Recombinant Saccharomyces cerevisiae Putative uncharacterized protein YPR064W (YPR064W)

What is the primary sequence and structural information of YPR064W?

YPR064W is a full-length protein consisting of 139 amino acids from Saccharomyces cerevisiae. Its primary amino acid sequence is: MKSCVLSNYVEGLEIIVCGYRNRLLFPFRATQVQAYFKVFSFFFFLLLTLGAAAAAKPTSERQVIFGSADKSPGYHWPVEVSAAWNYRELLCKTIGNLVGKPCSRTWRLLTKKKRAYCCCLFCCSSSYCLAGVLCVFCV . The protein contains notable hydrophobic stretches suggesting potential membrane association or transmembrane domains. While detailed three-dimensional structural data is limited due to its uncharacterized nature, computational structure prediction tools can provide preliminary insights into its potential secondary and tertiary structural elements.

How should researchers reconstitute and store recombinant YPR064W protein for experimental use?

For optimal handling of recombinant YPR064W, the lyophilized protein should be briefly centrifuged prior to opening to ensure contents are at the bottom of the vial. Reconstitution should be performed in deionized sterile water to a concentration of 0.1-1.0 mg/mL. For long-term storage, adding glycerol to a final concentration of 5-50% (with 50% being standard) and aliquoting before storing at -20°C/-80°C is recommended . Repeated freeze-thaw cycles should be avoided as they may compromise protein integrity. Working aliquots can be maintained at 4°C for up to one week . For experiments requiring specific buffer conditions, additional buffer exchanges may be necessary depending on the intended application.

What differential gene expression patterns have been observed for YPR064W under various conditions?

YPR064W has been identified in differential gene expression analyses, particularly in studies comparing wild-type and SNF2 mutant strains of S. cerevisiae . While specific fold-change values for YPR064W were not explicitly detailed in the available data, the protein appears in lists of differentially expressed genes. To thoroughly characterize YPR064W expression patterns, researchers should design experiments examining various stress conditions (oxidative, osmotic, temperature), carbon sources, and growth phases. Quantitative PCR approaches similar to those described for other yeast genes can be adapted, using primers designed with tools such as IDT's PrimerQuest . For such analyses, ACT1 has been successfully used as a reference gene for normalization using the ΔΔCт method .

How can researchers perform chromatin immunoprecipitation (ChIP) experiments to identify transcription factors that regulate YPR064W?

To identify transcription factors that regulate YPR064W expression, researchers can implement ChIP-exo methodology. This approach involves:

• Cross-linking proteins to DNA in vivo using formaldehyde

• Cell lysis and chromatin fragmentation

• Immunoprecipitation using antibodies against suspected transcription factors

• On-bead end repair and adapter ligation

• Elution and reverse cross-linking

• DNA amplification and next-generation sequencing

Specifically, researchers should follow laboratory protocols that include treatment with lambda exonuclease and RecJf for chromatin trimming, followed by adapter ligation containing unique 6-bp index sequences . After reverse cross-linking at 65°C overnight and protease K digestion, DNA should be extracted with phenol-chloroform-isoamyl alcohol, subjected to primer extension using phi29 DNA polymerase, and amplified by PCR for 20-22 cycles . Size selection and quality control using systems like Qubit dsDNA HS assay kit and TapeStation are crucial before sequencing on platforms such as Illumina NextSeq 500 .

What bioinformatic approaches should be utilized to analyze YPR064W gene expression data?

For comprehensive analysis of YPR064W expression data, researchers should employ a multi-faceted bioinformatic approach:

• Differential expression analysis: Use packages like DESeq2 to identify significant changes in YPR064W expression across conditions. This generates metrics including baseMean values, log2 fold changes, standard errors, and adjusted p-values to determine statistical significance .

• Binding site motif analysis: For promoter analysis, implement MEME-ChIP 4.10.2 to analyze sequences upstream and downstream of binding sites . Prediction of transcription factor binding sites can be performed using databases such as YeTFaSCo .

• Gene ontology annotation: Utilize GO Slim Mapper from SGD to understand the functional context of YPR064W .

• Transcription factor network analysis: Apply tools like YEASTRACT's "Rank Genes by TF" to identify overlaps between YPR064W regulation and other transcription factor targets .

• Visualization: Generate enhanced volcano plots to visualize differential expression results, plotting log2 fold changes against -log10 p-values to identify significant expression changes .

What approaches can researchers use to determine the subcellular localization of YPR064W?

Determining the subcellular localization of YPR064W requires multiple complementary experimental approaches:

• Fluorescent protein tagging: Generate C- or N-terminal GFP/mCherry fusion constructs of YPR064W through homologous recombination in S. cerevisiae. When designing these constructs, consider the protein sequence characteristics, including the potential membrane-associated regions indicated by hydrophobic stretches in the amino acid sequence (MKSCVLSNYVEGLEIIVCGYRNRLLFPFRATQVQAYFKVFSFFFFLLLTLGAAAAAKPTS) .

• Subcellular fractionation: Perform differential centrifugation to isolate cellular compartments (cytosol, nucleus, mitochondria, membrane fractions), followed by Western blot analysis using antibodies against the His-tag of recombinant YPR064W protein or against the native protein if specific antibodies are available.

• Immunofluorescence microscopy: Use antibodies against YPR064W in fixed yeast cells, combined with markers for specific organelles to determine colocalization patterns.

• Bioinformatic prediction: Employ tools such as PSORT, TargetP, or SignalP to predict potential localization signals within the YPR064W sequence.

The combination of these approaches will provide robust evidence for the protein's subcellular distribution, which is essential for understanding its function.

How can researchers identify potential protein-protein interactions involving YPR064W?

To identify protein interaction partners of YPR064W, researchers should implement a multi-technique approach:

After identifying potential interaction partners, validation through reciprocal experiments and functional studies is essential. Bioinformatic analysis of interacting proteins may reveal functional clusters and pathway associations that provide insights into YPR064W's biological role.

What genetic approaches can determine if YPR064W is involved in DNA repair mechanisms?

Given the context of DNA repair research in the available literature , investigating YPR064W's potential role in DNA repair mechanisms would require systematic genetic approaches:

• Phenotypic analysis of deletion mutants: Generate and characterize ypr064wΔ strains for sensitivity to DNA damaging agents (UV, MMS, hydrogen peroxide, ionizing radiation). Compare growth rates and survival percentages with wild-type and known DNA repair mutants.

• Synthetic genetic arrays (SGA): Screen for genetic interactions between ypr064wΔ and mutations in known DNA repair genes to identify potential pathway associations. Synthetic lethality or sickness would suggest functional relationships.

• Epistasis analysis: Construct double mutants with genes involved in specific DNA repair pathways (homologous recombination, non-homologous end joining, mismatch repair) to determine pathway membership based on epistatic relationships.

• Recombination assays: Implement reporter systems to measure rates of homologous recombination, non-homologous end joining, or single-strand annealing in ypr064wΔ mutants compared to controls.

• Localization during DNA damage: Examine if YPR064W-GFP forms repair foci following induction of DNA damage, co-localizing with known repair factors.

Results from these approaches would collectively determine whether YPR064W functions in DNA repair and identify the specific repair pathway in which it participates.

How can researchers distinguish between direct and indirect effects when YPR064W appears in differential expression datasets?

Distinguishing direct from indirect effects in differential expression datasets containing YPR064W requires a rigorous experimental framework:

• Time-course expression analysis: Implement a detailed temporal analysis of gene expression changes following perturbation (e.g., environmental stress, genetic modification). Primary response genes typically show expression changes within minutes, while secondary responders show delayed kinetics.

• Transcription inhibition studies: Use transcriptional inhibitors like 1,10-phenanthroline or thiolutin during perturbation experiments. Genes that change expression despite transcription inhibition are likely post-transcriptionally regulated.

• Chromatin immunoprecipitation sequencing (ChIP-seq): If YPR064W is regulated by a known transcription factor identified in previous studies, perform ChIP-seq analysis to determine if the transcription factor directly binds to the YPR064W promoter region. Follow protocols similar to those used for ChIP-exo experiments, including appropriate controls and statistical analysis .

• Promoter dissection: Create reporter constructs with progressive deletions of the YPR064W promoter to identify regulatory elements responsible for observed expression changes.

• Integration with transcription factor binding site predictions: Compare experimental results with computational predictions from databases like YeTFaSCo to validate direct regulatory interactions .

Statistical significance should be evaluated using appropriate tests, such as a one-tailed equal variance t-test, with clear reporting of p-values .

What are the challenges in structural characterization of YPR064W and how can they be addressed?

The structural characterization of YPR064W faces several significant challenges:

• Protein solubility and stability: The amino acid sequence suggests hydrophobic regions that may cause aggregation during expression and purification. To address this, researchers should:

  • Test multiple fusion tags beyond the standard His-tag

  • Screen various buffer conditions with stabilizing agents (glycerol, detergents for membrane proteins)

  • Consider truncation constructs to remove problematic regions

• Crystallization difficulties: Membrane-associated or intrinsically disordered proteins often resist crystallization. Alternative approaches include:

  • Nuclear Magnetic Resonance (NMR) spectroscopy for solution structure

  • Cryo-electron microscopy for larger complexes

  • Small-angle X-ray scattering (SAXS) for low-resolution envelope structures

• Conformational heterogeneity: If YPR064W adopts multiple conformations, computational approaches like molecular dynamics simulations can complement experimental data to characterize conformational ensembles.

• Post-translational modifications: Express YPR064W in eukaryotic systems (e.g., Pichia pastoris) if native modifications are crucial for structure determination, followed by mass spectrometry to identify and map modifications.

• Protein-protein interactions: If YPR064W structure depends on binding partners, co-expression and co-purification strategies should be implemented based on interaction data from previous experiments.

For initial crystallization trials, researchers should purify the His-tagged recombinant protein to high homogeneity and screen hundreds of crystallization conditions using automated systems.

How should researchers approach RNA-seq experimental design to study YPR064W expression across different growth conditions?

A comprehensive RNA-seq experimental design for studying YPR064W expression should address biological variability while maximizing detection of condition-specific changes:

• Condition selection and replication:

  • Include at least 3-5 biological replicates per condition to account for biological variability

  • Test multiple carbon sources (glucose, ethanol, glycerol)

  • Include various stress conditions (oxidative, osmotic, temperature, nutrient limitation)

  • Sample across multiple growth phases (lag, log, post-diauxic, stationary)

• Sample preparation protocols:

  • Harvest cells by rapid centrifugation after mixing with crushed ice

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

  • Extract total RNA using established kits (e.g., RNeasy minikit)

  • Assess RNA quality using bioanalyzer (RNA Integrity Number > 8.0)

  • Prepare RNA-seq libraries using strand-specific protocols

• Sequencing parameters:

  • Aim for 20-30 million reads per sample for adequate coverage

  • Use paired-end sequencing (2 × 75 bp) for improved transcript mapping

  • Include spike-in controls for normalization

• Analysis workflow:

  • Align reads to the S. cerevisiae reference genome

  • Quantify gene expression using count-based methods

  • Perform differential expression analysis using DESeq2

  • Visualize results with volcano plots showing log2 fold changes against -log10 p-values

  • Validate key findings with qPCR using established reference genes like ACT1

• Data integration:

  • Compare RNA-seq results with existing ChIP-seq/ChIP-exo data

  • Perform pathway enrichment analysis to contextualize YPR064W expression changes

  • Correlate expression patterns with growth parameters and metabolic measurements

This comprehensive approach will provide robust insights into YPR064W regulation across diverse conditions.

How should researchers interpret discrepancies between predicted and experimental data for YPR064W function?

When faced with discrepancies between predicted and experimental data for YPR064W function, researchers should implement a systematic evaluation process:

• Reassess prediction methodology:

  • Examine the assumptions and limitations of the prediction algorithms

  • Consider if the prediction tools were trained on proteins similar to YPR064W

  • Evaluate if the protein contains unusual features that might confound prediction tools

• Validate experimental approaches:

  • Review experimental controls for potential confounding factors

  • Assess whether the experimental conditions might influence protein behavior

  • Consider if protein tags or fusion constructs might alter native function

  • Evaluate statistical power and reproducibility across independent experiments

• Reconciliation strategies:

  • Implement orthogonal experimental approaches to test the same hypothesis

  • Consider if YPR064W might have multiple functions depending on context

  • Examine if post-translational modifications might explain functional discrepancies

  • Investigate if protein-protein interactions modify YPR064W function

• Computational refinement:

  • Update models with experimental data for improved predictions

  • Employ machine learning approaches that incorporate new experimental features

  • Use molecular dynamics simulations to explore functional states not captured in static models

• Evolutionary context:

  • Analyze function of orthologs in related species

  • Consider if gene duplication and functional divergence might explain discrepancies

When reporting such discrepancies in publications, researchers should present both predicted and experimental data transparently, acknowledging limitations and proposing testable hypotheses to resolve contradictions.

What statistical approaches should be used when analyzing YPR064W differential expression across multiple experimental conditions?

When analyzing YPR064W differential expression across multiple conditions, researchers should implement rigorous statistical frameworks:

• For pairwise comparisons:

  • Employ DESeq2 or edgeR for count-based RNA-seq data, which model the negative binomial distribution and account for biological variability

  • Apply multiple testing correction using Benjamini-Hochberg procedure to control false discovery rate

  • Consider a gene differentially expressed when adjusted p-value < 0.05 and absolute log2 fold change > 1.0

  • Visualize results using volcano plots showing statistical significance against effect size

• For multi-factorial designs:

  • Implement linear models that account for multiple experimental factors and their interactions

  • Use likelihood ratio tests to evaluate the significance of specific factors

  • Consider mixed-effect models if including random effects (e.g., batch effects)

• For time-series data:

  • Apply methods specifically designed for temporal data, such as maSigPro or ImpulseDE2

  • Consider autocorrelation between timepoints when determining statistical significance

• Validation approaches:

  • Confirm key findings with qPCR using reference genes like ACT1 for normalization

  • Apply one-tailed equal variance t-tests for targeted validation experiments

  • Report both statistical significance and effect size measures

• Data integration:

  • Use meta-analysis approaches when combining data from multiple studies

  • Employ network-based statistics when analyzing YPR064W in the context of interacting genes

  • Consider Bayesian approaches that can incorporate prior knowledge about YPR064W

By applying these statistical methods, researchers can obtain robust insights into YPR064W expression patterns while minimizing false discoveries and spurious correlations.

How can researchers resolve conflicting data about YPR064W's role in cellular pathways?

Resolving conflicting data about YPR064W's role in cellular pathways requires a systematic approach combining experimental validation, contextual analysis, and integration of diverse data types:

• Experimental validation hierarchy:

  • Prioritize direct biochemical evidence over genetic interactions

  • Value in vivo studies over in vitro experiments

  • Consider dosage effects—overexpression versus deletion phenotypes may reveal different aspects of function

  • Evaluate results from acute versus chronic perturbations separately

• Context-dependent function analysis:

  • Systematically test YPR064W function across diverse conditions (carbon sources, stress conditions, growth phases)

  • Consider genetic background effects by testing in multiple strain backgrounds

  • Examine if environmental context explains apparent functional contradictions

• Molecular detail resolution:

  • Map functional domains through mutational analysis to determine if different protein regions mediate distinct functions

  • Investigate post-translational modifications that might switch protein function

  • Characterize protein-protein interactions that could redirect pathway involvement

• Integration approaches:

  • Implement Bayesian network analysis to integrate diverse evidence types with appropriate weighting

  • Use protein interaction network topology to contextualize conflicting observations

  • Consider temporal dynamics—apparent contradictions may reflect different phases of a process

• Collaborative resolution:

  • Establish direct collaborations between labs reporting conflicting results

  • Perform side-by-side experiments with standardized protocols and reagents

  • Consider publishing joint papers addressing and resolving contradictions

When contradictions persist despite these approaches, researchers should transparently report conflicting data in publications rather than selectively presenting evidence supporting a preferred hypothesis, and propose specific experiments designed to definitively resolve the conflicts.

What emerging technologies might advance our understanding of YPR064W function and regulation?

Several cutting-edge technologies hold promise for elucidating YPR064W function and regulation:

• CRISPR-based approaches:

  • CRISPRi/CRISPRa for precise modulation of YPR064W expression without genetic modification

  • CRISPR base editing for introducing specific mutations to functional domains

  • CRISPR screening with guide RNA libraries targeting potential genetic interactors

• Single-cell technologies:

  • Single-cell RNA-seq to examine cell-to-cell variability in YPR064W expression

  • Single-cell proteomics to correlate transcript and protein levels across individual cells

  • Live-cell imaging with fluorescent reporters to track dynamic regulation in real-time

• Structural biology advances:

  • Cryo-electron microscopy for determining structures of YPR064W complexes without crystallization

  • Integrative structural biology combining multiple data types (SAXS, XL-MS, NMR)

  • AlphaFold2 and similar AI approaches for predicting structures of YPR064W and interaction partners

• Functional genomics tools:

  • Perturb-seq combining CRISPR perturbations with single-cell RNA-seq

  • Spatial transcriptomics to understand YPR064W expression in colony architectures

  • Temporal-focused techniques like time-resolved RNA-seq after acute perturbations

• Systems biology approaches:

  • Multi-omics integration (transcriptomics, proteomics, metabolomics) to place YPR064W in functional networks

  • Genome-scale metabolic models incorporating YPR064W to predict metabolic impacts

  • Network medicine approaches to understand potential disease relevance in higher eukaryotes

These technologies, particularly when applied in combination, offer unprecedented potential to resolve YPR064W's biological function and regulatory mechanisms.

What are the most promising experimental designs for definitively determining YPR064W function?

The most promising experimental designs for definitive functional characterization of YPR064W combine genetic manipulation, biochemical approaches, and systems-level analysis:

• Comprehensive phenotypic profiling:

  • Generate a clean deletion strain (ypr064wΔ) and measure growth across hundreds of conditions using high-throughput phenotyping

  • Create a strain collection with YPR064W under control of a titratable promoter to assess dosage effects

  • Perform metabolomic profiling of deletion and overexpression strains to identify altered pathways

• Synthetic genetic interaction mapping:

  • Conduct systematic genetic interaction screens by crossing ypr064wΔ with genome-wide deletion collections

  • Perform quantitative analysis of genetic interactions to position YPR064W in functional networks

  • Use chemical-genetic interactions with compounds of known mechanism to probe pathway involvement

• Direct biochemical function determination:

  • Purify recombinant YPR064W and systematically test for enzymatic activities (kinase, phosphatase, transferase)

  • Perform unbiased substrate identification using protein or metabolite arrays

  • Use hydrogen-deuterium exchange mass spectrometry to identify substrate binding regions

• In vivo proximity labeling:

  • Generate YPR064W fusion with TurboID or BioID2 for in vivo proximity labeling

  • Identify proximal proteins and potential substrates through streptavidin pulldown and mass spectrometry

  • Validate key interactions through reciprocal experiments and functional studies

• Structural-functional mapping:

  • Create a comprehensive alanine-scanning library of YPR064W to identify critical residues

  • Correlate structural predictions with functional outcomes of mutations

  • Use structure-guided design of mutations targeting predicted functional sites

These complementary approaches, particularly when implemented in a coordinated research program, offer the greatest potential to definitively establish YPR064W function.

How might comparative genomics across fungal species inform our understanding of YPR064W evolution and function?

Comparative genomics across fungal species provides a powerful framework for understanding YPR064W evolution and inferring function:

• Ortholog identification and analysis:

  • Conduct sensitive sequence-based searches (PSI-BLAST, HMMer) to identify distant YPR064W orthologs

  • Generate comprehensive multiple sequence alignments to identify conserved residues and motifs

  • Construct phylogenetic trees to understand evolutionary history and potential functional divergence

• Synteny analysis:

  • Examine gene neighborhood conservation across fungal species

  • Identify co-evolved gene clusters that might suggest functional relationships

  • Analyze promoter evolution to identify conserved regulatory elements

• Evolutionary rate analysis:

  • Calculate site-specific evolutionary rates to identify functionally constrained regions

  • Perform tests for positive selection to identify adaptively evolving sites

  • Compare evolutionary rates with structurally or functionally characterized proteins

• Correlative presence/absence patterns:

  • Analyze co-occurrence patterns with genes of known function across diverse fungi

  • Identify species that lack YPR064W and examine compensatory mechanisms

  • Correlate presence/absence with specific ecological niches or metabolic capabilities

• Experimental validation across species:

  • Test functional complementation by expressing orthologs from diverse fungi in S. cerevisiae ypr064wΔ

  • Compare phenotypic consequences of ortholog deletion in multiple fungal species

  • Examine if protein localization and interaction partners are conserved across species