Recombinant Saccharomyces cerevisiae Uncharacterized protein YDR119W-A (YDR119W-A)

What makes Saccharomyces cerevisiae a suitable model organism for studying uncharacterized proteins like YDR119W-A?

S. cerevisiae is an excellent model organism for studying uncharacterized proteins due to several key advantages. It is inherently nonpathogenic, can be easily engineered to express proteins in large quantities, and can be rapidly propagated and purified . Furthermore, approximately 30% of genes implicated in human disease may have orthologs in the yeast proteome, making it valuable for studying protein functions with potential human relevance . For uncharacterized proteins like YDR119W-A, yeast offers well-established genetic manipulation tools, a fully sequenced genome, and extensive databases of protein interactions that facilitate functional characterization through comparative genomics and proteomic approaches .

How do researchers initially approach the characterization of an uncharacterized protein like YDR119W-A?

Initial characterization typically follows a systematic approach:

• Bioinformatic analysis: Sequence alignment with orthologs across species to predict functional domains and evolutionary conservation .

• Expression profiling: Determining when and where the protein is expressed using RNA-seq and proteomics approaches.

• Protein localization: Using GFP tagging to determine subcellular localization.

• Interaction studies: Employing yeast two-hybrid or co-immunoprecipitation to identify protein-protein interactions.

• Phenotypic analysis: Creating knockout strains and assessing phenotypic changes under various conditions.

This multifaceted approach provides complementary data points that collectively suggest potential functions for previously uncharacterized proteins .

What expression systems are most effective for recombinant production of yeast proteins like YDR119W-A?

For recombinant expression of yeast proteins like YDR119W-A, several systems can be considered, each with specific advantages:

Yeast-based expression systems:

• Constitutive promoters: The TEF2 (translation elongation factor 1-alpha) promoter provides high-level constitutive expression, similar to what has been used for other recombinant proteins in S. cerevisiae .

• Inducible systems: Copper-inducible promoters allow controlled expression timing, though for some applications, constitutive promoters may be preferred .

Expression vectors:

• High-copy 2μM expression plasmids (such as pGI-100) serve as effective backbone vectors for recombinant protein expression .

When expressing YDR119W-A, researchers should consider codon optimization, inclusion of appropriate tags for detection and purification, and verification of expression using immunoblot analysis with specific antibodies, similar to methods described for other recombinant proteins in S. cerevisiae .

What are the optimal PCR conditions for amplifying the YDR119W-A gene from genomic DNA?

For optimal PCR amplification of YDR119W-A from S. cerevisiae genomic DNA, the following methodological approach is recommended:

• Primer design: Design gene-specific primers that incorporate restriction sites compatible with your expression vector. For example:

  • Forward primer: 5'-CGGAATTC(EcoRI site)-(20-25 bp of gene-specific sequence)-3'

  • Reverse primer: 5'-ATAAGAATGCGGCCGC(NotI site)-(20-25 bp of gene-specific sequence)-3'

• PCR reaction components:

  • High-fidelity DNA polymerase (e.g., Phusion or Q5)

  • Buffer system optimized for GC-rich yeast genomic DNA

  • DMSO (2-5%) to reduce secondary structure formation

  • Template: 50-100 ng of genomic DNA

• Thermal cycling conditions:

  • Initial denaturation: 98°C for 3 minutes

  • 30-35 cycles of:

    • Denaturation: 98°C for 15 seconds

    • Annealing: 55-65°C for 20 seconds (optimize based on primer Tm)

    • Extension: 72°C for 30 seconds per kb

  • Final extension: 72°C for 5 minutes

This approach parallels methods used for amplifying other yeast genes for recombinant expression, such as those described for CEA cDNA amplification in the reference .

How should researchers validate the expression of recombinant YDR119W-A protein?

Validation of recombinant YDR119W-A expression requires a comprehensive approach:

• Molecular verification:

  • PCR confirmation of gene insertion in the expression vector

  • Sequencing to verify the absence of mutations

• Protein expression analysis:

  • Immunoblot analysis using antibodies against an epitope tag (if included) or custom antibodies against YDR119W-A

  • Comparison with wild-type yeast to confirm overexpression

• Quantitative assessment:

  • Densitometric analysis of Western blots

  • Mass spectrometry-based quantification

For effective immunoblotting, researchers should use appropriate controls and verify both the expected molecular weight and post-translational modifications of the protein, as seen in the example where a recombinant protein showed both 71 kDa and 130 kDa bands due to differential glycosylation and GPI anchoring .

What techniques are most effective for studying protein-protein interactions involving YDR119W-A?

Several complementary techniques are recommended for studying protein-protein interactions involving uncharacterized proteins like YDR119W-A:

• Yeast two-hybrid (Y2H) screening:

  • Construct bait plasmids with YDR119W-A fused to a DNA-binding domain

  • Screen against a prey library of S. cerevisiae proteins

  • Validate positive interactions with secondary assays

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

  • Express tagged YDR119W-A in yeast

  • Purify protein complexes using the tag

  • Identify interacting partners by mass spectrometry

• Proximity-dependent biotin identification (BioID):

  • Fuse YDR119W-A with a biotin ligase

  • Identify proximal proteins through biotinylation

  • Purify and identify biotinylated proteins

• Co-immunoprecipitation (Co-IP):

  • Use antibodies against YDR119W-A or a tag

  • Pull down protein complexes

  • Identify partners by Western blot or mass spectrometry

These methods should be applied in combination to build confidence in identified interactions, as each has specific strengths and limitations. The orthology-based approaches used to identify functional relationships in S. cerevisiae can help prioritize candidate interactors for validation .

How can researchers perform comparative genomic analysis to predict YDR119W-A function?

Comparative genomic analysis for functional prediction of YDR119W-A should follow this methodological framework:

• Ortholog identification:

  • Create clusters of orthologs (similar to ScCOGs: S. cerevisiae Clusters of Orthologs) for YDR119W-A across multiple species

  • Apply reciprocal BLAST approaches to confirm orthologous relationships

  • Include at least 500-700 organisms from diverse phyla for robust analysis

• Phylogenetic profiling:

  • Map the presence/absence pattern of YDR119W-A orthologs across species

  • Identify proteins with similar phylogenetic profiles, suggesting functional relationships

• Domain architecture analysis:

  • Identify conserved domains and motifs

  • Compare domain organization across orthologs

• Genomic context analysis:

  • Examine neighboring genes in various species (synteny)

  • Identify conserved gene clusters that might indicate functional relationships

• Integration with high-throughput data:

  • Correlate comparative genomics predictions with expression data

  • Incorporate protein-protein interaction networks

This approach has successfully identified functions for previously uncharacterized yeast proteins and can help determine which organisms would be appropriate models for extrapolating YDR119W-A function .

What CRISPR-Cas9 strategies are most effective for generating YDR119W-A knockout strains?

For generating precise YDR119W-A knockout strains using CRISPR-Cas9, researchers should implement the following methodological approach:

• gRNA design:

  • Select target sequences with minimal off-target effects

  • Design gRNAs targeting the coding region early in the sequence

  • Use yeast-optimized CRISPR-Cas9 systems

• Repair template construction:

  • Design repair templates with 40-60 bp homology arms flanking the target site

  • Include selectable markers (e.g., antibiotic resistance genes)

  • Consider including unique restriction sites for screening

• Transformation and selection protocol:

  • Co-transform gRNA plasmid, Cas9 expression plasmid, and repair template

  • Select transformants on appropriate media

  • Confirm knockouts by PCR, sequencing, and Western blotting

• Phenotypic analysis workflow:

  • Compare growth rates under various conditions

  • Analyze cellular morphology and ultrastructure

  • Perform transcriptome and proteome analysis of knockout strains

This approach allows precise genome editing while minimizing off-target effects, providing a powerful tool for functional characterization of YDR119W-A.

How can quantitative proteomics be applied to study YDR119W-A expression and regulation?

Quantitative proteomics offers powerful approaches for studying YDR119W-A expression and regulation:

• Sample preparation workflow:

  • Culture S. cerevisiae under various conditions

  • Extract proteins using optimized lysis buffers

  • Perform protein digestion (trypsin/Lys-C mix)

• Labeling and fractionation strategies:

  • SILAC labeling for direct comparison of conditions

  • TMT or iTRAQ for multiplexed analysis

  • High-pH reversed-phase fractionation to increase proteome coverage

• Mass spectrometry analysis:

  • Data-dependent acquisition for discovery

  • Parallel reaction monitoring for targeted quantification

  • Data-independent acquisition for comprehensive analysis

• Data analysis pipeline:

  • Protein identification using database search algorithms

  • Quantification using appropriate software tools

  • Statistical analysis to identify significant changes

• Post-translational modification analysis:

  • Phosphorylation site mapping

  • Glycosylation profiling

  • Ubiquitination analysis

These approaches allow researchers to track YDR119W-A abundance changes and identify regulatory mechanisms under different conditions, providing insights into its function and regulation.

How should researchers analyze RNA-seq data to understand YDR119W-A expression patterns?

RNA-seq data analysis for YDR119W-A expression should follow this structured methodological framework:

• Experimental design considerations:

  • Include biological replicates (minimum 3)

  • Sample cells under relevant conditions (stress, developmental stages)

  • Consider time-course experiments to capture expression dynamics

• Quality control measures:

  • Assess raw read quality (FastQC)

  • Filter low-quality reads

  • Remove adapter sequences

• Alignment and quantification protocol:

  • Align reads to the S. cerevisiae reference genome

  • Quantify expression using count-based methods

  • Calculate FPKM/TPM values for normalization

• Differential expression analysis:

  • Apply appropriate statistical methods (DESeq2, edgeR)

  • Control for false discovery rate

  • Validate key findings with qRT-PCR

• Integration with other data types:

  • Correlate with proteomic data

  • Integrate with ChIP-seq to identify regulatory mechanisms

  • Compare with orthologous genes in other species

This systematic approach enables researchers to generate robust insights into YDR119W-A expression patterns and regulatory mechanisms across different conditions.

What statistical approaches should be used to analyze phenotypic data from YDR119W-A mutant strains?

For robust statistical analysis of phenotypic data from YDR119W-A mutant strains, researchers should implement:

• Experimental design optimization:

  • Power analysis to determine appropriate sample sizes

  • Inclusion of appropriate controls (wild-type, other relevant mutants)

  • Randomization and blinding where applicable

• Descriptive statistics:

  • Calculate means, standard errors, and confidence intervals

  • Present data in a format similar to: 44.3±13.8 (17–65) with mean±SD (range)

  • Ensure proper significant figures and units for all measurements

• Inferential statistics:

  • Select appropriate tests based on data distribution (parametric vs. non-parametric)

  • Apply correction for multiple comparisons (e.g., Bonferroni, FDR)

  • Report exact p-values rather than threshold values (e.g., p=0.206 instead of p>0.05)

• Data presentation guidelines:

  • Use well-formatted tables with clear headings and units

  • Create informative figures with properly labeled axes

  • Include sample sizes in all statistical reporting

• Advanced analysis approaches:

  • Multivariate analysis for complex phenotypes

  • Machine learning for pattern recognition

  • Network analysis to contextualize findings

This approach ensures statistical rigor and reproducibility in phenotypic analyses of YDR119W-A mutant strains.

How can researchers effectively visualize YDR119W-A protein structure predictions?

For effective visualization of YDR119W-A protein structure predictions, researchers should follow these methodological guidelines:

• Structure prediction workflow:

  • Generate multiple models using different algorithms (AlphaFold2, RoseTTAFold)

  • Evaluate model quality using established metrics (pLDDT, TM-score)

  • Refine models as needed

• Visualization techniques:

  • Use specialized software (PyMOL, ChimeraX, VMD)

  • Apply appropriate rendering methods for different structural features

  • Create multiple views highlighting key domains and motifs

• Comparative visualization strategies:

  • Superimpose models with known structures of related proteins

  • Create morph animations between conformational states

  • Generate electrostatic surface representations

• Figure preparation for publication:

  • Include scale bars and orientation markers

  • Use consistent color schemes across related figures

  • Provide both cartoon and surface representations

• Interactive visualization options:

  • Prepare interactive figures for online publications

  • Create 3D printable models for educational purposes

  • Develop AR/VR visualizations for complex structural relationships

This comprehensive approach ensures that structural predictions are effectively communicated, facilitating hypothesis generation about YDR119W-A function based on structural features.

What high-throughput screening approaches can identify conditions affecting YDR119W-A function?

For comprehensive identification of conditions affecting YDR119W-A function, researchers should implement:

• Chemical genomics screening protocol:

  • Test YDR119W-A mutant strains against diverse chemical libraries

  • Employ concentration gradients to determine sensitivity/resistance profiles

  • Use automated growth monitoring systems for quantitative assessment

• Environmental perturbation screening:

  • Systematically vary temperature, pH, osmolarity, and carbon sources

  • Monitor growth rates, morphology, and metabolic parameters

  • Employ microfluidic systems for precise environmental control

• Genetic interaction screening:

  • Create double mutant collections using synthetic genetic array technology

  • Quantify genetic interactions (synthetic lethality, epistasis)

  • Construct interaction networks to position YDR119W-A in cellular pathways

• Monitoring approach optimization:

  • Employ fluorescent reporters for real-time phenotypic readouts

  • Use high-content imaging for morphological analysis

  • Implement metabolomic profiling for biochemical phenotypes

• Data analysis and interpretation framework:

  • Apply machine learning for pattern recognition in complex datasets

  • Use clustering methods to group similar phenotypic responses

  • Integrate with existing S. cerevisiae functional networks

This systematic approach allows researchers to comprehensively map conditions affecting YDR119W-A function, providing insights into its cellular roles and regulatory mechanisms.

How can researchers determine if YDR119W-A interacts with specific cellular pathways?

To systematically determine YDR119W-A interactions with specific cellular pathways, researchers should implement:

• Pathway-focused genetic interaction analysis:

  • Generate double mutants with key pathway components

  • Quantify genetic interactions using growth-based assays

  • Calculate interaction scores to identify suppressors/enhancers

• Biochemical pathway analysis:

  • Measure pathway output in YDR119W-A mutants

  • Monitor metabolite levels using targeted metabolomics

  • Assess flux through pathways using isotope labeling

• Pathway reporter assays:

  • Construct fluorescent/luminescent reporters for pathway activity

  • Compare reporter signals in wild-type and YDR119W-A mutants

  • Perform time-course analysis during pathway activation

• Phosphoproteomic analysis:

  • Quantify pathway-specific phosphorylation events

  • Compare phosphorylation patterns between wild-type and mutants

  • Identify altered signaling nodes

• Computational pathway modeling:

  • Integrate experimental data into pathway models

  • Simulate pathway behavior with and without YDR119W-A

  • Test predictions with targeted experiments

This multifaceted approach allows researchers to comprehensively map YDR119W-A's involvement in cellular pathways, similar to how other yeast proteins have been functionally characterized through pathway analysis .

What approaches can determine if YDR119W-A has human orthologs with potential disease relevance?

To systematically identify and characterize potential human orthologs of YDR119W-A with disease relevance, researchers should implement:

• Ortholog identification workflow:

  • Perform reciprocal BLAST searches against human proteome

  • Apply sensitive sequence comparison methods (HMM profiles, PSSM)

  • Consider structural similarities beyond sequence conservation

  • Create clusters of orthologs across species to strengthen predictions

• Functional conservation assessment:

  • Test human candidate genes for complementation in yeast YDR119W-A mutants

  • Compare protein interaction networks between species

  • Assess conservation of post-translational modifications

• Disease association analysis:

  • Search disease mutation databases for variants in candidate orthologs

  • Analyze GWAS data for disease associations

  • Examine expression patterns in disease-relevant tissues

• Experimental validation approaches:

  • Create equivalent mutations in yeast and human cell models

  • Compare phenotypes across species

  • Assess conservation of protein localization and interactions

• Translational potential evaluation:

  • Determine if YDR119W-A can serve as a model for human disease mechanisms

  • Test potential therapeutic approaches in yeast before human studies

  • Assess pathway conservation between yeast and humans

This comprehensive approach leverages the finding that approximately 30% of genes implicated in human disease have orthologs in the yeast proteome , making it a valuable model organism for studying conserved disease mechanisms.

Table: Comparative Analysis of Methods for Studying YDR119W-A Function

What are the most promising future research directions for YDR119W-A characterization?

Several high-potential research directions for YDR119W-A characterization include:

• Integrative multi-omics approaches combining transcriptomics, proteomics, and metabolomics to comprehensively map YDR119W-A's functional impact across cellular systems.

• Evolutionary functional genomics to trace the protein's functional conservation across species, potentially revealing fundamental biological roles preserved through evolution .

• Systems biology modeling to position YDR119W-A within the broader cellular network context, predicting its importance in maintaining cellular homeostasis under various conditions.

• Human disease modeling using YDR119W-A as a platform to understand orthologous human proteins potentially implicated in disease, leveraging the finding that approximately 30% of disease-associated human genes have yeast orthologs .

• Synthetic biology applications exploring the potential utility of YDR119W-A in engineered biological systems, potentially building on S. cerevisiae's established role as a chassis for recombinant protein production .

These directions collectively represent a comprehensive strategy to fully elucidate the biological significance of this uncharacterized protein, moving from basic characterization to potential translational applications.

How should researchers report inconclusive or contradictory findings regarding YDR119W-A?

When reporting inconclusive or contradictory findings about YDR119W-A, researchers should follow these methodological guidelines:

• Transparent data presentation:

  • Present all data, including negative and inconclusive results

  • Use clear tables with appropriate statistical measures

  • Include sample sizes, p-values, and confidence intervals for all comparisons

• Methodological transparency:

  • Detail all experimental conditions and variables

  • Acknowledge limitations of techniques used

  • Provide complete methodological details to enable replication

• Context-appropriate interpretation:

  • Discuss possible reasons for contradictory results

  • Consider biological variability and technical limitations

  • Compare findings with related proteins or orthologs

• Future research recommendations:

  • Suggest alternative approaches to resolve contradictions

  • Propose experiments that could clarify inconclusive results

  • Outline hypothesis-driven research to address uncertainties

This approach ensures scientific integrity while advancing the collective understanding of YDR119W-A, even when initial findings are unclear or seemingly contradictory.

What benchmarks should be used to evaluate successful characterization of YDR119W-A?

Comprehensive benchmarks for evaluating successful YDR119W-A characterization include:

• Functional assignment confidence metrics:

  • Multiple lines of evidence supporting functional predictions

  • Statistical confidence measures for predicted functions

  • Experimental validation of key predictions

• Comparative characterization standards:

  • Determination of conservation and divergence across species

  • Establishment of orthology relationships and functional equivalence

  • Positioning within evolutionary protein families

• Interaction network quality assessment:

  • Confidence scores for protein-protein interactions

  • Validation through multiple orthogonal methods

  • Integration with existing interaction networks

• Phenotypic characterization completeness:

  • Comprehensive assessment across diverse conditions

  • Quantitative phenotypic measurements with statistical rigor

  • Connection of phenotypes to molecular mechanisms

• Integration with existing knowledge:

  • Consistency with known pathway components

  • Resolution of contradictions with existing literature

  • Contribution to broader understanding of cellular processes