Recombinant Saccharomyces cerevisiae Putative uncharacterized protein YLR140W (YLR140W)

What is YLR140W and where is it located in the S. cerevisiae genome?

YLR140W is a gene locus in the Saccharomyces cerevisiae genome from laboratory strain S288C. It is designated as a putative uncharacterized protein, meaning its precise function has not been fully determined. The systematic name "YLR140W" indicates its chromosomal location: Y (yeast), L (chromosome 12), R (right arm), 140 (relative position), and W (Watson strand orientation) . The gene is maintained in the Saccharomyces Genome Database (SGD) where researchers can access its sequence data, genomic context, and coordinates .

What approaches are recommended for initial characterization of YLR140W?

For initial characterization of YLR140W, a multi-faceted approach is recommended:

• Sequence analysis: Perform BLAST searches against fungi and other organisms to identify potential homologs and conserved domains.

• Protein-protein interaction studies: Examine interaction networks to identify functional relationships with characterized proteins .

• Gene expression analysis: Measure expression levels under various conditions to identify patterns that may suggest function.

• Phenotypic screening: Create knockout or overexpression strains and subject them to various growth conditions to observe phenotypic effects.

Researchers should begin with bioinformatic analyses using tools available through SGD, followed by experimental validation of predictions generated through computational approaches .

What computational tools are most effective for predicting the function of YLR140W?

When predicting functions of uncharacterized proteins like YLR140W, researchers should employ multiple computational approaches:

As shown in research examples, proteins that don't share functional annotations with direct interaction neighbors may share annotations with indirect (level-2) neighbors, suggesting that interaction network context is valuable for functional prediction .

What are the most effective methods for cloning and expressing recombinant YLR140W protein?

For successful cloning and expression of YLR140W, researchers should consider the following methodology:

• Gene amplification: Design primers to amplify the YLR140W gene (1044 bp) via PCR, similar to strategies used for other yeast genes like ari1 .

• Vector selection: For expression in yeast, vectors with strong constitutive promoters like pGAPZαC have proven effective for similar proteins .

• Transformation strategy: Standard lithium acetate transformation can be used for S. cerevisiae.

• Expression verification: Western blot with anti-His tag antibodies can confirm successful expression if a His-tag is incorporated into the construct .

The complete procedure should include sequence verification after cloning to ensure no mutations were introduced during PCR amplification, especially avoiding changes to potential active sites or binding domains .

How can I design a robust experimental system to study YLR140W function in vivo?

A comprehensive experimental system for studying YLR140W function should include:

• Strain construction:

  • Generate knockout strains (ΔYLR140W)

  • Create overexpression strains (using constitutive promoters)

  • Develop tagged versions (GFP/HA/FLAG) for localization and pull-down studies

• Phenotypic analysis:

  • Growth curve analysis under various stress conditions

  • Compare doubling time and lag phase similar to studies done with ari1-overexpressed strains

  • Test resistance to specific stressors (oxidative, osmotic, temperature)

• Transcriptional response:

  • Real-time PCR to measure expression levels under different conditions

  • RNA-seq to identify genes affected by YLR140W deletion/overexpression

• Interaction studies:

  • Co-immunoprecipitation with potential interaction partners

  • Yeast two-hybrid screening to identify direct protein interactions

This multi-layered approach allows for triangulation of function through complementary methodologies, increasing confidence in functional assignments .

What control experiments are essential when studying YLR140W?

When investigating YLR140W, include these critical controls:

Additionally, when performing Western blots for protein expression verification, include positive controls with known His-tagged proteins of similar size for reference .

How can protein-protein interaction networks be used to predict the function of YLR140W?

Protein-protein interaction (PPI) networks provide valuable insights into the function of uncharacterized proteins like YLR140W through association analysis:

• Network proximity analysis: Research shows that proteins with overlapping interaction neighborhoods often share functional similarities, even when they don't directly interact .

• Indirect neighbor analysis: As demonstrated in Figure 1 from source , YLR140W appears in a network where proteins without direct functional annotation sharing may share annotations with level-2 (indirect) neighbors.

• Implementation methodology:

  • Map all direct interaction partners of YLR140W

  • Extend to second-level interactions (partners of partners)

  • Calculate functional similarity scores between YLR140W and network proteins

  • Apply filtering to exclude functionally unrelated neighbors

  • Identify enriched functions in the remaining network

• Limitations: The predictive power tends to diminish with interaction distance, and errors in lower-level interactions propagate to higher levels .

For optimal results, researchers should combine network-based function prediction with experimental validation, as network data may contain both false positives and negatives that affect prediction accuracy.

What techniques are most effective for resolving contradictions in experimental data regarding YLR140W?

When facing contradictory experimental data about YLR140W function, researchers should employ systematic contradiction resolution approaches:

• Design of Experiments (DOE) approach: Systematically explore the parameter space to identify factors contributing to contradictory results .

• Multiphysics modeling: Formulate models that capture the complexity of the system and inter-relationships between design parameters, rather than linear cause-analysis that may miss system complexity .

• Contradiction analysis framework:

  • Identify the parameters involved in the contradiction

  • Determine the weight of each parameter on experimental outcomes

  • Formulate the priority contradiction to focus resolution efforts

  • Apply TRIZ principles to find solution concepts that resolve contradictions

• Practical implementation:

  • Repeat experiments under strictly controlled conditions

  • Vary one parameter at a time to isolate effects

  • Consider strain background differences that may affect outcomes

  • Account for genetic variations in YLR140W between laboratory strains

As shown in research on contradiction resolution, both experimental and modeling approaches can lead to similar results in identifying priority contradictions, providing complementary methods to resolve complex research problems .

How can CRISPR-Cas9 gene editing be optimized for studying YLR140W function?

CRISPR-Cas9 provides powerful tools for precise genetic manipulation of YLR140W in S. cerevisiae:

• gRNA design considerations:

  • Select target sites with minimal off-target effects

  • Design multiple gRNAs targeting different regions of YLR140W

  • Verify specificity against the S288C reference genome

• Editing strategies:

  • Gene knockout: Complete deletion of YLR140W

  • Point mutations: Introduce specific amino acid changes to study structure-function relationships

  • Promoter modifications: Alter expression levels without changing protein sequence

  • Tagging: Add fluorescent proteins or epitope tags for localization and pulldown studies

• Delivery method optimization:

  • Transformation efficiency comparison for various methods:

  Method	Efficiency	Advantages	Limitations
  Lithium acetate	Moderate	Simple, inexpensive	Variable efficiency
  Electroporation	High	Consistent results	Equipment required
  Biolistic	Variable	Works with difficult strains	Specialized equipment

• Verification protocols:

  • PCR verification of genomic modifications

  • Sequencing to confirm precise edits

  • Functional assays to validate phenotypic effects

When implementing CRISPR-Cas9 editing, researchers should first validate the system using known genes before applying it to YLR140W to ensure technical proficiency with the method.

How should growth curves of wild-type versus YLR140W-modified S. cerevisiae strains be analyzed?

Growth curve analysis requires systematic quantification approaches:

• Key metrics to extract:

  • Lag phase duration

  • Exponential growth rate (doubling time)

  • Maximum cell density

  • Area under curve (AUC)

• Statistical analysis framework:

  • Compare growth parameters using appropriate statistical tests (t-test for simple comparisons, ANOVA for multiple conditions)

  • Calculate confidence intervals to assess variability

  • Perform regression analysis to fit growth models

• Stress response assessment:

  • Based on methodologies used for ari1-overexpressed strains, compare wild-type and YLR140W-modified strains under various stress conditions

  • Quantify relative growth inhibition under each condition

  • Calculate EC50 values for stress compounds

• Interpretive considerations:

  • Distinguish between growth defects and adaptive responses

  • Correlate growth parameters with gene expression data

  • Consider strain background effects on growth characteristics

As demonstrated in studies with other S. cerevisiae genes, even genes with similar functions may show different growth patterns under specific stress conditions, highlighting the importance of testing multiple stressors .

What bioinformatics approaches can reveal potential functions of YLR140W?

A comprehensive bioinformatics workflow for functional prediction includes:

• Sequence-based analysis:

  • Homology detection using PSI-BLAST and HHpred

  • Identification of conserved domains and motifs

  • Detection of catalytic residues or binding sites

  • Prediction of post-translational modifications

• Structural bioinformatics:

  • 3D structure prediction

  • Binding pocket identification

  • Virtual ligand screening

  • Structure-based function inference

• Systems biology integration:

  • Co-expression network analysis

  • Protein-protein interaction mapping

  • Metabolic pathway positioning

  • Gene neighborhood conservation across species

• Machine learning applications:

  • Function prediction from sequence features

  • Classification based on multiple data types

  • Deep learning for pattern recognition in complex datasets

The protein-protein interaction network approach has been specifically demonstrated for proteins like YLR140W, showing that functional relationships can be detected even when direct interactions don't provide clear functional insights .

How can mass spectrometry data be optimized for identification and characterization of YLR140W protein?

Mass spectrometry (MS) analysis of YLR140W requires careful optimization:

• Sample preparation strategies:

  • Enrichment methods for low-abundance proteins

  • Optimization of digestion protocols for optimal peptide coverage

  • Fractionation approaches to reduce sample complexity

• MS method development:

  • Selection of ionization methods (ESI vs. MALDI)

  • Optimization of fragmentation parameters

  • Development of targeted methods for YLR140W-specific peptides

• Data analysis pipeline:

  • Database selection and search parameters

  • False discovery rate control

  • Peptide validation criteria

  • Protein inference algorithms

• Post-translational modification analysis:

  • Enrichment strategies for modified peptides

  • Neutral loss scanning for specific modifications

  • Site localization algorithms

  • Quantification of modification stoichiometry

When analyzing YLR140W, which has a predicted mass of approximately 14.2 kDa (based on similar yeast proteins) , researchers should account for possible post-translational modifications that may affect molecular weight and chromatographic behavior.

How does the study of YLR140W contribute to our understanding of uncharacterized proteins in S. cerevisiae?

The investigation of YLR140W contributes to broader understanding of uncharacterized yeast proteins through:

• Systematic functional genomics integration:

  • YLR140W represents one of many putative uncharacterized proteins in yeast

  • Methodologies developed for YLR140W characterization can be applied to other uncharacterized proteins

  • Findings may reveal common patterns in function assignment challenges

• Model system advantages:

  • S. cerevisiae provides a well-characterized genetic background for study

  • The complete genome sequence and extensive genetic tools facilitate comprehensive analysis

  • Findings in yeast often translate to higher eukaryotes

• Functional category expansion:

  • Characterization may reveal novel functional categories or expand existing ones

  • Network analysis approaches demonstrated with proteins like YLR140W help improve function prediction algorithms

  • Integration of multiple data types enables more confident function assignment

• Methodological advancement:

  • Technical approaches developed for YLR140W contribute to the toolbox for studying other uncharacterized proteins

  • Contradiction resolution strategies provide frameworks for addressing complex experimental challenges

The systematic study of uncharacterized proteins like YLR140W fills gaps in our understanding of cellular systems and potentially reveals new biological principles and functional relationships.

What are the most promising applications of YLR140W research in biotechnology?

While avoiding commercial focus, several academic biotechnology applications emerge from YLR140W research:

• Strain engineering applications:

  • Development of stress-resistant yeast strains for research

  • Creation of biosensor systems using YLR140W-based reporters

  • Engineering of metabolic pathways that may involve YLR140W or related proteins

• Protein engineering opportunities:

  • Structure-function studies to develop proteins with novel properties

  • Scaffold development for enzyme immobilization

  • Chimeric protein design incorporating YLR140W domains

• Research tool development:

  • Genetic reporter systems

  • Protein interaction detection methods

  • Cellular localization markers

• Methodological advances:

  • Optimization of recombinant protein expression systems

  • Development of purification strategies for challenging proteins

  • Improvement of functional annotation algorithms

Similar to research conducted with aldehyde reductase (encoded by ari1), studies of YLR140W could potentially reveal stress response mechanisms or detoxification pathways that have broader implications for fundamental research .