Recombinant Saccharomyces cerevisiae Putative uncharacterized protein YPR170W-A (YPR170W-A)

What is YPR170W-A and how is it classified in yeast genomic databases?

YPR170W-A is a putative uncharacterized protein from Saccharomyces cerevisiae (baker's yeast) with a full length of 61 amino acids . The protein has been identified in genomic studies, but its function remains largely unknown. According to genomic classification studies, YPR170W-A has been classified among open reading frames (ORFs) showing similarity to known proteins, despite its uncharacterized status . In some classification schemes, it appears in datasets alongside other yeast proteins of research interest, suggesting potential biological significance despite limited functional characterization . This classification creates an interesting research opportunity as it represents a protein with recognizable structural motifs but unknown biological roles.

What expression patterns have been documented for YPR170W-A?

According to the Saccharomyces Genome Database (SGD), there is currently no documented expression data for YPR170W-A in the standard expression datasets . This absence of expression data presents a significant research challenge and opportunity. Researchers interested in YPR170W-A expression should consider using the SPELL (Serial Pattern of Expression Levels Locator) tool, which can identify genes with similar expression profiles and potentially provide insight through guilt-by-association approaches . The lack of expression data may indicate either technical limitations in current detection methods, expression under specific untested conditions, or genuinely low expression levels that make detection challenging in standard transcriptomic approaches.

What structural information is available for YPR170W-A?

YPR170W-A is a relatively small protein with 61 amino acids in its full-length form . While detailed three-dimensional structural information appears limited in the available literature, recombinant versions of the protein have been produced with histidine tags for purification purposes . The small size of the protein may present challenges for certain structural determination methods, but also provides opportunities for comprehensive analysis through techniques such as nuclear magnetic resonance (NMR) spectroscopy, which is often more suitable for smaller proteins. The protein's small size also suggests potential roles as a regulatory peptide rather than as an enzyme with complex catalytic domains.

How should researchers approach the functional characterization of YPR170W-A?

Functional characterization of YPR170W-A requires a multi-faceted approach given its uncharacterized status. Begin with comparative sequence analysis against known protein domains across species to identify potential functional motifs. For experimental characterization, consider a gene deletion approach measuring phenotypic changes under various stress conditions. Protein interaction studies using techniques such as yeast two-hybrid or co-immunoprecipitation would identify potential binding partners that might suggest function . Integrating these approaches with metabolic profiling and transcriptomic analysis in wild-type versus YPR170W-A mutant strains can provide comprehensive insights into functional roles. As YPR170W-A appears in various protein datasets, correlating its presence with specific experimental conditions may provide functional clues .

What methods are appropriate for distinguishing YPR170W-A from non-coding sequences?

Distinguishing YPR170W-A as a genuine protein-coding gene rather than a non-coding sequence requires rigorous computational and experimental validation. Computationally, examine codon usage bias, conservation patterns across related yeast species, and the presence of characteristic protein-coding sequence features . The protein appears in classification studies of yeast open reading frames (ORFs), where sophisticated algorithms distinguish between coding and non-coding sequences based on characteristic patterns . Experimentally, confirm protein expression through techniques such as Western blotting with specific antibodies or mass spectrometry-based proteomics to detect the translated product. Ribosome profiling provides another powerful approach to determine whether the sequence is actively translated in vivo, offering definitive evidence of protein-coding status.

How can researchers effectively produce and purify recombinant YPR170W-A for functional studies?

Production of recombinant YPR170W-A has been successfully achieved using E. coli expression systems with histidine tag purification strategies . For optimal results, researchers should consider the following methodological approach: design expression constructs with codon optimization for the host system, test multiple fusion tags beyond histidine (such as GST or MBP) to enhance solubility, and evaluate different E. coli strains specialized for difficult-to-express proteins. Purification should employ a multi-step strategy beginning with affinity chromatography using the fusion tag, followed by size exclusion chromatography to ensure homogeneity. For functional studies, both tagged and tag-cleaved versions should be prepared to assess any potential interference from the purification tags on protein function.

What experimental designs are most effective for elucidating YPR170W-A function in the context of cellular pathways?

Elucidating YPR170W-A function within cellular pathways requires sophisticated experimental designs that capture both direct and indirect effects. Implement a systems biology approach combining multiple data layers: Begin with quantitative proteomics using label-free quantification (LFQ) to identify differential protein abundance patterns between wild-type and YPR170W-A deletion mutants under various stress conditions . Design time-course experiments to capture dynamic changes in the proteome following environmental perturbations. Complement proteomics with metabolomic and lipidomic analyses to identify altered metabolic pathways. For genetic interaction mapping, employ synthetic genetic array (SGA) analysis to systematically identify genes that show synthetic interactions with YPR170W-A, thus placing it within functional networks. Integration of these multi-omic datasets using computational modeling will provide comprehensive insight into the protein's role within cellular pathways.

How can researchers resolve contradictory data regarding YPR170W-A classification?

Resolving contradictions in YPR170W-A classification requires a systematic approach to evaluating evidence quality. YPR170W-A presents an interesting case study as it appears in classifications both as a protein with "similarity to known proteins" and as a "putative uncharacterized protein" . To resolve such contradictions, implement the following methodology: First, conduct a comprehensive literature review documenting all classification claims and their supporting evidence. Second, perform independent computational analyses using current algorithms and databases to reassess sequence homology and functional predictions. Third, design definitive experimental validations focusing specifically on the disputed aspects of classification. Finally, consider the possibility that both classifications contain partial truths – the protein may indeed show sequence similarity to known proteins while its specific function remains uncharacterized. Document all analysis steps thoroughly to provide transparency in resolving the contradictions.

What approaches should be used to integrate YPR170W-A research into broader systems biology frameworks?

Integrating YPR170W-A research into systems biology frameworks requires methodologies that connect this individual protein to global cellular processes. Begin by utilizing correlation-based tools like SPELL (Serial Pattern of Expression Levels Locator) to identify genes with similar expression patterns, creating a functional association network . Expand this network through protein interaction screens and genetic interaction mapping. Overlay this network with pathway annotations from databases like KEGG or Reactome to identify enriched biological processes. For computational integration, employ Bayesian network approaches to infer causal relationships between YPR170W-A and other cellular components. Validate key predictions through targeted perturbation experiments where YPR170W-A is deleted or overexpressed followed by global response measurements. This multi-layered approach places YPR170W-A research within the broader context of cellular function rather than as an isolated entity.

What protein detection and quantification methods are most appropriate for YPR170W-A research?

Given YPR170W-A's small size (61 amino acids) and potentially low expression levels, detection and quantification require specialized methodological approaches . For detection, develop highly specific antibodies targeting unique epitopes, validated against recombinant protein and YPR170W-A deletion strains as controls. When antibody development proves challenging, employ epitope tagging strategies (HA, FLAG, or GFP) through genomic integration at the native locus to maintain physiological expression levels. For quantification, targeted proteomics using selected reaction monitoring (SRM) or parallel reaction monitoring (PRM) offer superior sensitivity compared to standard shotgun proteomics approaches. These mass spectrometry methods can detect and quantify proteins at attomole levels. Alternatively, if fluorescent tagging is viable, quantitative microscopy combined with image analysis can provide spatial information alongside quantification. Each approach has specific limitations that should be clearly documented in research reports.

What are the optimal experimental designs for label-free quantification studies involving YPR170W-A?

Label-free quantification (LFQ) studies involving YPR170W-A require careful experimental design to ensure reliable detection and quantification. The minimal experimental design should include at least three biological replicates for each condition being compared, with randomized sample preparation and analysis order to minimize batch effects . Sample preparation should employ methods optimized for small proteins, as standard proteomics workflows may not efficiently capture proteins under 10 kDa. Consider using specialized extraction methods for small proteins, including acid extraction protocols that preferentially isolate small, basic proteins. Data analysis should employ sophisticated normalization methods that account for potential biases in detecting small proteins. When identifying differentially expressed proteins in experiments involving YPR170W-A, use statistical methods that properly control for multiple testing while maintaining sensitivity for detecting subtle changes in protein abundance patterns.

How should functional annotation inconsistencies for YPR170W-A be addressed in research databases?

Addressing functional annotation inconsistencies for YPR170W-A in research databases requires a systematic approach to evidence evaluation and reporting. Researchers should first catalog all existing annotations across major databases (SGD, UniProt, NCBI, etc.) and classify the supporting evidence according to type (computational prediction, low-throughput experimental, high-throughput experimental, etc.) and strength . For annotations based solely on computational predictions, perform updated analyses using current algorithms and databases to verify continued support. For conflicting experimental evidence, evaluate methodological differences that might explain discrepancies. When publishing new functional insights, explicitly address how the findings relate to existing annotations, confirming or refuting them with clear evidence descriptions. Consider contributing directly to community annotation efforts by submitting evidence-based annotation updates to relevant databases, ensuring consistent propagation of validated information throughout the research ecosystem.

What technological limitations currently hinder comprehensive characterization of YPR170W-A?

Comprehensive characterization of YPR170W-A faces several technological limitations that require innovative approaches. The protein's small size (61 amino acids) presents challenges for standard proteomics workflows, which often inefficiently capture and identify small proteins . Detection sensitivity remains a significant obstacle, particularly if expression levels are naturally low or condition-specific, as suggested by the lack of expression data in standard datasets . Structural characterization faces difficulties with conventional crystallography approaches if the protein is intrinsically disordered or requires interaction partners for stability. Additionally, functional redundancy in the yeast genome may mask phenotypes in single-gene deletion studies. To overcome these limitations, researchers should consider developing specialized protocols for small protein enrichment, employing more sensitive mass spectrometry approaches with targeted methods, utilizing NMR for structural studies, and designing experiments that account for potential functional redundancy through combination with other genetic modifications.

How can researchers reconcile the classification of YPR170W-A as both "uncharacterized" and having "similarity to known proteins"?

The seemingly contradictory classification of YPR170W-A as both "uncharacterized" and having "similarity to known proteins" represents an intriguing research puzzle . This paradox can be reconciled through careful distinction between sequence similarity and functional characterization. YPR170W-A may contain sequence motifs or domains that are structurally similar to characterized proteins, while its specific biochemical function, biological role, and regulation remain undefined. To address this scientifically, researchers should: (1) perform detailed domain mapping to identify precisely which regions show similarity to known proteins; (2) determine whether the similar regions correspond to functional domains with known biochemical activities; (3) assess whether the similar proteins themselves are well-characterized functionally or simply structurally defined; and (4) design experiments that directly test whether YPR170W-A possesses similar biochemical activities to its sequence homologs. This approach transforms the apparent contradiction into a structured research program.

What emerging technologies hold the greatest promise for advancing YPR170W-A research?

Several emerging technologies offer unprecedented opportunities for advancing YPR170W-A research beyond current limitations. CRISPR-based technologies enable precise genome editing for creating subtle mutations rather than complete deletions, allowing functional domain mapping without eliminating the entire protein. Single-cell multi-omics approaches can reveal cell-to-cell variation in YPR170W-A expression and function that might be masked in population averages. Proximity labeling techniques such as BioID or APEX can map the protein's immediate interaction neighborhood without requiring stable interactions. For structural studies, cryo-electron microscopy advances now enable structural determination of smaller proteins and complexes. Computational approaches leveraging artificial intelligence, particularly AlphaFold2 and similar tools, can predict protein structures with increasing accuracy, potentially providing structural insights even without experimental determination. Integration of multi-omics data through advanced machine learning approaches can reveal functional associations not apparent in single data types. Researchers should consider how these emerging technologies might bypass current limitations in understanding YPR170W-A function.