Recombinant Saccharomyces cerevisiae Putative uncharacterized protein YGR137W (YGR137W)

How can researchers determine the subcellular localization of YGR137W?

Determining subcellular localization requires a multi-faceted approach:

• Fluorescent protein tagging: Fusion of YGR137W with GFP variants such as GFPdeg (which is rapidly degraded in the cytoplasm but protected in organelles) can reveal localization. This approach has successfully identified uncharacterized proteins potentially localized to mitochondria (UPMs) in yeast .

• Bioinformatic prediction tools: Tools like DeepLoc-1.0 can predict protein localization based on sequence characteristics. For mitochondrial localization specifically, researchers should check for N-terminal mitochondrial localization signals .

• Subcellular fractionation: Physical separation of cellular compartments followed by Western blot analysis can confirm the presence of YGR137W in specific organelles.

• Immunolocalization: Using antibodies against YGR137W or epitope tags in fixed cells can visually confirm localization patterns.

• Correlation with expression patterns: Analysis of expression during specific cellular states (e.g., post-diauxic shift when mitochondria develop) may provide functional clues .

What approaches should be taken to infer the function of an uncharacterized protein like YGR137W?

Function inference requires multiple complementary approaches:

• Network-based function prediction: Analyze protein-protein interaction networks to predict function based on interaction partners. This approach correctly predicts functional categories for 72% of characterized proteins with at least one partner of known function .

• Gene deletion phenotypic analysis: Systematic evaluation of growth rates, stress responses, and metabolic profiles in YGR137W deletion strains across various conditions.

• Expression correlation analysis: Identifying genes with similar expression patterns under various conditions may reveal functional relationships.

• Evolutionary profiling: Examining the presence/absence pattern of YGR137W across species can provide functional clues.

• Structure prediction and domain analysis: Computational analysis of predicted structural features and conserved domains.

• Integration of multi-omics data: Combining transcriptome, proteome, and metabolome data to place YGR137W in biological context .

What are the optimal expression systems for producing recombinant YGR137W in S. cerevisiae?

Optimal expression of YGR137W requires careful selection of expression components:

• Vector selection:

  • For stable expression: Integration plasmids (YIp) that integrate into the yeast genome

  • For high-copy expression: Episomal plasmids (YEp) based on the 2μ origin (5-30 copies)

  • For moderate expression: Centromeric plasmids (YCp) based on ARS-CEN elements (1-2 copies)

• Promoter options:

  • Constitutive strong promoters: TEF1 and GPD (TDH3) for constant high-level expression

  • Inducible promoters: GAL1/10 for galactose induction or MET25 for methionine repression

• Secretion signal sequences:

  • The α-mating factor prepro-sequence for efficient secretion

  • Engineering signal peptides for better recognition by S. cerevisiae secretory machinery

• Codon optimization:

  • Adjust codon usage to match S. cerevisiae preferences, especially considering the bias in S. cerevisiae towards certain codons

• Expression strain selection:

  • Protease-deficient strains (e.g., pep4Δ) to reduce proteolytic degradation

  • Strains with enhanced secretory capacity for improved yields

How can researchers enhance secretion and reduce hyperglycosylation of YGR137W in yeast expression systems?

Enhancing secretion while managing hyperglycosylation requires specific strategies:

• Managing hyperglycosylation:

  • Directed evolution approaches targeting residues that affect glycosylation sites, which has been shown to reduce glycosylation degrees below 10% in peroxidases and laccases

  • Mutations that reduce residence time in the Golgi apparatus can decrease hyperglycosylation

  • Expression in glycosylation-deficient strains (e.g., och1Δ mutants)

• Enhancing secretion:

  • Introduction of random mutations in processing regions of the native gene to adapt to S. cerevisiae proteases

  • Engineering KEX2 Golgi protease cleavage sites, which has been shown to enhance secretion up to 10-fold in some cases

  • Optimizing the C-terminal tail, which can significantly impact processing efficiency

• Signal peptide optimization:

  • Replace the native signal peptide with alternatives better recognized by S. cerevisiae

  • Test multiple signal peptides as their efficiency can be protein-specific

• Co-expression strategies:

  • Co-express chaperones to assist protein folding

  • Optimize expression levels to prevent ER stress response activation

What analytical methods should be used to assess the quality and characteristics of purified YGR137W?

Comprehensive characterization requires multiple analytical approaches:

• Purity assessment:

  • SDS-PAGE with Coomassie staining or silver staining

  • Size exclusion chromatography (SEC)

  • Capillary electrophoresis

• Glycosylation analysis:

  • PNGase F or Endo H treatment followed by mobility shift analysis

  • Mass spectrometry to identify glycosylation sites and patterns

  • Lectin blotting to characterize glycan structures

• Structural characterization:

  • Circular dichroism (CD) spectroscopy for secondary structure analysis

  • Differential scanning calorimetry (DSC) for thermal stability

  • Limited proteolysis to identify stable domains

• Functional assays:

  • If function is unknown, activity screening against various substrates

  • Binding assays with potential interaction partners identified through bioinformatics

  • Comparison with orthologous proteins from related species

• Mass spectrometry:

  • Intact mass analysis to confirm expression of full-length protein

  • Peptide mapping for sequence coverage confirmation

  • Post-translational modification identification

How can researchers design deletion studies to characterize the function of YGR137W?

Systematic deletion studies require careful experimental design:

• Creating precise deletion constructs:

  • Complete ORF removal while preserving regulatory elements

  • Use of marker cassettes with loxP sites for marker recycling

  • Construction of conditional alleles for essential genes

• Phenotypic screening approach:

  • Systematic phenotyping under multiple growth conditions (carbon sources, temperatures, stressors)

  • High-throughput fitness profiling in the presence of various chemicals

  • Analysis of chronological lifespan (CLS) and replicative lifespan (RLS), as YGR137W deletion has been shown to increase both CLS and RLS in previous studies

• Molecular phenotyping:

  • Transcriptome analysis to identify affected pathways

  • Metabolomic profiling to detect metabolic alterations

  • Systematic genetic interaction mapping (e.g., synthetic genetic array analysis)

• Control considerations:

  • Multiple independent deletion strains to account for genetic background effects

  • Complementation tests with the wild-type gene to confirm phenotype causality

  • Analysis in different strain backgrounds (e.g., BY4743 and CEN.PK) to ensure robustness

What approaches can identify potential protein-protein interactions involving YGR137W?

Multiple complementary methods should be employed:

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

  • Tagging YGR137W with affinity tags (e.g., TAP, FLAG, HA)

  • Gentle cell lysis to preserve native complexes

  • Quantitative MS analysis with appropriate controls to filter non-specific interactions

• Yeast two-hybrid screening:

  • Library screening approaches using YGR137W as bait

  • Targeted Y2H with suspected interaction partners

  • Verification using reverse Y2H configurations

• Proximity labeling approaches:

  • BioID or APEX2 fusions to YGR137W to identify proximal proteins

  • Controlled expression to minimize artifacts

  • Analysis under different growth conditions

• Co-localization studies:

  • Dual-fluorescent tagging of YGR137W and potential partners

  • Live-cell imaging to capture dynamic interactions

  • FRET or BiFC to confirm direct interactions

• Network inference from genomic data:

  • Integration with existing protein interaction networks

  • Analysis of genetic interaction profiles for functional relationships

  • Correlation analysis across large-scale datasets

How should researchers investigate potential mitochondrial functions of YGR137W?

Given previous findings of uncharacterized mitochondrial proteins in yeast, comprehensive investigation requires:

• Mitochondrial phenotype analysis:

  • Respiratory growth assessment on non-fermentable carbon sources (e.g., glycerol)

  • Measurement of oxygen consumption rates

  • Mitochondrial membrane potential assessment using fluorescent dyes

  • Analysis of reactive oxygen species (ROS) levels, which have been shown to be altered in some uncharacterized protein deletions

• Mitochondrial DNA maintenance:

  • qRT-PCR analysis of mitochondrial DNA copy number

  • Assessment of mitochondrial gene expression (e.g., ATP6, COX3)

  • Analysis of mitochondrial translation efficiency

• Mitochondrial morphology:

  • Fluorescence microscopy using mitochondrial markers

  • Electron microscopy for ultrastructural analysis

  • Time-lapse imaging to assess dynamics

• Biochemical approaches:

  • Submitochondrial fractionation to determine precise localization

  • In organello import assays to confirm mitochondrial targeting

  • Analysis of mitochondrial enzymatic activities

• Gene expression analysis:

  • Examination of expression patterns during post-diauxic shift when mitochondria develop

  • Response to TORC1 inhibition (e.g., rapamycin treatment)

How can directed evolution techniques be applied to study YGR137W function?

S. cerevisiae offers powerful directed evolution capabilities for YGR137W studies:

• In vivo DNA recombination strategies:

  • Utilize S. cerevisiae's high frequency of homologous DNA recombination for library creation

  • Apply in vivo Assembly of Mutant libraries (IvAM) to combine multiple mutation approaches

  • Engineer overlapping regions of approximately 40 bp for efficient recombination

• Mutagenesis approaches:

  • Error-prone PCR with polymerases of different biases to create diverse libraries

  • In vivo Overlap Extension (IVOE) for site-directed mutagenesis studies

  • DNA shuffling techniques taking advantage of S. cerevisiae's recombination machinery

• Selection strategies:

  • Design functional screens based on predicted activities

  • Growth-based selections under specific stress conditions

  • Fluorescence-activated cell sorting (FACS) for variants with desired properties

• Screening methodology:

  • High-throughput plate-based assays

  • Secretion-based screening leveraging S. cerevisiae's secretory machinery

  • Coupling with reporter systems for indirect activity measurement

• Analytical considerations:

  • Deep sequencing of selected populations

  • Structural analysis of beneficial mutations

  • Epistasis mapping among multiple mutations

What computational approaches can help predict the function of YGR137W?

Modern computational biology offers multiple prediction strategies:

• Sequence-based approaches:

  • Remote homology detection using sensitive methods like HHpred

  • Analysis of conserved sequence motifs and domains

  • Sequence architecture analysis with tools like ANNOTATOR, which has identified intrinsically unstructured regions and repeat sequences in similar proteins

• Structure prediction tools:

  • AlphaFold2 or RoseTTAFold for tertiary structure prediction

  • Analysis of predicted binding pockets and catalytic sites

  • Molecular dynamics simulations to identify functional states

• Network-based inference:

  • Guilt-by-association analysis in protein-protein interaction networks

  • Pathway enrichment analysis of correlated genes

  • Construction of regulatory network models using TF binding data

• Integrative approaches:

  • Bayesian integration of multiple data types

  • Machine learning models trained on characterized proteins

  • Evolutionary coupling analysis for co-evolving residues

• Comparative genomics:

  • Phylogenetic profiling across yeast species

  • Synteny analysis to identify conserved genomic context

  • Analysis of selection pressure on different protein regions

How can researchers integrate multi-omics data to infer the function of YGR137W?

Comprehensive multi-omics integration requires sophisticated approaches:

• Data collection strategy:

  • Transcriptome profiling of YGR137W deletion vs. wild-type under multiple conditions

  • Proteome analysis focusing on changes in abundance and post-translational modifications

  • Metabolome analysis to identify altered metabolic pathways

  • Genetic interaction mapping through systematic double mutant generation

• Integration methodologies:

  • Construction of probabilistic networks incorporating diverse data types

  • Machine learning approaches for pattern recognition across datasets

  • Pathway and network analysis to identify enriched biological processes

• Validation approaches:

  • Targeted experiments to test predictions from integrated analyses

  • Cross-validation using independent datasets

  • Literature-based validation of predicted functional relationships

• Analytical frameworks:

  • Bayesian network models for causal inference

  • Matrix factorization techniques for dimensionality reduction

  • Graph-based algorithms for network module detection

• Experimental design considerations:

  • Carefully designed matrix of conditions to maximize information content

  • Inclusion of time-course data to capture dynamic responses

  • Appropriate replication to ensure statistical power

What are common challenges in expressing YGR137W in S. cerevisiae and how can they be overcome?

Expression troubleshooting requires systematic problem-solving:

• Low expression levels:

  • Try different promoter strengths and induction conditions

  • Optimize codon usage for S. cerevisiae preferences

  • Consider chromosomal integration for stable expression

  • Test different growth media and conditions

• Protein misfolding:

  • Co-express molecular chaperones to assist folding

  • Lower expression temperature to slow folding kinetics

  • Create fusion proteins with solubility-enhancing tags

  • Try different strain backgrounds with varied folding capacities

• Proteolytic degradation:

  • Use protease-deficient strains (e.g., pep4Δ)

  • Add protease inhibitors during extraction

  • Optimize harvest timing to minimize exposure to proteases

  • Design constructs to remove protease-sensitive regions

• Secretion bottlenecks:

  • Test multiple signal sequences for improved secretion

  • Engineer KEX2 protease cleavage sites for enhanced processing

  • Develop strains with enhanced secretory capacity

  • Optimize culture conditions to reduce cell stress

• Hyperglycosylation:

  • Use glycosylation-deficient strains

  • Apply directed evolution to reduce glycosylation as previously demonstrated for other proteins

  • Site-directed mutagenesis of potential glycosylation sites

How can researchers troubleshoot issues with YGR137W purification?

Purification troubleshooting requires multiple strategies:

• Solubility problems:

  • Screen multiple extraction buffers varying pH, salt, and detergents

  • Test mild solubilization agents like sarkosyl or non-ionic detergents

  • Use fusion tags known to enhance solubility (e.g., MBP, SUMO)

  • Consider on-column refolding approaches

• Low binding to affinity resins:

  • Test alternative tag positions (N-terminal vs. C-terminal)

  • Optimize binding conditions (buffer composition, temperature, flow rate)

  • Try different affinity tags (His, FLAG, GST) that may perform differently

  • Include additives to reduce non-specific interactions

• Contamination with host proteins:

  • Implement multi-step purification strategies

  • Use more stringent washing conditions

  • Consider ion exchange or hydrophobic interaction chromatography as additional steps

  • Validate with mass spectrometry to identify persistent contaminants

• Protein instability:

  • Include stabilizing agents (glycerol, arginine, trehalose)

  • Determine optimal pH and ionic conditions

  • Store at appropriate temperature with cryoprotectants

  • Aliquot to avoid freeze-thaw cycles

• Aggregation during concentration:

  • Use gentle concentration methods (e.g., dialysis against PEG)

  • Add solubilizing agents during concentration

  • Determine concentration limits before aggregation occurs

  • Consider alternative buffer systems

What controls and validation steps are essential when studying an uncharacterized protein like YGR137W?

Rigorous validation requires comprehensive controls:

• Expression validation:

  • Western blotting with tag-specific and, if available, protein-specific antibodies

  • Mass spectrometry confirmation of protein identity

  • qRT-PCR to verify transcript levels

  • Use wild-type cells as negative controls for tagged proteins

• Localization controls:

  • Include well-characterized proteins with known localizations as reference markers

  • Verify with multiple tagging approaches (N-terminal, C-terminal, internal)

  • Use fractionation approaches to complement microscopy data

  • Test localization under multiple conditions to detect potential dynamics

• Functional assays:

  • Include both positive and negative controls for each assay

  • Perform dose-response analyses to establish specificity

  • Use multiple independent methods to verify key findings

  • Perform rescue experiments with wild-type protein

• Phenotypic analysis:

  • Test multiple independently derived deletion strains

  • Complement deletions with plasmid-borne wild-type gene

  • Compare phenotypes across different strain backgrounds

  • Use appropriate reference strains with similar characteristics

• Interaction studies:

  • Include non-specific binding controls (e.g., unrelated proteins with same tag)

  • Verify key interactions with multiple methods

  • Test interactions under native expression levels

  • Apply quantitative filtering to remove non-specific interactions

YGR137W Basic Information Table

Common Yeast Expression Promoters for Recombinant Protein Production

Data compiled from source

Yeast Vector Types for Recombinant Protein Expression