Recombinant Saccharomyces cerevisiae Putative uncharacterized protein YGL149W (YGL149W)

What is the structural composition of YGL149W protein?

YGL149W is a putative uncharacterized protein from Saccharomyces cerevisiae (baker's yeast) with an amino acid sequence of GSVVTLLLLLFFCLFLLFFSLHFFCFTREHVHYTLPPKCHSLKFQFDSIPSSSLSLSPFPFLFFPRLRAVAFASPTLSFFFPI. The protein has an expression region spanning from amino acids 19-101, suggesting that the first 18 amino acids likely constitute a signal peptide or another regulatory element . The high proportion of hydrophobic amino acids (leucine, phenylalanine, isoleucine) in the sequence suggests potential membrane association, though further structural studies are required to confirm this hypothesis. Researchers should note that while the protein is uncharacterized, its conservation in yeast suggests evolutionary significance.

What are the recommended storage conditions for recombinant YGL149W protein?

For optimal stability of recombinant YGL149W, store the lyophilized protein at -20°C or -80°C upon receipt. After reconstitution, working aliquots can be stored at 4°C for up to one week, but repeated freeze-thaw cycles should be avoided as they may compromise protein integrity . For long-term storage, it is recommended to add glycerol to a final concentration of 5-50% (with 50% being standard) and store aliquoted samples at -20°C/-80°C . The protein is typically supplied in a Tris/PBS-based buffer with 6% trehalose at pH 8.0, which helps maintain stability during freezing and thawing processes .

How should YGL149W protein be reconstituted for experimental use?

For proper reconstitution of YGL149W:

• Briefly centrifuge the vial prior to opening to bring contents to the bottom

• Reconstitute the protein in deionized sterile water to achieve a concentration of 0.1-1.0 mg/mL

• Add glycerol to a final concentration of 5-50% for storage stability

• Aliquot the reconstituted protein to minimize freeze-thaw cycles

• Verify protein solubility and activity after reconstitution through appropriate assays

This methodological approach ensures maximum retention of protein functionality for downstream applications such as enzymatic assays, binding studies, or structural analyses.

What experimental design is most appropriate for studying the function of uncharacterized proteins like YGL149W?

When investigating uncharacterized proteins like YGL149W, a Completely Randomized Design (CRD) is often suitable for initial screening experiments conducted in controlled laboratory settings . This design is particularly useful when:

• The experimental material is relatively homogeneous

• You have a small number of treatments to compare

• You want flexibility in the number of treatments or replications

• The experiment is conducted in controlled laboratory conditions

For more complex studies where multiple factors might influence protein function, consider:

• Randomized Block Design (RBD) when one additional factor needs to be controlled

• Latin Square Design (LSD) when two additional factors need to be controlled

The key advantage of CRD for initial YGL149W characterization is that all variability among experimental units contributes to experimental error, allowing for a cleaner assessment of treatment effects in controlled environments .

How can researchers optimize replication strategies when working with recombinant YGL149W?

Optimal replication strategies for YGL149W experiments should consider both biological and technical variability:

• Determine appropriate number of replications based on:

  • Expected variability in observations for individual treatments

  • Required precision for estimating treatment effects

  • Available resources and experimental units

• Consider differential replication where more replications are allocated to treatments with:

  • Higher expected variability

  • Greater research interest

  • Need for higher precision in effect estimation

For example, if studying four different treatments of YGL149W under varying conditions, you might allocate different numbers of replications (e.g., 3, 5, 6, and 6) based on these considerations, while ensuring the total number of experimental units is maintained .

A methodologically sound approach involves power analysis to determine the minimum number of replicates needed to detect significant effects at your desired confidence level, followed by proper randomization of experimental units to minimize bias.

What approaches are recommended for functional characterization of YGL149W given its uncharacterized status?

For systematic functional characterization of YGL149W, a multi-omics approach is recommended:

• Comparative Genomics Analysis:

  • Identify potential orthologs in related species

  • Analyze conserved domains and sequence motifs

  • Predict functional associations through phylogenetic profiling

• Protein-Protein Interaction Studies:

  • Yeast two-hybrid screening using His-tagged YGL149W as bait

  • Co-immunoprecipitation followed by mass spectrometry

  • Proximity-labeling approaches (BioID or APEX)

• Phenotypic Analysis:

  • Create YGL149W deletion/overexpression strains

  • Perform high-throughput phenotypic screening under various stress conditions

  • Assess growth curves, metabolic profiles, and morphological changes

• Subcellular Localization:

  • Fluorescent tagging and microscopy

  • Subcellular fractionation and Western blotting

  • Correlation with predicted transmembrane domains based on the hydrophobic amino acid content

Given the hydrophobic nature of the YGL149W amino acid sequence, particular attention should be paid to potential membrane association and lipid interaction studies.

How can researchers address data inconsistencies when studying uncharacterized proteins like YGL149W?

When encountering data inconsistencies in YGL149W studies:

• Evaluate Experimental Design Factors:

  • Reassess whether CRD, RBD, or LSD would be more appropriate for your specific experimental questions

  • Consider whether blocking factors (e.g., protein batch, time of experiment) might be introducing variability

• Analyze Technical Variables:

  • Assess protein purity (>90% as determined by SDS-PAGE is standard)

  • Verify tag influence by comparing His-tagged vs. untagged versions

  • Evaluate buffer composition effects on protein stability and activity

• Apply Statistical Approaches:

  • Use Analysis of Variance (ANOVA) to determine sources of variation

  • Implement appropriate post-hoc tests to identify specific differences

  • Consider nested designs to account for hierarchical sources of variation

• Implement Control Experiments:

  • Include well-characterized proteins as positive controls

  • Use empty vector or scrambled sequences as negative controls

  • Perform spike-in experiments to assess recovery and matrix effects

For reproducible results, document all experimental conditions comprehensively, including precise buffer compositions, incubation times and temperatures, and lot numbers of key reagents.

What are the optimal buffer conditions for maintaining YGL149W stability during experimental procedures?

The optimal buffer conditions for YGL149W stability include:

When designing experiments, it's crucial to consider potential buffer incompatibilities with downstream applications. For instance, high glycerol concentrations may interfere with binding assays, while some detergents might affect spectroscopic measurements. Always perform buffer exchange using dialysis or size exclusion chromatography when transitioning between storage and experimental conditions.

What analytical techniques are most effective for verifying the structural integrity of recombinant YGL149W?

To verify structural integrity of recombinant YGL149W, employ a combination of these analytical techniques:

When applying these techniques, establish appropriate positive controls using well-characterized proteins of similar size and properties to provide context for interpreting YGL149W results.

How should researchers approach statistical analysis of experiments involving YGL149W?

Statistical analysis for YGL149W experiments should follow these methodological principles:

• Design-Appropriate Analysis:

  • For CRD: One-way ANOVA followed by appropriate post-hoc tests

  • For RBD: Two-way ANOVA accounting for treatment and block effects

  • For LSD: Three-way ANOVA accounting for row, column, and treatment effects

• Model Validation:

  • Verify assumptions of normality using Shapiro-Wilk or Kolmogorov-Smirnov tests

  • Check homogeneity of variance using Levene's or Bartlett's test

  • Assess independence of observations through residual plots

• Effect Size Calculation:

  • Report partial eta-squared or Cohen's d values alongside p-values

  • Calculate confidence intervals for all estimates

  • Consider minimum detectable effect sizes in experimental planning

• Multiple Testing Correction:

  • Apply Bonferroni, Šidák, or False Discovery Rate corrections when performing multiple comparisons

  • Report both unadjusted and adjusted p-values for transparency

What bioinformatic approaches can provide insights into potential functions of YGL149W?

To gain insights into YGL149W function through bioinformatics:

• Sequence-Based Analysis:

  • Search for conserved domains using PFAM, SMART, or CDD

  • Predict secondary structure using PSIPRED or JPred

  • Identify signal peptides and transmembrane regions using SignalP and TMHMM

  • Assess post-translational modification sites using NetPhos, NetOGlyc, etc.

• Structure Prediction:

  • Generate 3D models using AlphaFold2 or RoseTTAFold

  • Validate structural models using MolProbity

  • Identify potential binding pockets using CASTp or DoGSiteScorer

  • Perform molecular dynamics simulations to assess stability and flexibility

• Functional Inference:

  • Query protein-protein interaction databases (STRING, BioGRID)

  • Analyze co-expression patterns using yeast microarray/RNA-seq datasets

  • Examine genetic interaction networks from genome-wide screens

  • Investigate phenotypes of deletion mutants in Saccharomyces Genome Database

• Evolutionary Analysis:

  • Construct phylogenetic trees with orthologs from related species

  • Calculate selection pressure (dN/dS ratios) across different domains

  • Identify functionally important residues through evolutionary trace methods

These bioinformatic approaches should be considered complementary to experimental characterization, providing testable hypotheses rather than definitive functional assignments.