, compare expression levels in different strains (BL21(DE3), Rosetta, Arctic Express) to address potential codon bias issues.

• Induction parameters: Test various IPTG concentrations (0.1-1.0 mM), temperatures (16°C, 25°C, 37°C), and induction durations (4h vs. overnight).

• Media formulation: Compare rich (LB, TB) versus defined media (M9) supplemented with appropriate nutrients.

Purification Strategy:

• Initial capture: His-tag affinity chromatography using Ni-NTA resin with the following buffer system:

  • Binding buffer: 50 mM Tris-HCl pH 8.0, 300 mM NaCl, 10 mM imidazole

  • Wash buffer: 50 mM Tris-HCl pH 8.0, 300 mM NaCl, 20 mM imidazole

  • Elution buffer: 50 mM Tris-HCl pH 8.0, 300 mM NaCl, 250 mM imidazole

• Secondary purification: Size exclusion chromatography using a Superdex 75 column with buffer containing 20 mM Tris-HCl pH 7.5, 150 mM NaCl.

• Quality control: Assess purity by SDS-PAGE (target >95%), protein identity by mass spectrometry, and structural integrity by circular dichroism.

• Yield optimization: Typical yields from 1L of E. coli culture should be 5-10 mg of purified protein, with optimization potentially increasing this to 15-20 mg/L .

How can I design experiments to investigate potential protein-protein interactions involving YLR467C-A?

Investigating protein-protein interactions (PPIs) involving YLR467C-A requires a multi-technique approach with complementary methods:

• Affinity purification coupled with mass spectrometry (AP-MS):

  • Express His-tagged YLR467C-A in S. cerevisiae under native promoter

  • Perform gentle cell lysis to preserve protein complexes

  • Capture complexes using Ni-NTA or anti-His antibodies

  • Analyze co-purified proteins by LC-MS/MS

  • Implement appropriate controls (e.g., untagged strain, irrelevant His-tagged protein)

• Yeast two-hybrid screening:

  • Create bait construct with YLR467C-A fused to DNA-binding domain

  • Screen against prey library of S. cerevisiae proteins fused to activation domain

  • Validate positive interactions through selective media and reporter gene activation

  • Confirm interactions by directed Y2H with individual candidates

• Proximity-dependent labeling:

  • Generate fusion proteins of YLR467C-A with BioID or APEX2

  • Express in yeast cells and activate labeling

  • Purify biotinylated proteins and identify by mass spectrometry

• Co-immunoprecipitation validation:

  • Generate antibodies against YLR467C-A or use epitope-tagged versions

  • Perform pull-downs and western blot analysis to confirm specific interactions

Data analysis should include filtering against common contaminants, implementation of statistical methods to distinguish true interactions from background, and visualization of resulting interaction networks using appropriate software tools .

What statistical approaches are appropriate for analyzing experimental data related to YLR467C-A function?

Statistical analysis of experimental data related to YLR467C-A function should be tailored to the specific experimental design and data type:

• Comparative growth studies:

  • Use repeated measures ANOVA to analyze growth curves of wild-type vs. mutant strains

  • Apply post-hoc tests (Tukey's HSD) for multiple condition comparisons

  • Consider non-parametric alternatives (Kruskal-Wallis) if normality assumptions are violated

• Protein interaction studies:

  • Implement significance analysis of interactome (SAINT) algorithm for AP-MS data

  • Use false discovery rate (FDR) control methods to account for multiple testing

  • Analyze network topology metrics (degree, betweenness centrality) to identify key interactions

• Transcriptomic responses:

  • Apply differential expression analysis (DESeq2, EdgeR) with appropriate FDR correction

  • Perform gene set enrichment analysis (GSEA) to identify affected pathways

  • Use hierarchical clustering to identify co-regulated gene modules

The experimental design should always include appropriate controls, sufficient biological and technical replicates (minimum n=3 for each condition), and careful consideration of confounding variables. Power analysis should be performed prior to experimentation to determine adequate sample sizes for detecting anticipated effect sizes .

How can I integrate multiple datasets to develop a comprehensive functional model for YLR467C-A?

Integrating multiple datasets to develop a comprehensive functional model for YLR467C-A requires a systematic data integration approach:

• Multi-omics data integration:

  • Combine proteomics, transcriptomics, and phenomics data using computational frameworks

  • Implement weighted data integration methods that account for varying reliability across datasets

  • Apply dimensionality reduction techniques (PCA, t-SNE) to visualize integrated data

• Network-based analysis:

  • Construct protein-protein interaction networks incorporating YLR467C-A

  • Overlay transcriptional response data onto these networks

  • Identify functionally enriched modules using tools like MCODE or ClusterONE

• Bayesian network modeling:

  • Develop probabilistic models of functional relationships

  • Use existing knowledge as priors and update with experimental data

  • Generate testable hypotheses based on conditional probabilities

• Experimental validation:

  • Design targeted experiments to test predictions from integrated models

  • Prioritize validation experiments based on confidence scores from integration

  • Iteratively refine the functional model with new experimental data

The integration process should be documented in a reproducible workflow, ideally using programming environments like R or Python with appropriate packages for bioinformatics analysis (Bioconductor, scikit-learn). This ensures transparency and allows for reanalysis as new data becomes available .

What data table structures are most effective for organizing experimental results related to YLR467C-A research?

Organizing experimental data related to YLR467C-A research requires thoughtful data table structures that facilitate analysis and interpretation:

• Protein characterization data tables:

• Interaction partner data tables:

• Phenotypic analysis data tables:

Effective data tables should include clearly labeled columns, appropriate units, statistical measures (mean, standard deviation, p-values), and metadata about experimental conditions. All tables should be machine-readable and follow principles of tidy data, where each variable forms a column, each observation forms a row, and each type of observational unit forms a table .

What are common challenges in working with recombinant YLR467C-A protein and how can they be addressed?

Working with recombinant YLR467C-A protein presents several challenges that require systematic troubleshooting approaches:

• Low expression yields:

  • Problem: Poor expression in E. coli or other systems

  • Solutions:

    • Optimize codon usage for the expression host

    • Test expression with different fusion tags (MBP, SUMO) to enhance solubility

    • Adjust induction conditions (temperature, inducer concentration, duration)

    • Switch to eukaryotic expression systems for proper folding

• Protein aggregation:

  • Problem: Formation of inclusion bodies or aggregates during expression/purification

  • Solutions:

    • Express at lower temperatures (16-18°C) to slow folding

    • Include mild solubilizing agents (0.1% Triton X-100, 5% glycerol) in lysis buffer

    • Consider refolding protocols if inclusion bodies persist

    • Screen buffer conditions systematically using differential scanning fluorimetry

• Protein instability:

  • Problem: Rapid degradation during storage

  • Solutions:

    • Add protease inhibitors during purification

    • Include stabilizing agents (glycerol, trehalose) in storage buffer

    • Aliquot and flash-freeze immediately after purification

    • Store with oxygen scavengers if oxidation is occurring

• Functional assay development:

  • Problem: Difficulty establishing activity assays for a protein of unknown function

  • Solutions:

    • Begin with binding assays using potential substrates based on bioinformatic predictions

    • Test activity under various buffer conditions (pH range 5.5-9.0, salt concentrations 50-500 mM)

    • Include potential cofactors (divalent cations, nucleotides) in activity screens

    • Consider coupled enzyme assays to detect subtle biochemical activities

Quality control should include SDS-PAGE analysis, mass spectrometry verification, circular dichroism to assess secondary structure, and thermal shift assays to confirm proper folding. Each troubleshooting intervention should be tested systematically with appropriate controls .

How can I validate that my recombinant YLR467C-A protein is correctly folded and functional?

Validating proper folding and functionality of recombinant YLR467C-A requires a multi-parameter assessment approach:

• Structural integrity analysis:

  • Circular dichroism (CD) spectroscopy to assess secondary structure content

  • Fluorescence spectroscopy to evaluate tertiary structure through intrinsic tryptophan emission

  • Size exclusion chromatography to confirm proper oligomeric state

  • Limited proteolysis to verify compact folding (properly folded proteins typically show resistance to mild proteolytic digestion)

• Thermal and chemical stability:

  • Differential scanning fluorimetry (DSF) to determine melting temperature (Tm)

  • Chemical denaturation curves using urea or guanidinium hydrochloride

  • Comparison of stability parameters between batches to ensure consistency

• Binding and functional assays:

  • Surface plasmon resonance (SPR) or isothermal titration calorimetry (ITC) with predicted binding partners

  • Comparison of binding parameters with literature values if available

  • Activity assays based on bioinformatic predictions of function

  • Complementation assays in YLR467C-A deletion strains

• Comparative analysis:

  • Side-by-side comparison with YLR467C-A purified from native source (if feasible)

  • Benchmark against related proteins with known structures/functions

A correctly folded and functional protein should demonstrate consistent biophysical parameters across multiple purification batches, specific binding to predicted partners, and the ability to rescue phenotypes in genetic deletion studies. Deviations in any of these parameters may indicate issues with folding or post-translational modifications that affect function .

How can I use structural biology approaches to study YLR467C-A and inform functional hypotheses?

Structural biology approaches can provide crucial insights into YLR467C-A function through detailed analysis of its three-dimensional architecture:

• X-ray crystallography workflow:

  • High-throughput crystallization screening (384-condition sparse matrix)

  • Optimization of promising crystallization conditions

  • Data collection at synchrotron radiation facilities

  • Phase determination (molecular replacement or experimental phasing)

  • Model building, refinement, and validation

• Cryo-electron microscopy approach:

  • Sample preparation optimization for homogeneity

  • Negative stain EM for initial structural assessment

  • Cryo-EM grid preparation and screening

  • High-resolution data collection and processing

  • 3D reconstruction and model building

• NMR spectroscopy for dynamic analyses:

  • Isotopic labeling (15N, 13C) of recombinant YLR467C-A

  • Assignment of backbone and side-chain resonances

  • Structure determination through NOE distance restraints

  • Relaxation experiments to characterize dynamic regions

• Integrative structural biology:

  • Combine low-resolution techniques (SAXS, SANS) with high-resolution data

  • Implement computational modeling to fill structural gaps

  • Validate models through crosslinking mass spectrometry

• Structure-based functional annotation:

  • Identify potential binding sites through surface electrostatic analysis

  • Perform structural alignments with functionally characterized proteins

  • Use computational docking to predict interactions with potential ligands

What CRISPR-based approaches can be used to study YLR467C-A function in vivo?

CRISPR-based approaches offer powerful tools for investigating YLR467C-A function directly in S. cerevisiae:

• Gene knockout/knockdown strategies:

  • Complete gene deletion using CRISPR-Cas9 and homology-directed repair

  • Conditional depletion using an auxin-inducible degron system fused to YLR467C-A

  • CRISPRi (dCas9-repressor) for transcriptional repression without genetic modification

• Domain mapping and structure-function analysis:

  • Precise insertion of premature stop codons to create truncation variants

  • Targeted mutagenesis of predicted functional residues

  • Domain swapping with homologous proteins to create chimeras

• Tracking and visualization:

  • Endogenous tagging with fluorescent proteins or epitope tags

  • Implementation of split fluorescent protein systems to detect protein-protein interactions

  • CRISPR activation (CRISPRa) to upregulate expression for overexpression studies

• High-throughput functional screening:

  • CRISPR tiling screens across the YLR467C-A locus to identify functional elements

  • Combinatorial CRISPR screens targeting YLR467C-A together with other genes to identify genetic interactions

  • Base editing or prime editing to introduce specific point mutations

The experimental design for these approaches should include appropriate controls (non-targeting gRNAs, wild-type cells), phenotypic readouts relevant to hypothesized functions, and validation of CRISPR editing efficiency and specificity. Off-target effects should be assessed using whole-genome sequencing or targeted sequencing of predicted off-target sites .