Recombinant Saccharomyces cerevisiae Putative UPF0479 protein YNL339W-B (YNL339W-B)

What is Saccharomyces cerevisiae Putative UPF0479 protein YNL339W-B and why is it significant?

Saccharomyces cerevisiae Putative UPF0479 protein YNL339W-B (YNL339W-B) is a 160-amino acid protein encoded by the YNL339W-B gene in Saccharomyces cerevisiae (baker's yeast). The term "putative" indicates that while the protein has been identified through genomic analysis, its precise function remains to be fully characterized experimentally. The protein belongs to the UPF0479 family, a group of uncharacterized proteins with conserved sequences across various organisms. The significance of YNL339W-B lies in its potential role in fundamental cellular processes within S. cerevisiae, which has extensive applications as a model eukaryotic organism in molecular biology, genetics, and biotechnology research .

Why is Saccharomyces cerevisiae commonly used as a model organism in protein research?

Saccharomyces cerevisiae serves as an exceptional model organism for protein research due to several key advantages. First, it possesses a fully sequenced genome that has been extensively annotated, facilitating comprehensive genetic manipulation and analysis. Second, as a eukaryotic organism, S. cerevisiae contains cellular machinery and signaling pathways that share significant homology with higher eukaryotes, including humans, making it valuable for understanding conserved biological processes. Third, S. cerevisiae has an extensive history of safe use in various industries, including food processing, which simplifies laboratory handling without stringent biosafety requirements .

Additionally, S. cerevisiae grows rapidly with a doubling time of approximately 90 minutes under optimal conditions, enabling efficient experimental cycles. The organism is amenable to various genetic manipulation techniques, including homologous recombination, CRISPR-Cas9 editing, and plasmid transformation. These characteristics collectively position S. cerevisiae as an ideal platform for studying proteins like YNL339W-B within their native cellular context and for producing recombinant proteins for further analysis .

What are the optimal conditions for expressing recombinant YNL339W-B protein?

The optimal conditions for expressing recombinant YNL339W-B protein depend on the expression system chosen. Based on available data, the following methodological approach is recommended:

Expression in E. coli system:

• Host strain: BL21(DE3) or Rosetta(DE3) for efficient expression of eukaryotic proteins

• Vector system: pET vector with N-terminal His-tag for purification convenience

• Induction parameters: 0.5 mM IPTG at OD600 = 0.6-0.8

• Post-induction temperature: 18°C for 16-20 hours to enhance proper folding

• Growth medium: LB or 2×YT supplemented with appropriate antibiotics

The expression of YNL339W-B has been successfully demonstrated using E. coli as the expression host with an N-terminal His-tag fusion, resulting in adequate protein yields for structural and functional studies .

For native expression in S. cerevisiae, consider:

• Strain selection: BY4741 or W303 laboratory strains

• Vector system: pYES2 for galactose-inducible expression

• Growth conditions: SC-URA medium with 2% raffinose, induction with 2% galactose

• Incubation: 30°C with shaking at 200 rpm

Selection of the appropriate expression system should be guided by the specific experimental objectives and downstream applications.

What is the recommended purification protocol for recombinant His-tagged YNL339W-B protein?

The purification of recombinant His-tagged YNL339W-B protein can be achieved through the following optimized protocol:

• Cell lysis:

  • Resuspend cell pellet in lysis buffer (50 mM Tris-HCl pH 8.0, 300 mM NaCl, 10 mM imidazole, 1 mM PMSF, protease inhibitor cocktail)

  • Sonicate on ice (6 cycles of 30 seconds on/30 seconds off)

  • Centrifuge at 15,000 × g for 30 minutes at 4°C to remove cell debris

• Immobilized Metal Affinity Chromatography (IMAC):

  • Load clarified lysate onto a Ni-NTA column pre-equilibrated with binding buffer (50 mM Tris-HCl pH 8.0, 300 mM NaCl, 10 mM imidazole)

  • Wash extensively with washing buffer (50 mM Tris-HCl pH 8.0, 300 mM NaCl, 20 mM imidazole)

  • Elute protein with elution buffer (50 mM Tris-HCl pH 8.0, 300 mM NaCl, 250 mM imidazole)

• Size Exclusion Chromatography:

  • Further purify the eluted protein using a Superdex 75 column

  • Use buffer containing 20 mM Tris-HCl pH 8.0, 150 mM NaCl

• Buffer Exchange and Storage:

  • Exchange into storage buffer (20 mM Tris-HCl pH 8.0, 150 mM NaCl, 5% glycerol)

  • Flash-freeze aliquots in liquid nitrogen and store at -80°C

The purified protein should be assessed for purity using SDS-PAGE (>90% purity is typically achievable), and concentration can be determined using the Bradford assay or absorbance at 280 nm with the calculated extinction coefficient .

How should purified YNL339W-B protein be stored to maintain stability and activity?

Proper storage of purified YNL339W-B protein is critical for maintaining its stability and functional integrity. Based on established protocols for similar proteins, the following guidelines are recommended:

• Short-term storage (1-2 weeks):

  • Store at 4°C in storage buffer (20 mM Tris-HCl pH 8.0, 150 mM NaCl with 6% trehalose)

  • Avoid repeated freeze-thaw cycles, which can lead to protein denaturation

• Long-term storage:

  • Aliquot the protein solution in small volumes (50-100 μl) to avoid repeated freeze-thaw cycles

  • Add glycerol to a final concentration of 5-50% (optimally 50%)

  • Flash-freeze in liquid nitrogen and store at -80°C

  • Alternatively, lyophilize the protein in the presence of appropriate stabilizers (e.g., trehalose)

• Reconstitution of lyophilized protein:

  • Briefly centrifuge the vial before opening to ensure all material is at the bottom

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

  • Allow complete rehydration before use

• Stability assessment:

  • Regularly check protein integrity by SDS-PAGE

  • Monitor activity using appropriate functional assays

  • Assess aggregation status by dynamic light scattering if available

These storage conditions should help maintain the structural integrity and functional properties of the purified YNL339W-B protein for experimental use .

How can I design experiments to determine the function of YNL339W-B protein?

Determining the function of an uncharacterized protein like YNL339W-B requires a multi-faceted experimental approach. The following methodological framework is recommended:

• Bioinformatic Analysis:

  • Perform sequence homology searches using BLAST against protein databases

  • Conduct domain prediction using tools like Pfam, SMART, or InterPro

  • Analyze secondary structure using PSIPRED or JPred

  • Predict subcellular localization using tools like DeepLoc or YLoc

  • Generate structural models using AlphaFold or similar tools

• Gene Deletion/Overexpression Studies:

  • Create YNL339W-B knockout strains using CRISPR-Cas9 or homologous recombination

  • Generate YNL339W-B overexpression strains using galactose-inducible promoters

  • Assess phenotypic changes under various growth conditions (temperature, pH, carbon sources, stress)

  • Conduct growth curve analysis to detect subtle growth defects

  • Perform metabolomic profiling to identify affected metabolic pathways

• Protein Localization:

  • Create GFP or mCherry fusion constructs with YNL339W-B

  • Visualize subcellular localization using fluorescence microscopy

  • Perform co-localization studies with known organelle markers

  • Conduct fractionation studies followed by Western blotting

• Interaction Partners:

  • Perform tandem affinity purification (TAP) to identify protein complexes

  • Conduct yeast two-hybrid screening

  • Perform co-immunoprecipitation with tagged YNL339W-B

  • Use BioID or APEX proximity labeling to identify neighboring proteins

• Transcriptome Analysis:

  • Compare RNA-seq data between wild-type and YNL339W-B knockout strains

  • Identify differentially expressed genes in response to YNL339W-B perturbation

  • Perform Gene Ontology enrichment analysis on affected pathways

This integrated approach enables researchers to generate multiple lines of evidence regarding YNL339W-B function, providing a comprehensive understanding of this putative protein's role in cellular processes .

What controls should be included when studying the effects of YNL339W-B gene deletion?

When designing experiments to study the effects of YNL339W-B gene deletion, rigorous controls are essential to ensure reliable and interpretable results. The following controls should be included:

• Strain Controls:

  • Wild-type parental strain (unmodified S. cerevisiae with the same genetic background)

  • Empty vector control (for complementation studies)

  • Deletion of a non-essential gene unrelated to YNL339W-B

  • Positive control (deletion of a gene with known phenotype)

• Genetic Validation Controls:

  • PCR verification of the YNL339W-B deletion

  • RT-qPCR to confirm absence of YNL339W-B transcription

  • Western blot to confirm absence of YNL339W-B protein expression

  • Complementation with wild-type YNL339W-B to rescue phenotype

• Growth Condition Controls:

  • Multiple replicate cultures (minimum n=3)

  • Various growth media compositions (minimal, rich, different carbon sources)

  • Range of environmental stressors (oxidative, osmotic, temperature)

  • Multiple time points for temporal analysis

• Phenotypic Assay Controls:

  • Standard curves for quantitative assays

  • Technical replicates for each measurement

  • Biological replicates from independent transformations

  • Blind scoring/analysis where applicable

• Data Analysis Controls:

  • Appropriate statistical tests with correction for multiple comparisons

  • Normalization to reference genes/proteins as applicable

  • Inclusion of experimental metadata (strain backgrounds, growth conditions)

The implementation of these controls ensures that any observed phenotypic differences can be confidently attributed to the YNL339W-B deletion rather than experimental artifacts or secondary genetic effects .

How can I design experiments to study potential protein-protein interactions involving YNL339W-B?

Investigating protein-protein interactions involving YNL339W-B requires carefully designed experiments that balance sensitivity, specificity, and physiological relevance. The following methodological approach is recommended:

• In vivo interaction detection:

  • Yeast Two-Hybrid (Y2H) screening:

    • Create bait constructs with YNL339W-B fused to DNA-binding domain

    • Screen against a prey library of S. cerevisiae proteins

    • Include autoactivation controls and confirmation through reverse Y2H

  • Bimolecular Fluorescence Complementation (BiFC):

    • Fuse YNL339W-B to one half of a split fluorescent protein (e.g., YFP-N)

    • Fuse candidate interactors to complementary half (e.g., YFP-C)

    • Monitor restored fluorescence as indication of proximity

    • Include negative controls with known non-interacting proteins

  • Proximity-based labeling:

    • Create BioID or APEX2 fusions with YNL339W-B

    • Identify biotinylated proteins through streptavidin pulldown and mass spectrometry

    • Include spatial and temporal controls for specificity

• Affinity purification methods:

  • Co-immunoprecipitation (Co-IP):

    • Express tagged YNL339W-B (e.g., FLAG, HA, or GFP tag)

    • Perform pulldown under native conditions

    • Identify co-precipitated proteins by Western blot or mass spectrometry

    • Include IgG controls and reverse Co-IP validation

  • Tandem Affinity Purification (TAP):

    • Generate TAP-tagged YNL339W-B strains

    • Perform sequential purification steps

    • Identify interacting proteins by mass spectrometry

    • Validate interactions through orthogonal methods

• In vitro interaction analysis:

  • Surface Plasmon Resonance (SPR):

    • Immobilize purified YNL339W-B on sensor chip

    • Measure binding kinetics with candidate interactors

    • Determine association/dissociation constants

    • Include negative controls with unrelated proteins

  • Isothermal Titration Calorimetry (ITC):

    • Quantitatively measure binding thermodynamics

    • Determine stoichiometry and binding affinity

    • Generate complete thermodynamic profile of interactions

• Structural studies of complexes:

  • Crosslinking Mass Spectrometry (XL-MS):

    • Identify interaction interfaces through crosslinker-mediated proximity detection

    • Map interaction domains at amino acid resolution

    • Generate structural constraints for modeling

  • Cryo-EM or X-ray crystallography:

    • Determine 3D structure of YNL339W-B in complex with partners

    • Identify key interaction residues

    • Validate through mutagenesis of interface residues

This multi-method approach provides complementary data on YNL339W-B interactions, from initial screening to detailed characterization of binding interfaces and kinetics .

What approaches can be used to study post-translational modifications of YNL339W-B protein?

Studying post-translational modifications (PTMs) of YNL339W-B requires specialized techniques that can detect, localize, and quantify these modifications. The following comprehensive methodological approach is recommended:

• Prediction and In Silico Analysis:

  • Utilize PTM prediction tools (NetPhos, UbPred, SUMOplot, etc.) to identify potential modification sites

  • Compare with known modification motifs in homologous proteins

  • Perform evolutionary conservation analysis of potential PTM sites

• Mass Spectrometry-Based Identification:

  • Sample Preparation:

    • Express and purify YNL339W-B under various physiological conditions

    • Perform enrichment for specific PTMs (phosphopeptides, glycopeptides, etc.)

    • Use both bottom-up (tryptic digestion) and top-down (intact protein) approaches

  • MS Analysis Techniques:

    • High-resolution LC-MS/MS with collision-induced dissociation (CID) and electron transfer dissociation (ETD)

    • Multiple reaction monitoring (MRM) for targeted analysis of specific modifications

    • Parallel reaction monitoring (PRM) for improved sensitivity and specificity

  • Data Analysis:

    • Use specialized PTM search algorithms (e.g., PTM-finder, Mascot PTM finder)

    • Validate PTM site localization using localization probability scores

    • Quantify PTM stoichiometry using label-free or isotope labeling approaches

• Site-Specific Validation:

  • Generate site-specific antibodies against predicted PTM sites

  • Perform Western blotting with phospho-specific or other PTM-specific antibodies

  • Create point mutations at predicted PTM sites (S/T/Y to A for phosphorylation, K to R for ubiquitination)

  • Assess functional consequences of PTM site mutations

• Temporal and Condition-Dependent Analysis:

  • Monitor PTM dynamics during cell cycle progression

  • Assess PTM changes under various stress conditions (oxidative, heat, nutrient limitation)

  • Quantify PTM changes in response to specific signaling pathways

• Enzyme Identification:

  • Perform kinase/phosphatase inhibitor screens to identify regulatory enzymes

  • Use chemical genetics with analog-sensitive kinases

  • Perform in vitro enzymatic assays with purified components

This systematic approach enables comprehensive characterization of YNL339W-B post-translational modifications, providing insights into regulatory mechanisms controlling this protein's function, localization, stability, and interactions .

How can I resolve contradictory data regarding YNL339W-B function?

Resolving contradictory data is a common challenge in protein characterization studies. When faced with conflicting results regarding YNL339W-B function, the following methodological framework is recommended:

• Systematic Comparative Analysis:

  • Create a detailed comparison table of conflicting studies, noting:

    • Strain backgrounds and genotypes

    • Experimental conditions (media, temperature, growth phase)

    • Methodological approaches

    • Data analysis techniques

  • Identify potential sources of variation that could explain discrepancies

• Validation Through Multiple Methodologies:

  • Re-examine the function using complementary techniques

  • Implement orthogonal approaches to test the same hypothesis

  • Use both in vivo and in vitro systems

  • Employ both genetic and biochemical methods

• Reproduction of Original Experiments:

  • Obtain original strains and materials when possible

  • Replicate experimental conditions precisely

  • Implement blind experimental design and analysis

  • Increase statistical power with larger sample sizes

• Strain-Specific Effects Analysis:

  • Test YNL339W-B function in multiple S. cerevisiae strain backgrounds

  • Investigate genetic interactions that might be strain-dependent

  • Perform complementation tests across strain backgrounds

  • Consider epigenetic factors that might differ between strains

• Condition-Dependent Function Assessment:

  • Systematically vary experimental conditions (pH, temperature, carbon source)

  • Test function under various stress conditions

  • Examine cell-cycle dependent effects

  • Consider metabolic state variations

• Technical Artifact Elimination:

  • Implement rigorous controls for each experiment

  • Use multiple detection methods (antibodies, tags, fusion proteins)

  • Consider off-target effects of genetic manipulations

  • Validate reagent specificity thoroughly

• Collaborative Verification:

  • Engage labs with conflicting results in collaborative studies

  • Implement standardized protocols across research groups

  • Perform inter-laboratory validation studies

  • Consider multi-center reproduction efforts

This systematic approach helps resolve contradictions by identifying whether discrepancies stem from genuine biological complexity, strain-specific effects, condition-dependent functions, or technical artifacts in experimental design .

What bioinformatics approaches should be used to predict the function of YNL339W-B?

Predicting the function of uncharacterized proteins like YNL339W-B requires sophisticated bioinformatics approaches that leverage multiple types of data. The following comprehensive methodology is recommended:

• Sequence-Based Analysis:

  • Homology Detection:

    • Position-Specific Iterative BLAST (PSI-BLAST) for remote homolog detection

    • Hidden Markov Model (HMM) profiling using HMMER

    • Multiple sequence alignment with MUSCLE or MAFFT

    • Conservation analysis to identify functionally important residues

  • Motif and Domain Identification:

    • InterProScan for integrated domain prediction

    • PFAM, SMART, and CDD database searches

    • Analysis of linear motifs using ELM (Eukaryotic Linear Motif) resource

    • Prediction of signal peptides and transmembrane regions

• Structural Prediction and Analysis:

  • 3D Structure Prediction:

    • AlphaFold2 or RoseTTAFold for accurate structure prediction

    • I-TASSER for template-based modeling

    • Quality assessment using MolProbity

  • Structural Comparison:

    • DALI, TM-align, or FATCAT for structural similarity searches

    • Analysis of binding pockets and active sites using CASTp

    • Electrostatic surface potential calculation using APBS

• Genomic Context Analysis:

  • Synteny Analysis:

    • Examination of gene neighborhood conservation

    • Identification of operonic structures in prokaryotic homologs

  • Gene Fusion Detection:

    • Identification of domain fusion events in other organisms

    • Analysis of protein architecture evolution

• Network-Based Approaches:

  • Co-expression Analysis:

    • Correlation analysis across multiple transcriptomic datasets

    • Identification of co-regulated gene modules

  • Protein-Protein Interaction Prediction:

    • Integration of known PPI networks

    • Structural-based PPI prediction using PRISM or HADDOCK

    • Functional association networks from STRING database

• Machine Learning and Integrative Methods:

  • Functional Annotation Transfer:

    • Gene Ontology term prediction using tools like DeepGOPlus

    • Enzyme Commission number prediction for potential enzymatic activity

  • Integrative Function Prediction:

    • Bayesian integration of multiple evidence types

    • Random forest or deep learning approaches combining diverse features

    • ConFunc or COFACTOR for integrated function prediction

• Evolutionary Analysis:

  • Phylogenetic Profiling:

    • Presence/absence patterns across diverse taxonomic groups

    • Correlation with known functional pathways

  • Selective Pressure Analysis:

    • dN/dS ratio calculation to detect selection signatures

    • Identification of coevolving residues using mutual information

The integration of these complementary approaches provides a robust prediction framework for YNL339W-B function, generating testable hypotheses that can guide experimental validation .

What are common challenges in purifying YNL339W-B protein and how can they be overcome?

Purification of recombinant proteins often presents technical challenges that can impact yield, purity, and activity. For YNL339W-B protein, the following challenges and solutions are particularly relevant:

• Low Expression Levels:

  • Challenge: YNL339W-B may express poorly in heterologous systems due to codon bias or toxicity.

  • Solutions:

    • Optimize codon usage for the expression host

    • Use strong inducible promoters with tight regulation

    • Reduce induction temperature to 16-18°C

    • Co-express rare tRNAs using Rosetta or similar strains

    • Test multiple fusion tags (His, GST, MBP, SUMO) for improved expression

• Protein Insolubility:

  • Challenge: Formation of inclusion bodies due to improper folding.

  • Solutions:

    • Reduce expression rate by lowering inducer concentration

    • Express as fusion with solubility-enhancing tags (MBP, SUMO, Trx)

    • Add solubility enhancers to growth media (sorbitol, glycine betaine)

    • Use specialized E. coli strains (SHuffle, Origami) for disulfide bond formation

    • Consider periplasmic expression for improved folding

• Protein Instability:

  • Challenge: Rapid degradation during expression or purification.

  • Solutions:

    • Add protease inhibitors throughout purification process

    • Maintain low temperature (4°C) during all purification steps

    • Include stabilizing agents (glycerol, trehalose) in buffers

    • Identify and eliminate specific protease cleavage sites through mutagenesis

    • Optimize buffer pH and ionic strength for maximum stability

• Protein Aggregation:

  • Challenge: Formation of aggregates during purification or storage.

  • Solutions:

    • Include mild detergents (0.05% Tween-20) in purification buffers

    • Add low concentrations of reducing agents (DTT, TCEP)

    • Perform size exclusion chromatography as final purification step

    • Monitor protein monodispersity using dynamic light scattering

    • Optimize protein concentration for storage

• Contaminant Co-purification:

  • Challenge: E. coli proteins binding to affinity resins or interacting with YNL339W-B.

  • Solutions:

    • Implement stringent washing steps with increased imidazole

    • Add sequential purification steps (ion exchange, hydrophobic interaction)

    • Include ATP/Mg²⁺ wash steps to remove chaperone contaminants

    • Use on-column refolding to separate from contaminants

    • Consider dual-tagging strategies for tandem purification

• Tag Removal Issues:

  • Challenge: Inefficient tag cleavage or protein precipitation after tag removal.

  • Solutions:

    • Optimize protease cleavage conditions (time, temperature, buffer)

    • Test multiple cleavage sites (TEV, PreScission, thrombin)

    • Perform tag removal on-column when possible

    • Include stabilizing agents during cleavage reaction

    • Consider leaving tag intact if it doesn't interfere with downstream applications

These strategies should be systematically tested and optimized for YNL339W-B purification, with careful documentation of conditions that improve yield and maintain protein integrity .

How can I troubleshoot experiments involving YNL339W-B protein that yield negative results?

Negative results when working with proteins like YNL339W-B can stem from various technical or biological factors. The following systematic troubleshooting approach is recommended:

• Protein Expression and Integrity Issues:

  • Verification Steps:

    • Confirm protein expression by Western blot using tag-specific antibodies

    • Verify protein integrity by mass spectrometry

    • Check for degradation using fresh SDS-PAGE analysis

    • Validate proper folding using circular dichroism

  • Remediation:

    • Re-express protein with fresh constructs

    • Purify under more stringent denaturing conditions followed by refolding

    • Test alternative tags or expression systems

    • Consider co-expression with chaperones

• Assay Design and Sensitivity:

  • Verification Steps:

    • Test assay sensitivity using positive controls

    • Evaluate signal-to-noise ratio in your detection system

    • Check reagent quality and shelf life

    • Verify instrument calibration and performance

  • Remediation:

    • Increase protein concentration or sample volume

    • Optimize buffer conditions (pH, salt, additives)

    • Implement more sensitive detection methods

    • Reduce background through additional controls

• Environmental and Experimental Conditions:

  • Verification Steps:

    • Monitor temperature stability during experiments

    • Check for contaminants in buffers and reagents

    • Verify accuracy of pipetting and solution preparation

    • Review experimental timing and incubation periods

  • Remediation:

    • Test multiple buffer systems

    • Vary experimental conditions systematically (pH, temperature, salt)

    • Include protective additives (BSA, glycerol)

    • Control for batch effects with internal standards

• Hypothesis and Experimental Design:

  • Verification Steps:

    • Re-evaluate underlying assumptions

    • Review literature for contradictory evidence

    • Examine experimental controls for unexpected patterns

    • Assess statistical power of experimental design

  • Remediation:

    • Reformulate hypothesis considering alternative functions

    • Design experiments with broader parameter ranges

    • Implement orthogonal approaches to test the same hypothesis

    • Consider that negative results may be valid biological findings

• Documentation and Reporting:

  • Create a detailed troubleshooting log including:

    • Experimental conditions

    • Batch information for reagents

    • Equipment settings

    • Raw data preservation

    • Statistical analyses applied

  • Share negative results with collaborators for additional perspectives

This methodical approach helps differentiate between true negative results (which can be scientifically valuable) and technical artifacts that require experimental adjustment. Transparent reporting of negative results contributes to the scientific understanding of YNL339W-B and prevents duplication of unproductive experimental paths .

What are the future research directions for understanding YNL339W-B function in Saccharomyces cerevisiae?

Understanding the function of putative proteins like YNL339W-B represents an important frontier in yeast biology. Several promising research directions should be considered for comprehensive characterization:

• Systems Biology Integration:

  • Incorporate YNL339W-B into genome-scale metabolic models

  • Perform multi-omics analysis (transcriptomics, proteomics, metabolomics) in YNL339W-B deletion strains

  • Apply network-based approaches to position YNL339W-B within functional modules

  • Develop predictive models of YNL339W-B regulation and activity

• Environmental and Stress Response Roles:

  • Systematically evaluate YNL339W-B expression and localization under diverse stress conditions

  • Investigate potential roles in adaptive responses to environmental fluctuations

  • Assess contribution to fitness under industrial fermentation conditions

  • Examine regulatory mechanisms controlling YNL339W-B expression during stress

• Evolutionary Perspectives:

  • Conduct comparative genomics across Saccharomyces species and other yeasts

  • Reconstruct the evolutionary history of the UPF0479 protein family

  • Investigate functional divergence through synthetic biology approaches

  • Assess selective pressures acting on YNL339W-B across evolutionary timescales

• Structural Biology Applications:

  • Determine high-resolution structure through X-ray crystallography or cryo-EM

  • Characterize dynamic properties through NMR or hydrogen-deuterium exchange

  • Identify potential ligand binding sites through computational docking

  • Engineer protein variants with enhanced properties for biotechnological applications

• Translational Research Opportunities:

  • Explore potential biotechnological applications based on YNL339W-B function

  • Investigate homologs in pathogenic yeasts as potential drug targets

  • Assess industrial strain improvement opportunities through YNL339W-B engineering

  • Develop biosensors or reporters based on YNL339W-B regulation

These research directions collectively contribute to a comprehensive understanding of YNL339W-B biology while potentially uncovering novel applications in biotechnology, medicine, and industrial fermentation. The integration of these approaches allows for both fundamental scientific discovery and practical applications of knowledge about this uncharacterized protein .

How can high-throughput approaches be applied to characterize YNL339W-B and related proteins?

High-throughput approaches offer powerful tools for systematic characterization of proteins like YNL339W-B. The following methodological framework outlines effective strategies:

• Functional Genomics Screening:

  • Synthetic Genetic Array (SGA) Analysis:

    • Cross YNL339W-B deletion with genome-wide deletion collection

    • Identify genetic interactions through growth fitness measurements

    • Map functional relationships based on interaction patterns

    • Classify YNL339W-B into known pathways through comparison with interaction profiles

  • Chemical-Genetic Profiling:

    • Screen YNL339W-B mutants against libraries of small molecules

    • Identify compounds with differential effects on mutant vs. wild-type

    • Map chemical-genetic interactions to infer function

    • Develop small-molecule probes for YNL339W-B function

• Proteome-Wide Interaction Mapping:

  • Affinity Purification-Mass Spectrometry:

    • Perform systematic protein complex purification

    • Identify condition-dependent interaction partners

    • Quantify interaction dynamics using SILAC or TMT labeling

    • Generate comprehensive interaction networks

  • Protein Complementation Assays:

    • Screen YNL339W-B against ordered arrays of yeast proteins

    • Use split-reporter systems (split-GFP, DHFR, luciferase)

    • Validate interactions through orthogonal assays

    • Map interaction domains using truncation libraries

• Multi-Omics Integration:

  • Transcriptome Analysis:

    • RNA-seq of YNL339W-B mutants under multiple conditions

    • Identify differentially expressed genes and regulons

    • Integration with transcription factor binding data

    • Time-course analysis for dynamic responses

  • Proteome and Metabolome Analysis:

    • Global proteomics to assess protein abundance changes

    • Phosphoproteomics to map signaling effects

    • Metabolomic profiling to identify affected pathways

    • Integration of multiple data types for system-level understanding

• High-Content Microscopy:

  • Subcellular Localization Screening:

    • Systematic imaging of YNL339W-B-GFP under various conditions

    • Co-localization with organelle markers

    • Quantitative image analysis of distribution patterns

    • Dynamic tracking during cellular processes

  • Morphological Profiling:

    • Cell morphology analysis in YNL339W-B mutants

    • Comparison with morphological databases

    • Machine learning classification of phenotypes

    • Integration with other phenotypic data

• CRISPR-Based Functional Screens:

  • CRISPRi/CRISPRa Modulation:

    • Targeted repression or activation of YNL339W-B

    • Dosage-dependent phenotypic analysis

    • Combination with other genetic perturbations

    • Temporal control of expression using inducible systems

  • Domain-Focused Mutagenesis:

    • Saturation mutagenesis of key domains

    • Base editing for precise amino acid substitutions

    • Functional complementation assays

    • Structure-function relationship mapping

These high-throughput approaches generate comprehensive datasets that, when integrated, provide unprecedented insights into YNL339W-B function within the cellular context of S. cerevisiae, enabling both hypothesis generation and validation at scale .