Recombinant Saccharomyces cerevisiae Putative uncharacterized protein YEL050W-A (YEL050W-A)

What is currently known about YEL050W-A protein?

YEL050W-A is a putative uncharacterized protein from Saccharomyces cerevisiae with a sequence length of 63 amino acids. It can be produced recombinantly in E. coli with a His-tag for purification purposes. According to current databases, there are no annotated phenotypes associated with this protein, suggesting limited functional characterization to date . The protein represents one of many uncharacterized proteins in the yeast genome, which has been completely sequenced but still contains approximately 60% of genes with no assigned function .

What physicochemical properties should be determined first for YEL050W-A?

When beginning characterization of an uncharacterized protein like YEL050W-A, researchers should first determine basic physicochemical properties using computational and experimental approaches:

• Molecular weight, isoelectric point, and extinction coefficient using tools like Expasy's ProtParam

• Hydrophobicity profile (GRAVY value) to predict solubility characteristics

• Instability index (values below 40 indicate stability)

• Secondary structure predictions

• Potential post-translational modification sites

These parameters provide baseline information about protein behavior in solution and guide purification strategies. Similar approaches have been successfully applied to annotate uncharacterized proteins in other organisms with approximately 83% accuracy .

How can I express and purify recombinant YEL050W-A?

Based on available information, the following protocol is recommended:

For a small protein like YEL050W-A (63 amino acids), special attention should be paid to prevent aggregation and maintain stability throughout the purification process .

What bioinformatic strategies can predict potential functions of YEL050W-A?

A comprehensive bioinformatic analysis should include:

• Sequence homology searches using BLAST, HHpred, and HMMER against multiple databases

• Structural prediction using Swiss-Model, Phyre2, and AlphaFold2

• Domain and motif identification using InterProScan, PFAM, and PROSITE

• Subcellular localization prediction with tools like PSORT, TargetP, and DeepLoc

• Analysis of conserved residues across different yeast species

• Promoter analysis to identify potential regulatory elements

• Integration with existing yeast interactome data

Each prediction should be assigned a confidence score based on multiple lines of evidence, following similar approaches used for uncharacterized protein annotation in other organisms .

How can I design genetic experiments to determine YEL050W-A function?

A systematic genetic approach would include:

• Generation of deletion strains in multiple genetic backgrounds to account for strain-specific effects

• Construction of overexpression strains using constitutive and inducible promoters

• Creation of conditional alleles (temperature-sensitive, auxin-inducible degron)

• GFP/RFP tagging for localization studies

• Synthetic genetic array (SGA) analysis to identify genetic interactions

• High-throughput phenotypic screening under various stress conditions

• Integration with yeast evolution experiments to study adaptive roles

The lack of currently annotated phenotypes for YEL050W-A suggests subtle functions that may only be revealed under specific conditions or genetic backgrounds .

What protein-protein interaction methods are most suitable for studying YEL050W-A?

For a small protein like YEL050W-A, the following interaction methods are recommended:

The most comprehensive approach would combine multiple methods, as demonstrated in previous systematic studies of yeast protein interactions. Two-hybrid methods in particular have been successful in characterizing networks of interactions between yeast proteins .

How can I design evolution experiments to understand YEL050W-A function?

Evolution experiments can provide unique insights into YEL050W-A function:

• Generate diverse starting populations through crosses of distinct yeast strains

• Verify efficient mating and viable spore production among founder strains

• Impose selection under various environmental conditions:

  • Different carbon sources

  • Chemical stressors

  • Temperature variation

  • Nutrient limitation

• Track adaptation using whole-genome sequencing at multiple timepoints

• Compare wild-type strains with YEL050W-A deletion strains to identify differential adaptation

• Analyze whether YEL050W-A undergoes functional changes during adaptation

This approach has proven valuable for understanding gene function in previous yeast evolution experiments featuring standing genetic variation .

What high-throughput phenotypic assays should I apply to characterize YEL050W-A?

Despite the current lack of annotated phenotypes , a comprehensive phenotypic analysis should include:

• Growth rate measurements across >100 conditions (temperature, pH, carbon sources, stress)

• Chemical genomic profiling using diverse compound libraries

• Morphological profiling using high-content microscopy

• Metabolic profiling using mass spectrometry

• Cell cycle analysis using flow cytometry

• Transcriptional profiling using RNA-seq under diverse conditions

• Lipidomic analysis to identify membrane-related phenotypes

Data should be analyzed using multivariate statistics to detect subtle phenotypes that may not be apparent from single-condition experiments.

How can I resolve contradictory functional data about YEL050W-A?

When facing contradictory results:

• Systematically evaluate strain background effects:

  • Repeat key experiments in at least three distinct genetic backgrounds

  • Create isogenic strains differing only in YEL050W-A status

• Test epistatic interactions with related pathway components:

  • Double deletion analysis

  • Overexpression screens

  • Chemical-genetic interactions

• Consider condition-specific functionality:

  • Test function across growth phases (log, diauxic shift, stationary)

  • Evaluate under precise environmental conditions

  • Examine meiotic vs. mitotic roles

• Apply quantitative rather than qualitative measurements:

  • Use high-precision growth measurements

  • Apply statistical models to account for variability

  • Integrate multi-omics data for systems-level understanding

• Collaborate with independent laboratories to validate key findings

How can multi-omics integration reveal YEL050W-A function?

A comprehensive systems biology approach would include:

• Comparative transcriptomics:

  • RNA-seq comparing wild-type and YEL050W-A deletion strains

  • Analysis across multiple conditions and time points

• Proteomics analysis:

  • Whole-cell proteome comparison

  • Phosphoproteomics to identify signaling changes

  • Protein turnover analysis

• Metabolomics profiling:

  • Primary metabolite changes

  • Lipid composition analysis

  • Flux analysis using labeled precursors

• Integration of datasets using:

  • Network analysis to identify affected pathways

  • Machine learning approaches to predict functional associations

  • Bayesian integration of heterogeneous data types

• Comparison with existing datasets for other uncharacterized proteins

This multi-faceted approach has proven successful in functional annotation of uncharacterized proteins in other organisms .

What statistical considerations are important when analyzing YEL050W-A phenotypic data?

Rigorous statistical analysis should include:

• Experimental design optimization:

  • Power analysis to determine sample size requirements

  • Randomization and blocking to minimize batch effects

  • Inclusion of appropriate positive and negative controls

• Data analysis approach:

  • Normalization methods appropriate to data type

  • Multiple testing correction (FDR, Bonferroni)

  • Consideration of effect size, not just statistical significance

  • Appropriate transformation for non-normal data

• Advanced statistical methods:

  • Mixed-effects models for complex designs

  • Multivariate analysis for high-dimensional datasets

  • Time-series analysis for dynamic processes

  • Bayesian approaches for integrating prior knowledge

• Validation strategies:

  • Cross-validation of predictive models

  • Independent biological replicates

  • Orthogonal experimental approaches

What databases and tools are essential for YEL050W-A research?

Key resources include:

Currently, YEL050W-A has limited annotations in these databases, highlighting the need for further experimental characterization .

How can I establish effective collaborations for comprehensive YEL050W-A characterization?

For optimal collaborative research:

• Identify complementary expertise partners:

  • Structural biologists for protein structure determination

  • Systems biologists for network analysis

  • Evolutionary biologists for comparative genomics

  • Biochemists for enzymatic characterization

  • Computational biologists for prediction and modeling

• Develop a structured collaboration plan:

  • Clear division of responsibilities

  • Regular communication schedule

  • Data sharing protocols

  • Authorship and intellectual property agreements

• Utilize shared resources:

  • Core facilities for specialized techniques

  • Computational infrastructure for data analysis

  • Strain and plasmid repositories

• Consider joining larger consortium efforts focused on uncharacterized protein annotation

This collaborative approach reflects successful strategies used in comprehensive yeast protein characterization projects .