Recombinant Saccharomyces cerevisiae Uncharacterized protein YPR170W-B (YPR170W-B)

What is YPR170W-B and what are its basic structural features?

YPR170W-B is an uncharacterized protein from Saccharomyces cerevisiae (baker's yeast) with a UniProt accession number P0C5R9. It consists of 85 amino acids with the following sequence: MRPVVSTGKAWCCTVLSAFGVVILSVIAHLFNTNHESFVGSINDPEDGPAVAHTVYLAALLVYLVFFVFCGFQVYLARRKPSIELR . The protein contains hydrophobic regions suggesting potential membrane association, with characteristic features including cysteine residues that may participate in disulfide bond formation and potential transmembrane domains.

The amino acid composition analysis reveals several notable features:

• High content of hydrophobic amino acids (A, V, L, I, F)

• Multiple aromatic residues (F, Y, W)

• Presence of charged amino acids (R, K) predominantly in the C-terminal region

• Several cysteines (C) potentially involved in structural stabilization

These characteristics suggest YPR170W-B may function as a membrane-associated protein, though further structural studies are needed to confirm this hypothesis.

How can I effectively express and purify recombinant YPR170W-B for research?

For optimal expression and purification of recombinant YPR170W-B:

• Expression System: Use E. coli as the expression host, as demonstrated in available recombinant products . BL21(DE3) or Rosetta strains are recommended for small membrane-associated proteins.

• Expression Construct: Design a construct with an N-terminal His-tag followed by the full-length protein (amino acids 1-85). Include a TEV protease cleavage site if tag removal is necessary for downstream applications.

• Expression Conditions:

  • Culture in LB medium at 37°C until OD600 reaches 0.6-0.8

  • Induce with 0.1-0.5 mM IPTG

  • Lower temperature to 16-18°C post-induction

  • Express for 16-18 hours

• Purification Protocol:

  • Lyse cells in Tris/PBS-based buffer (pH 8.0) containing 6% trehalose and protease inhibitors

  • Use immobilized metal affinity chromatography (IMAC) with Ni-NTA resin

  • Apply stringent washing with increasing imidazole concentrations (10 mM, 20 mM, 50 mM)

  • Elute with 250-300 mM imidazole

  • Optional: Size exclusion chromatography for higher purity

• Storage: Lyophilize the purified protein or store in Tris/PBS buffer with 50% glycerol at -80°C to maintain stability .

This methodology yields protein with greater than 90% purity as determined by SDS-PAGE, suitable for most research applications .

What are the optimal storage conditions for YPR170W-B to maintain its stability?

To maintain the stability and activity of purified YPR170W-B:

• Short-term Storage (1-2 weeks):

  • Store working aliquots at 4°C in Tris/PBS-based buffer (pH 8.0)

  • Add 6% trehalose as a stabilizing agent to prevent protein aggregation

• Long-term Storage:

  • Store at -20°C/-80°C in small aliquots to minimize freeze-thaw cycles

  • Add 5-50% glycerol (optimal: 50%) as a cryoprotectant

  • Alternatively, lyophilize the protein and store the powder at -80°C

• Reconstitution Protocol:

  • Briefly centrifuge vials before opening

  • Reconstitute lyophilized protein in deionized sterile water to 0.1-1.0 mg/mL

  • Allow complete dissolution before use

  • For functional assays, reconstitute in buffers mimicking physiological conditions

• Stability Considerations:

  • Avoid repeated freeze-thaw cycles which significantly reduce protein activity

  • Monitor protein integrity via SDS-PAGE after extended storage periods

  • If storing aliquots in solution, flash freeze in liquid nitrogen before transferring to -80°C

These storage protocols are essential to maintain protein integrity for subsequent functional and structural studies.

What RNA interactions have been predicted for YPR170W-B and how reliable are these predictions?

YPR170W-B shows predicted interactions with multiple RNA transcripts based on catRAPID prediction scores:

The highest prediction score (13.71) is with NSR1 RNA, a transcript encoding a nucleolar protein involved in pre-rRNA processing and ribosome biogenesis . These predictions are based on computational algorithms that analyze physicochemical properties and binding propensities.

Regarding reliability:

• The negative z-scores (ranging from -0.21 to -0.76) indicate moderate confidence in these predictions

• No experimentally detected interactions (via RIP-Chip) are currently reported in the database

• The prediction scores are relatively modest compared to well-established RNA-binding proteins

To validate these predictions:

• RNA immunoprecipitation (RIP) followed by qPCR for specific targets

• Cross-linking immunoprecipitation (CLIP) to identify direct binding sites

• In vitro binding assays with purified components

These computational predictions provide valuable starting points for experimental validation, particularly focusing on NSR1 and MDJ1 RNAs which show the highest prediction scores .

How can I design RNA-binding experiments to validate YPR170W-B interactions?

To validate the predicted RNA interactions of YPR170W-B, a multi-tiered experimental approach is recommended:

• In Vitro Binding Assays:

  • RNA Electrophoretic Mobility Shift Assay (EMSA)

    • Synthesize RNA transcripts for top predicted partners (NSR1, MDJ1)

    • Incubate with recombinant YPR170W-B at increasing concentrations

    • Analyze via native PAGE to detect mobility shifts

  • Filter Binding Assays

    • Use radiolabeled or fluorescently labeled RNA

    • Determine binding affinity (Kd) for quantitative comparison

• Cellular Validation:

  • RNA Immunoprecipitation (RIP)

    • Express tagged YPR170W-B in yeast

    • Crosslink protein-RNA complexes

    • Immunoprecipitate using anti-tag antibodies

    • Identify bound RNAs via RT-PCR or RNA-seq

  • CLIP-seq (Cross-linking and Immunoprecipitation followed by sequencing)

    • Provides single-nucleotide resolution of binding sites

    • Can identify sequence or structural motifs recognized by YPR170W-B

• Experimental Design for 96-well Format Binding Assays:

  • Layout considerations for testing multiple RNA candidates:

    • Include technical replicates (minimum triplicate)

    • Incorporate concentration gradients

    • Include positive controls (known RNA-binding proteins)

    • Include negative controls (non-binding proteins of similar size)

  • Fluorescence Polarization in 96-well format:

    • Use fluorescently labeled RNA oligonucleotides

    • Monitor binding through changes in polarization signal

    • Can be scaled for multiple candidates

• Data Validation:

  • Perform competition assays with unlabeled RNA

  • Test binding specificity using mutated RNA sequences

  • Validate findings in vivo using genetic approaches (e.g., yeast mutants)

Each experimental approach has advantages and limitations, so combining multiple methods provides the most robust validation of predicted RNA interactions.

What is the significance of YPR170W-B's predicted interaction with NSR1 RNA?

The predicted interaction between YPR170W-B and NSR1 RNA (prediction score: 13.71) is particularly noteworthy and warrants detailed investigation . NSR1 encodes a nucleolar protein that plays crucial roles in ribosome biogenesis and pre-rRNA processing in yeast.

Potential Significance:

• Regulatory Implications:

  • YPR170W-B may participate in post-transcriptional regulation of NSR1 expression

  • This interaction could represent a feedback loop in ribosome biogenesis pathways

  • The regulatory mechanism might involve:

    • Altered NSR1 mRNA stability

    • Changed translational efficiency

    • Subcellular localization control

• Functional Context:

  • NSR1 protein functions in nucleolar processes and ribosome assembly

  • YPR170W-B's interaction suggests potential roles in:

    • Stress response pathways affecting ribosome biogenesis

    • Cell growth regulation mechanisms

    • RNA quality control pathways

• Evolutionary Perspective:

  • Conservation analysis of this interaction across related yeast species could reveal evolutionary importance

  • The specificity of this interaction among the predicted RNA partners suggests functional specialization

• Experimental Follow-up Approaches:

  • Genetic interaction studies between YPR170W-B and NSR1

  • Effects of YPR170W-B overexpression/deletion on NSR1 RNA levels and localization

  • Co-localization studies using fluorescence microscopy

  • Analysis of NSR1 expression in YPR170W-B mutant strains

The relatively high prediction score compared to other RNA interactions suggests this might be a biologically relevant interaction, potentially linking YPR170W-B to nucleolar functions and the fundamental process of ribosome biogenesis in yeast.

How should I design a comprehensive study to elucidate YPR170W-B function in yeast?

To elucidate YPR170W-B function, a multi-faceted approach combining genetic, biochemical, and cell biological techniques is recommended:

• Genetic Approaches:

  • CRISPR/Cas9 gene editing to generate:

    • Complete knockout strains

    • Point mutations in key residues (based on conservation analysis)

    • Tagged versions for localization studies

  • Phenotypic Characterization:

    • Growth curves under various conditions (temperature, pH, osmotic stress)

    • Sensitivity to RNA metabolism inhibitors

    • Ribosome profile analysis

• Protein Interaction Network:

  • Yeast two-hybrid screening

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

  • Proximity labeling approaches (BioID or APEX)

  • Co-immunoprecipitation with predicted RNA-binding partners

• RNA-focused Experiments:

  • Transcriptome analysis of knockout strains (RNA-seq)

  • RNA immunoprecipitation followed by sequencing (RIP-seq)

  • Structure-function analysis of RNA-binding domains

  • Testing interactions with top predicted RNA partners (NSR1, MDJ1)

• Cellular Localization:

  • Fluorescence microscopy with GFP-tagged YPR170W-B

  • Co-localization with RNA granule markers

  • Subcellular fractionation followed by western blotting

  • Response to cellular stress conditions

• Evolutionary Analysis:

  • Comparative genomics across yeast species

  • Analysis of selection pressure on coding sequence

  • Identification of conserved functional domains

• 96-Well Plate Experimental Design:

  • For high-throughput phenotypic screening:

    • Arrange strains systematically (WT, mutants, controls)

    • Include technical and biological replicates

    • Randomize plate positions to minimize edge effects

    • Include gradient of treatment conditions across plates

This comprehensive approach integrates multiple lines of evidence to develop a cohesive model of YPR170W-B function, with special attention to its potential role in RNA processing pathways suggested by predicted interactions.

What controls and validation methods are essential for studying YPR170W-B in vitro?

When conducting in vitro studies with recombinant YPR170W-B, rigorous controls and validation methods are essential to ensure reliable and reproducible results:

• Protein Quality Controls:

  • Purity Assessment:

    • SDS-PAGE with Coomassie staining (>90% purity recommended)

    • Mass spectrometry verification of intact protein mass

    • N-terminal sequencing to confirm identity

  • Functional Validation:

    • Circular dichroism (CD) to confirm proper folding

    • Size exclusion chromatography to assess oligomeric state

    • Dynamic light scattering to detect aggregation

• RNA Interaction Controls:

  • Positive Controls:

    • Known RNA-binding proteins with similar size/properties

    • Synthetic positive control constructs with verified binding domains

  • Negative Controls:

    • Non-relevant proteins of similar size (e.g., GFP)

    • Heat-denatured YPR170W-B

    • RNase-treated samples

    • Scrambled RNA sequences

• Buffer Optimization:

  • Systematic testing of:

    • pH range (6.5-8.5)

    • Salt concentrations (50-300 mM)

    • Reducing agent requirements

    • Divalent cation effects (Mg²⁺, Mn²⁺)

• Binding Assay Validation:

  • Technical Validation:

    • Triplicate measurements (minimum)

    • Z-factor determination for high-throughput assays

    • Signal-to-noise ratio optimization

    • Determining limits of detection and quantification

  • Orthogonal Confirmation:

    • Validate interactions using multiple techniques (EMSA, filter binding, ITC)

    • Competition assays with unlabeled RNA

    • Dose-response curves for quantitative assessment

• Experimental Design for 96-Well Format:

  • Plate Layout Considerations:

    • Randomized block design to control for position effects

    • Edge wells filled with buffer only (to control for evaporation effects)

    • Standard curves on each plate

    • Inter-plate calibration controls

These controls and validation methods ensure that observations related to YPR170W-B are genuine, reproducible, and free from experimental artifacts or bias.

How can I optimize protocols for studying membrane-associated properties of YPR170W-B?

Based on its amino acid sequence and hydrophobic regions, YPR170W-B likely possesses membrane-associated properties . The following specialized protocols are recommended for studying these characteristics:

• Membrane Extraction and Fractionation:

  • Sequential Extraction Protocol:

    • Prepare spheroplasts from yeast cells expressing tagged YPR170W-B

    • Extract with increasing detergent concentrations (0.1% to 1%)

    • Test multiple detergent classes (nonionic, zwitterionic, ionic)

    • Analyze fractions by western blotting

  • Optimal Detergents for YPR170W-B:

    • Primary recommendations: DDM, CHAPS, or digitonin

    • Secondary options: Triton X-100, NP-40

    • Concentration optimization needed for each detergent

• Membrane Reconstitution:

  • Liposome Preparation:

    • Utilize yeast lipid extracts or defined lipid mixtures

    • Prepare liposomes via extrusion through 100 nm filters

    • Incorporate purified YPR170W-B using dialysis or detergent removal

  • Proteoliposome Characterization:

    • Confirm orientation using protease protection assays

    • Assess protein:lipid ratio via quantitative western blotting

    • Verify homogeneity using dynamic light scattering

• Transmembrane Topology Analysis:

  • Cysteine Scanning Mutagenesis:

    • Create single-cysteine mutants throughout YPR170W-B sequence

    • Test accessibility to membrane-impermeable sulfhydryl reagents

    • Analyze results to generate topology model

  • Fluorescence Quenching Approaches:

    • Introduce tryptophan residues at strategic positions

    • Measure fluorescence quenching by water-soluble quenchers

    • Determine membrane-embedded regions

• Lipid Interaction Studies:

  • Lipid Blot Assays:

    • Test binding to PIP strips containing various phospholipids

    • Identify specific lipid binding preferences

  • Surface Plasmon Resonance:

    • Immobilize lipid bilayers on sensor chips

    • Measure YPR170W-B association/dissociation kinetics

    • Determine lipid composition effects on binding

• Microscopy Validation:

  • Super-resolution Microscopy:

    • Utilize PALM or STORM for nanoscale localization

    • Co-visualize with known membrane markers

    • Quantify spatial distribution relative to cellular compartments

• Experimental Considerations:

  • Temperature sensitivity (conduct experiments at 25-30°C)

  • Stabilizing agents (glycerol, specific lipids)

  • Gentle handling to prevent aggregation

These specialized protocols address the challenges of working with membrane-associated proteins and will provide crucial insights into the structural and functional properties of YPR170W-B.

How should RNA-binding data for YPR170W-B be analyzed and interpreted?

When analyzing RNA-binding data for YPR170W-B, a systematic analytical framework should be employed:

• Quantitative Binding Analysis:

  • Binding Curve Fitting:

    • Apply appropriate binding models (simple, cooperative, or competitive)

    • Calculate dissociation constants (Kd) using nonlinear regression

    • Compare affinity for different RNA targets (particularly NSR1 and MDJ1)

    • Determine binding stoichiometry through Scatchard analysis

  • Statistical Validation:

    • Calculate 95% confidence intervals for Kd values

    • Perform goodness-of-fit tests (R² and residual analysis)

    • Conduct ANOVA for comparing multiple RNA targets

    • Apply multiple testing correction for high-throughput data

• Sequence Motif Analysis:

  • For identified binding sites:

    • Apply MEME, HOMER, or similar algorithms for motif discovery

    • Compare identified motifs to known RNA-binding protein recognition sites

    • Assess motif conservation across related yeast species

    • Create position weight matrices for binding site prediction

• Structure-Function Correlation:

  • Secondary Structure Analysis:

    • Determine if YPR170W-B preferentially binds structured or unstructured regions

    • Use RNA structure prediction tools (RNAfold, Mfold) to analyze binding regions

    • Test binding to mutated structures to confirm structural requirements

  • Mapping Interaction Domains:

    • Identify critical amino acids for RNA binding through mutagenesis

    • Correlate binding properties with structural features of the protein

    • Generate structure-function relationship models

• Comparative Analysis with Prediction Data:

  • Create correlation plots between:

    • Experimental binding affinities

    • Computational prediction scores from catRAPID

    • RNA transcript length effects

  • Visualization Techniques:

    • Heatmaps of binding affinities across RNA targets

    • Principal component analysis of binding properties

    • Network graphs of protein-RNA interactions

• Integration with Biological Context:

  • Pathway Analysis:

    • Map interacting RNAs to biological processes and pathways

    • Identify potential regulatory networks

    • Connect to yeast stress response or growth regulation pathways

This analytical framework enables robust interpretation of RNA-binding data and facilitates the development of testable hypotheses about YPR170W-B's functional role in yeast biology.

What bioinformatic approaches can help predict the function of YPR170W-B?

Given YPR170W-B's uncharacterized status, comprehensive bioinformatic analyses can provide crucial insights into its potential functions:

• Sequence-Based Analysis:

  • Homology Detection:

    • PSI-BLAST searches against diverse databases

    • HHpred for remote homology detection

    • HMMER searches against domain databases

  • Motif Identification:

    • PROSITE, PRINTS, or InterPro scans for functional motifs

    • Analysis of the amino acid sequence for RNA-binding domains

    • Disorder prediction (PONDR, IUPred) to identify flexible binding regions

• Structural Prediction and Analysis:

  • 3D Structure Prediction:

    • AlphaFold2 or RoseTTAFold for ab initio structure prediction

    • Structure-based function annotation via ProFunc or COFACTOR

    • Identification of potential binding pockets or interfaces

  • Transmembrane Topology:

    • TMHMM, TopPred, or MEMSAT for transmembrane helix prediction

    • SignalP for signal peptide detection

    • Hydrophobicity analysis (Kyte-Doolittle plots)

• RNA Interaction Prediction:

  • Binding Site Prediction:

    • catRAPID for protein-RNA interaction propensities

    • BindN+ or RNABindRPlus for RNA-binding residue prediction

    • Computational docking with predicted RNA structures

  • Correlation Analysis:

    • Compare prediction scores across the transcriptome

    • Identify RNA features that correlate with high binding scores

• Genomic Context Analysis:

  • Conservation Analysis:

    • Phylogenetic profiling across fungal species

    • Synteny analysis of genomic neighborhood

    • Selection pressure analysis (dN/dS ratios)

  • Co-expression Networks:

    • Identify genes co-expressed with YPR170W-B under various conditions

    • Construct co-expression networks to predict functional associations

    • Integrate with protein-protein interaction data

• Functional Association Prediction:

  • Gene Ontology Enrichment:

    • Analyze GO terms of predicted interacting partners

    • Predict cellular component, biological process, and molecular function

  • Integrative Approaches:

    • STRING database for functional protein association networks

    • FunCoup for functional coupling analysis

    • Bayesian integration of multiple data types

By applying these complementary bioinformatic approaches, researchers can generate testable hypotheses about YPR170W-B's function, particularly focusing on its potential role in RNA metabolism suggested by its predicted interactions with transcripts like NSR1 .

How can I resolve contradictory experimental results when studying YPR170W-B?

When faced with contradictory experimental results in YPR170W-B research, a systematic troubleshooting and reconciliation approach is essential:

By systematically addressing contradictions through this framework, researchers can resolve discrepancies and develop a more nuanced understanding of YPR170W-B's function, particularly in relation to its predicted RNA interactions with transcripts like NSR1 .

What are the most promising research directions for further characterization of YPR170W-B?

Based on current knowledge and preliminary data, several high-priority research directions emerge for YPR170W-B characterization:

• RNA Interactome Mapping:

  • Comprehensive Binding Profile:

    • Perform CLIP-seq to identify all RNA targets in vivo

    • Compare experimental results with computational predictions

    • Focus on validating the interaction with NSR1 RNA (highest prediction score)

    • Determine if binding is direct or mediated through protein complexes

  • Regulatory Impact Assessment:

    • Analyze effects of YPR170W-B depletion/overexpression on transcript levels

    • Determine if binding affects RNA stability, localization, or translation

    • Investigate condition-dependent regulation of RNA targets

• Structural Biology Approaches:

  • High-Resolution Structure Determination:

    • Cryo-EM of YPR170W-B in complex with RNA targets

    • NMR structure of soluble domains

    • X-ray crystallography of the protein or its domains

    • Validate membrane association predictions from sequence analysis

  • Structure-Function Studies:

    • Map RNA-binding interface through mutagenesis and binding assays

    • Identify residues critical for membrane association

    • Determine structural changes upon RNA binding

• Cellular Function Elucidation:

  • Genetic Interaction Mapping:

    • Synthetic genetic array (SGA) analysis with YPR170W-B deletion

    • CRISPR interference screens to identify genetic dependencies

    • Suppressor screens to identify functional pathways

  • Stress Response Roles:

    • Test response to various cellular stresses (oxidative, heat, nutrient)

    • Examine localization changes under stress conditions

    • Investigate potential roles in RNA granule formation

• Evolutionary Studies:

  • Comparative Genomics:

    • Identify orthologs across fungal species

    • Analyze sequence conservation patterns

    • Study functional divergence in different yeast lineages

  • Horizontal Gene Transfer Investigation:

    • Examine potential viral or transposon origins

    • Analyze integration patterns in the genome

    • Study potential domestication of mobile genetic elements

• Systems Biology Integration:

  • Multi-omics Integration:

    • Correlate transcriptome, proteome, and RNA interactome data

    • Build predictive models of YPR170W-B function

    • Place YPR170W-B in the context of yeast regulatory networks

  • Translational Relevance:

    • Investigate conservation of function in pathogenic fungi

    • Explore potential as an antifungal target

    • Examine biotechnological applications in yeast engineering

These research directions leverage the predicted RNA interactions, particularly with NSR1 , and the recombinant protein's availability to systematically unveil YPR170W-B's biological functions and significance.

How can high-throughput techniques be applied to study YPR170W-B functions?

High-throughput techniques offer powerful approaches to elucidate YPR170W-B functions efficiently:

• Next-Generation Sequencing Applications:

  • Enhanced CLIP-seq Approaches:

    • iCLIP or eCLIP for single-nucleotide resolution of binding sites

    • PAR-CLIP to identify direct contacts using photoreactive nucleosides

    • CRAC (crosslinking and analysis of cDNAs) optimized for yeast

  • Transcriptome-wide Analyses:

    • RNA-seq of deletion/overexpression strains under multiple conditions

    • Ribosome profiling to assess translational impacts

    • SHAPE-MaP to examine RNA structural changes upon binding

• Proteomics-Based Methods:

  • Interaction Proteomics:

    • BioID proximity labeling to identify neighboring proteins

    • APEX2 proximity labeling for temporal interaction dynamics

    • Quantitative AP-MS with TMT labeling for condition-dependent interactions

  • Post-translational Modification Mapping:

    • Phosphoproteomics to identify regulatory modifications

    • Ubiquitylome analysis to assess protein stability regulation

    • Crosslink mass spectrometry to map interaction interfaces

• High-Content Screening:

  • Phenotypic Profiling:

    • Chemical-genetic interaction screening (chemogenomics)

    • Yeast knockout collection screening for synthetic interactions

    • Morphological profiling using automated microscopy

  • 96-Well Format Optimization:

    • Design randomized block layouts to control position effects

    • Implement multiple condition gradients per plate

    • Include technical and biological replicates with appropriate spacing

    • Utilize plate reader-compatible assays for RNA binding

• Pooled Genetic Screens:

  • CRISPR-Based Approaches:

    • CRISPRi screens to identify genetic dependencies

    • CRISPRa screens to identify suppressor pathways

    • Base editing screens for structure-function analysis

  • Mutagenesis Approaches:

    • Deep mutational scanning of YPR170W-B

    • Saturation mutagenesis of predicted RNA-binding regions

    • Barcoded library screening for functional variants

• Computational Integration:

  • Machine Learning Applications:

    • Develop predictive models of binding specificity

    • Pattern recognition in high-dimensional phenotypic data

    • Network inference from multi-omics datasets

  • Data Visualization and Analysis:

    • Interactive visualization of protein-RNA interaction networks

    • Multi-parameter analysis of screening results

    • Clustering approaches to identify functional groups

These high-throughput approaches can be implemented in well-designed 96-well format experiments , enabling systematic investigation of YPR170W-B's functions, particularly its interactions with predicted RNA partners like NSR1 , while maintaining experimental rigor through appropriate controls and replication.