Recombinant Saccharomyces cerevisiae Uncharacterized protein YKL023C-A (YKL023C-A)

What experimental approaches are recommended for initial characterization of YKL023C-A?

For initial characterization of an uncharacterized protein like YKL023C-A, a multi-faceted approach is recommended:

• Gene expression analysis: Quantify YKL023C-A expression under various environmental conditions using RT-qPCR or RNA-seq to determine when and where the gene is active.

• Subcellular localization: Express YKL023C-A fused with a fluorescent tag (e.g., GFP) to visualize its cellular localization through fluorescence microscopy.

• Phenotypic analysis: Generate knockout or overexpression strains to observe resulting phenotypes. Since S. cerevisiae is a well-established model organism, standard transformation protocols can be effectively employed for genetic manipulation .

• Protein-protein interaction studies: Use yeast two-hybrid systems or co-immunoprecipitation followed by mass spectrometry to identify interacting partners.

• Comparative genomics: Analyze potential orthologs across species to infer functional conservation. This is particularly valuable as S. cerevisiae shares many biological processes with higher eukaryotes including humans .

What bioinformatic tools can help predict the potential function of YKL023C-A?

Several computational approaches can provide initial insights into the potential function of YKL023C-A:

• Sequence homology analysis: Tools like BLAST, HHpred, and HMMER can identify distant relationships with characterized proteins.

• Structural prediction: AlphaFold2 or I-TASSER can generate structural models to inform potential functions.

• Domain and motif identification: PROSITE, PFAM, or InterPro can detect functional domains and motifs within the sequence.

• Transmembrane topology prediction: TMHMM or Phobius can determine if YKL023C-A contains membrane-spanning regions.

• Protein interaction network analysis: STRING database integration can predict functional partners based on genomic context and co-expression patterns.

• Evolutionary conservation analysis: ConSurf can map conservation patterns onto the predicted structure to identify functionally important residues.

The above approaches would provide complementary information to guide subsequent experimental validation of hypothesized functions.

What regulatory mechanisms likely control YKL023C-A expression in S. cerevisiae?

Available data suggests YKL023C-A may be regulated by the transcription factor Fkh1p (Fork head homolog 1) . Fkh1p is a member of the fork head family of transcription factors and plays roles in chromatin silencing, cell cycle regulation, and expression control of cell-cycle dependent genes.

To comprehensively investigate the regulatory mechanisms controlling YKL023C-A expression:

• Promoter analysis: Perform chromatin immunoprecipitation (ChIP) assays to confirm Fkh1p binding to the YKL023C-A promoter region. Follow with mutation studies of predicted binding sites to assess their functional significance.

• Transcriptional profiling: Analyze YKL023C-A expression levels in wild-type versus Fkh1p knockout strains under various conditions using RNA-seq.

• Epigenetic regulation: Assess chromatin modification patterns at the YKL023C-A locus using ChIP-seq for various histone modifications.

• Post-transcriptional regulation: Investigate mRNA stability and potential regulation by RNA-binding proteins or non-coding RNAs.

This multi-level analysis would provide insights into both the transcriptional and post-transcriptional regulation of YKL023C-A.

How can CRISPR-Cas9 be optimized for studying YKL023C-A function in S. cerevisiae?

CRISPR-Cas9 offers powerful approaches for studying uncharacterized proteins like YKL023C-A in S. cerevisiae. For optimal results:

• Guide RNA design: Use S. cerevisiae-specific design tools to generate guide RNAs with minimal off-target effects. For YKL023C-A's small ORF (225 bp), carefully select target sites to ensure complete disruption.

• Delivery method optimization:

  • Plasmid-based expression: Use centromeric plasmids with appropriate promoters (e.g., SNR52 for gRNA, TEF1 for Cas9)

  • Transformation efficiency: Optimize transformation protocols specifically for S. cerevisiae using lithium acetate with heat shock

• Repair template design:

  • For knock-outs: Include homology arms of 40-60 bp flanking YKL023C-A

  • For tagging: Design in-frame fusion constructs with appropriate linkers

  • For point mutations: Include silent mutations in the PAM site to prevent re-cutting

• Verification strategies:

  • PCR screening and Sanger sequencing of edited loci

  • Western blotting for tagged variants

  • RNA-seq to confirm expression changes

• Phenotypic analysis: Systematically assess growth rates, morphology, and stress responses across different environmental conditions in the edited strains.

The yeast S. cerevisiae is particularly amenable to CRISPR-Cas9 editing due to its efficient homologous recombination machinery, making it an excellent platform for studying YKL023C-A function .

What comparative genomics approaches can identify potential orthologs of YKL023C-A across species?

To identify potential orthologs of YKL023C-A across species, implement the following comprehensive approach:

• Sequence-based methods:

  • Position-Specific Iterative BLAST (PSI-BLAST) to detect distant homologs

  • Hidden Markov Model (HMM) profile searches against diverse genomic databases

  • Synteny analysis to examine conservation of genomic context

• Structure-based methods:

  • Fold recognition algorithms to identify proteins with similar predicted structures

  • Analysis of conserved structural motifs even in the absence of sequence similarity

• Phylogenetic analysis:

  • Maximum likelihood or Bayesian approaches to construct phylogenetic trees

  • Reconciliation of gene trees with species trees to differentiate orthologs from paralogs

• Functional inference:

  • Analysis of co-expression patterns across species

  • Conservation of protein-protein interaction networks

A particularly valuable approach would be to compare YKL023C-A across the Saccharomycetaceae family and other fungi to establish its evolutionary history. The methodology used in comparing S. cerevisiae strains as described in search result could be adapted, where whole-genome sequencing and variant calling against the reference genome identified evolutionary relationships .

What methodologies are most effective for studying protein-protein interactions involving YKL023C-A?

For comprehensive characterization of YKL023C-A's interaction network:

• In vivo approaches:

  • Yeast two-hybrid (Y2H): Create YKL023C-A bait constructs and screen against a genome-wide prey library. For small proteins like YKL023C-A, consider both N- and C-terminal fusions to avoid masking interaction domains.

  • Proximity-labeling methods: Express YKL023C-A fused to BioID or APEX2 to biotinylate proximal proteins in their native cellular environment.

  • Fluorescence resonance energy transfer (FRET): Use for validating direct interactions with specific candidate partners.

• In vitro approaches:

  • Pull-down assays: Use recombinant GST-tagged or His-tagged YKL023C-A as bait.

  • Surface plasmon resonance (SPR): Determine binding kinetics and affinity constants for specific interactions.

  • Isothermal titration calorimetry (ITC): Characterize thermodynamic parameters of interactions.

• Structural approaches:

  • X-ray crystallography or NMR: Determine 3D structures of complexes.

  • Cryo-electron microscopy: Visualize larger complexes containing YKL023C-A.

• Mass spectrometry-based approaches:

  • Affinity purification coupled with mass spectrometry (AP-MS): Express tagged YKL023C-A, purify complexes, and identify components by MS.

  • Cross-linking mass spectrometry (XL-MS): Map interaction interfaces at amino acid resolution.

Since S. cerevisiae has been established as an effective model system for studying protein interactions, these methodologies can be readily applied to understand YKL023C-A's functional context .

How can systems biology approaches illuminate the functional role of YKL023C-A in cellular networks?

Systems biology offers powerful frameworks to contextualize YKL023C-A within broader cellular networks:

• Multi-omics integration:

  • Combine transcriptomics, proteomics, and metabolomics data from wild-type and YKL023C-A mutant strains

  • Develop correlation networks to identify modules associated with YKL023C-A function

  • Apply machine learning algorithms to predict functional associations

• Network analysis:

  • Construct protein-protein interaction networks centered on YKL023C-A

  • Identify network motifs and topological features indicating functional significance

  • Apply centrality measures to assess YKL023C-A's importance in different cellular processes

• Flux balance analysis:

  • Incorporate YKL023C-A-related constraints into genome-scale metabolic models

  • Simulate metabolic phenotypes under various genetic and environmental conditions

  • Predict systemic effects of YKL023C-A perturbation

• Comparative systems biology:

  • Analyze conservation of network modules containing YKL023C-A orthologs across species

  • Apply the methodology described in search result , which assessed S. cerevisiae as a model organism by comparing functional protein classifications across 704 organisms

• Data visualization and integration:

  • Develop interactive visualizations of multi-scale data

  • Create searchable databases of YKL023C-A-associated phenotypes and interactions

This systems-level understanding could reveal emergent properties not obvious from reductionist approaches, potentially uncovering YKL023C-A's role in maintaining cellular homeostasis or responding to environmental changes.

What are the optimal conditions for expression and purification of recombinant YKL023C-A?

Based on the information available about YKL023C-A and general practices for yeast proteins, the following optimized protocol is recommended:

• Expression system selection:

  • Prokaryotic: E. coli BL21(DE3) with codon optimization for small proteins

  • Eukaryotic: Pichia pastoris for potential post-translational modifications

  • Homologous: S. cerevisiae itself for native conditions

• Vector design considerations:

  • Include fusion tags (His6, GST, or MBP) to facilitate purification

  • Incorporate a precision protease cleavage site for tag removal

  • Consider inducible promoters (T7 for E. coli, GAL1 for yeast)

• Expression conditions optimization:

  • Temperature: Test expression at 16°C, 25°C, and 30°C

  • Induction time: Optimize between 4-24 hours

  • Media composition: Compare rich vs. minimal media performance

• Purification strategy:

  • Initial capture: Affinity chromatography based on fusion tag

  • Intermediate purification: Ion exchange chromatography

  • Polishing: Size exclusion chromatography

  • Buffer optimization: Test various pH ranges (6.0-8.0) and salt concentrations

• Protein storage:

  • Based on search result , YKL023C-A is typically stored in Tris-based buffer with 50% glycerol

  • Store aliquots at -20°C for short-term use and at -80°C for long-term storage

  • Avoid repeated freeze-thaw cycles

The purified protein should be assessed for purity via SDS-PAGE and for structural integrity using circular dichroism spectroscopy before proceeding to functional studies.

How can researchers resolve contradictory experimental data when characterizing YKL023C-A?

When faced with contradictory experimental data about YKL023C-A, implement this systematic approach:

• Data validation and quality assessment:

  • Rigorously examine experimental controls and technical replicates

  • Assess statistical robustness and power of conflicting experiments

  • Evaluate methodology sensitivity and specificity for each experimental approach

• Biological context considerations:

  • Analyze strain background differences (reference strain S288c vs. other backgrounds)

  • Compare growth conditions and media composition across experiments

  • Examine cell cycle stage or metabolic state variations

• Resolving contradictions through complementary approaches:

  • Implement orthogonal techniques to address the same question

  • Design experiments that can distinguish between competing hypotheses

  • Perform time-course studies to capture dynamic behaviors

• Collaborative resolution strategies:

  • Establish standardized protocols across research groups

  • Share reagents (particularly antibodies and strain collections)

  • Implement multi-laboratory validation studies

• Computational integration of conflicting data:

  • Develop Bayesian models to weight evidence based on methodological strength

  • Apply machine learning approaches to identify patterns in seemingly contradictory results

  • Use simulations to test if contradictions could be explained by stochastic effects