Recombinant Saccharomyces cerevisiae Putative uncharacterized protein YNL013C (YNL013C)

What is YNL013C and what are its basic structural characteristics?

YNL013C is a putative uncharacterized protein from Saccharomyces cerevisiae (Baker's yeast) consisting of 125 amino acids. The full amino acid sequence of YNL013C is mLTSKIYKLLTERDVLDFKLKIFIRRNVFITHLFFLLHSLLLFLSQFCRREFAFFLPTINLVTHSIKFITLFFFFLNSWASTLSCNPIMARGCHFPILNRPNFVSQILSKFCRMRNNNDTTFKSF . This protein is identified in the Uniprot database with the accession number P53979, and it is also known by alternative gene names including ORF Names: N2854 .

Bioinformatic analysis suggests YNL013C contains several hydrophobic regions, which may indicate membrane association or interaction capabilities. The protein's relatively small size makes it an interesting candidate for structural studies and functional characterization, as small proteins often serve as regulatory elements or components of larger protein complexes in cellular systems.

What expression systems are most suitable for producing recombinant YNL013C?

Multiple expression systems have been successfully employed for recombinant YNL013C production, each with distinct advantages depending on research objectives:

How should different fusion tags be selected for YNL013C expression studies?

The selection of fusion tags significantly impacts recombinant YNL013C solubility, purification efficiency, and potential functional studies:

For YNL013C, which is putatively uncharacterized, initial expression trials with His-tagged constructs are recommended for basic purification and characterization studies . For proteins with solubility challenges, MBP fusion often provides significant improvements. The tag position (N-terminal or C-terminal) should be empirically determined, as YNL013C's function and structure remain uncharacterized .

What bioinformatic approaches best predict potential functions of YNL013C?

Contemporary functional prediction for uncharacterized proteins like YNL013C requires an integrated bioinformatic approach:

• Sequence Homology Analysis: While YNL013C shows limited homology to characterized proteins, even distant relationships can provide functional clues. Use sensitive methods like PSI-BLAST, HHpred, and HMMER against multiple databases (UniProt, Pfam, CDD).

• Structural Prediction: AlphaFold2 and RoseTTAFold can generate high-confidence structural models of YNL013C, potentially revealing structural motifs absent in sequence-based analyses.

• Genome Context Analysis: Examining genes adjacent to YNL013C in the S. cerevisiae genome may indicate functional relationships, particularly if gene order is conserved across related yeast species.

• Co-expression Network Analysis: RNA-seq data across multiple conditions can identify genes with expression patterns correlated with YNL013C, suggesting functional relationships.

• Protein-Protein Interaction Prediction: Tools like STRING integrate multiple evidence sources to predict interaction partners, placing YNL013C in a functional context.

Implementation of these approaches requires combining results from multiple predictive algorithms, as no single method provides comprehensive functional insights for novel proteins like YNL013C.

What experimental approaches are most effective for characterizing YNL013C function?

Comprehensive functional characterization of YNL013C requires a multi-faceted experimental strategy:

• Genetic Approaches:

  • CRISPR/Cas9-mediated knockout or knockdown

  • Overexpression studies

  • Synthetic genetic array (SGA) analysis to identify genetic interactions

• Biochemical Approaches:

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

  • In vitro activity assays based on bioinformatic predictions

  • Post-translational modification analysis

• Cellular Localization:

  • Fluorescent protein tagging for live-cell imaging

  • Immunofluorescence with antibodies against tagged YNL013C

  • Subcellular fractionation followed by Western blotting

• Phenotypic Analysis:

  • Growth profiling under various stress conditions

  • Metabolomic analysis comparing wild-type and YNL013C mutant strains

  • Transcriptomic response to YNL013C perturbation

The most successful characterization studies combine multiple approaches, as singular techniques rarely provide definitive functional insights for previously uncharacterized proteins.

How can structural characterization advance our understanding of YNL013C?

Structural studies provide critical insights into protein function that complement genetic and biochemical approaches:

• X-ray Crystallography: Though challenging for membrane-associated proteins, crystallization of YNL013C or soluble domains can reveal atomic-level details of structure. Success often requires screening hundreds of crystallization conditions and potentially removing flexible regions.

• Cryo-electron Microscopy (cryo-EM): Particularly valuable if YNL013C forms larger complexes with other proteins, potentially revealing interaction interfaces and conformational states.

• Nuclear Magnetic Resonance (NMR) Spectroscopy: Suitable for smaller proteins or domains under 20 kDa, providing information about protein dynamics in solution.

• Small-Angle X-ray Scattering (SAXS): Offers low-resolution structural information in native solution conditions, complementing higher-resolution techniques.

• Hydrogen-Deuterium Exchange Mass Spectrometry (HDX-MS): Provides information about protein dynamics and solvent accessibility, useful for identifying functional regions.

For YNL013C specifically, initial efforts should focus on producing highly pure, homogeneous protein suitable for these structural techniques. The 125-amino acid length makes it potentially amenable to NMR studies if solubility can be optimized.

What is the optimal protocol for purifying recombinant YNL013C to achieve high purity?

Purification of recombinant YNL013C requires a carefully optimized protocol to achieve the high purity needed for structural and functional studies:

• Initial Purification Strategy for His-tagged YNL013C:

  a) Cell Lysis: For E. coli-expressed protein, sonication in buffer containing 50 mM Tris-HCl pH 8.0, 300 mM NaCl, 10 mM imidazole, 1 mM PMSF, and protease inhibitor cocktail.

  b) IMAC Purification: Load cleared lysate onto Ni-NTA resin, wash with increasing imidazole concentrations (20-50 mM), and elute with 250-300 mM imidazole.

  c) Size Exclusion Chromatography: Further purify using Superdex 75 column in buffer containing 20 mM Tris-HCl pH 7.5, 150 mM NaCl.

• Alternative Tags and Approaches:

  a) MBP Fusion: For improved solubility, purify using amylose resin followed by size exclusion.

  b) GST Fusion: Purify using glutathione sepharose, with optional on-column cleavage.

• Tag Removal Considerations:

  Protease	Recognition Site	Cleavage Efficiency	Conditions
  TEV	ENLYFQ↓G	High	16°C, overnight, reducing conditions
  PreScission	LEVLFQ↓GP	Very good	4°C, overnight
  Thrombin	LVPR↓GS	Variable	Room temperature, 4-16 hours

• Protein Storage: Store purified YNL013C in buffer containing 50% glycerol at -20°C for extended stability . For longer storage, flash-freeze aliquots in liquid nitrogen and store at -80°C to prevent freeze-thaw cycles.

The optimal purification strategy should be determined empirically, as the behavior of YNL013C during purification cannot be fully predicted from sequence information alone.

What analytical methods are most effective for assessing YNL013C quality and homogeneity?

Comprehensive quality assessment of purified YNL013C is critical before proceeding to functional or structural studies:

• Purity Assessment:

  • SDS-PAGE with Coomassie or silver staining (target >95% purity)

  • Capillary electrophoresis for higher resolution separation

  • Reversed-phase HPLC for detection of closely related species

• Homogeneity Analysis:

  • Size exclusion chromatography with multi-angle light scattering (SEC-MALS)

  • Dynamic light scattering (DLS) to detect aggregation

  • Analytical ultracentrifugation for detailed oligomeric state analysis

• Structural Integrity Verification:

  • Circular dichroism (CD) spectroscopy for secondary structure assessment

  • Fluorescence spectroscopy to monitor tertiary structure

  • Differential scanning fluorimetry (DSF) for thermal stability

• Mass Spectrometry Applications:

  • Intact mass analysis to confirm molecular weight and modifications

  • Peptide mapping to verify sequence coverage

  • Top-down proteomics for comprehensive characterization

For YNL013C research, establishing reliable quality assessment protocols is particularly important due to its uncharacterized nature and the need to ensure consistent protein preparations across different experimental approaches.

How can site-directed mutagenesis be applied to study structure-function relationships in YNL013C?

Site-directed mutagenesis provides powerful insights into structure-function relationships for uncharacterized proteins like YNL013C:

• Rational Design of Mutations:

  • Conserved residues across homologs, even distant ones, are prime targets

  • Charged residues (D, E, K, R) that may form salt bridges or catalytic sites

  • Hydrophobic clusters that potentially contribute to structural stability

  • Predicted post-translational modification sites

• Mutagenesis Strategy:

  • Alanine scanning of sections with predicted functional importance

  • Conservative substitutions to preserve charge or hydrophobicity

  • Non-conservative substitutions to drastically alter properties

  • Introduction or removal of potential disulfide bonds

• Functional Assessment of Mutants:

  • Complementation of knockout phenotypes

  • In vitro activity assays (once a function is hypothesized)

  • Protein-protein interaction studies

  • Structural stability and folding analysis

• Systematic Approach:

  • Begin with 5-10 key residues based on conservation or prediction

  • Expand based on initial results

  • Consider creating libraries of variants for high-throughput screening

For YNL013C specifically, initial mutagenesis might target the highly hydrophobic regions, as these could represent membrane-interacting domains or protein-protein interaction surfaces critical to function.

How should researchers integrate multi-omics data to understand YNL013C's role in cellular networks?

Understanding YNL013C's function requires synthesizing data from multiple experimental approaches:

• Multi-omics Data Integration Strategy:

  • Combine transcriptomics, proteomics, and metabolomics data from YNL013C perturbation studies

  • Use network analysis tools to identify pathways affected by YNL013C manipulation

  • Apply machine learning approaches to predict function from integrated datasets

• Software and Computational Tools:

  • Cytoscape for network visualization and analysis

  • MetaboAnalyst for metabolomics data integration

  • Perseus for proteomics data analysis

  • Integrated algorithms like WGCNA for co-expression network analysis

• Validation Approach:

  • Confirm key findings with targeted experiments

  • Use orthogonal techniques to verify critical interactions

  • Employ mathematical modeling to predict system-level effects

Multi-omics integration is particularly valuable for YNL013C as an uncharacterized protein, as single-approach studies are unlikely to reveal its full functional context within cellular networks.

What statistical approaches are most appropriate for analyzing YNL013C knockout/knockdown phenotypic data?

Rigorous statistical analysis is essential for interpreting phenotypic data from YNL013C perturbation studies:

• Experimental Design Considerations:

  • Include multiple biological replicates (minimum n=3, preferably n=5)

  • Account for batch effects through randomization and blocking

  • Include appropriate positive and negative controls

• Statistical Analysis Methods:

  • For growth phenotypes: Repeated measures ANOVA with post-hoc tests

  • For transcriptomic data: DESeq2 or limma for differential expression

  • For high-content screening: Mixed-effects models to account for plate effects

  • For survival analysis: Kaplan-Meier curves with log-rank tests

• Multiple Testing Correction:

  • Apply Benjamini-Hochberg procedure for false discovery rate control

  • Consider more stringent family-wise error rate methods for critical findings

• Effect Size Reporting:

  • Report confidence intervals alongside p-values

  • Calculate and report standardized effect sizes (Cohen's d, odds ratios)

  • Provide power analysis for negative results

For YNL013C studies, statistical rigor is particularly important given the likely subtle phenotypes associated with many uncharacterized yeast proteins, where effects may be condition-dependent or manifest only under specific stresses.

How might YNL013C research contribute to understanding fundamental cellular processes?

While YNL013C remains uncharacterized, several research directions hold promise for connecting this protein to broader cellular functions:

• Stress Response Pathways: Systematic testing of YNL013C knockout and overexpression strains under various stress conditions (oxidative, osmotic, thermal, nutrient limitation) may reveal condition-specific phenotypes.

• Metabolic Network Integration: Metabolic profiling of YNL013C mutants could connect this protein to specific metabolic pathways, particularly if phenotypes emerge under defined nutrient conditions.

• Evolutionary Conservation Analysis: Detailed phylogenetic studies across fungi could identify conserved features and potentially connect YNL013C to characterized proteins in other species.

• Interactome Mapping: Comprehensive protein-protein interaction studies using techniques like BioID or APEX proximity labeling could place YNL013C in specific cellular compartments and complexes.

• Conditional Essentiality Screening: Testing genetic interactions with essential genes may reveal synthetic lethal or sick interactions that connect YNL013C to critical cellular processes.

The systematic characterization of uncharacterized proteins like YNL013C contributes to completing our understanding of the yeast cellular network, potentially revealing novel regulatory mechanisms and cellular processes.

What collaborative research approaches would accelerate understanding of YNL013C function?

Due to the multidisciplinary nature of protein characterization, collaborative research strategies are particularly valuable for uncharacterized proteins like YNL013C:

• Interdisciplinary Team Structure:

  • Computational biologists for structure prediction and data integration

  • Biochemists for protein purification and in vitro characterization

  • Cell biologists for localization and phenotypic studies

  • Structural biologists for 3D structure determination

  • Systems biologists for network-level analysis

• Technology Integration:

  • High-throughput phenotyping platforms

  • Advanced imaging facilities

  • Mass spectrometry for proteomics and metabolomics

  • Next-generation sequencing for transcriptomics

  • Computational clusters for data analysis

• Knowledge Sharing Platforms:

  • Dedicated database for YNL013C-related findings

  • Regular cross-disciplinary meetings

  • Standardized protocols for reproducibility

  • Open data sharing through repositories like Zenodo or Dryad

Collaborative approaches are essential for building a comprehensive understanding of previously uncharacterized proteins, where multiple expertise areas and technological platforms must be integrated to reveal function.