Recombinant Schizosaccharomyces pombe Uncharacterized protein C14C4.10c (SPAC14C4.10c)

What is the Schizosaccharomyces pombe Uncharacterized protein C14C4.10c?

The Uncharacterized protein C14C4.10c is a protein encoded by the SPAC14C4.10c gene in Schizosaccharomyces pombe 972h- (fission yeast). It is classified as "uncharacterized" because its biological function has not been fully determined through experimental validation. The protein consists of 534 amino acids and is identified by the UniProt ID O13717 . While its precise role remains unknown, the protein sequence data is available in public databases, enabling researchers to make predictions about its potential functions using comparative genomics and bioinformatics approaches. Structural predictions suggest it may be a membrane protein, but definitive characterization requires experimental evidence from biochemical and genetic studies.

What are the key identifiers and genomic information for SPAC14C4.10c?

SPAC14C4.10c can be accessed across multiple databases using the following identifiers:

The genomic information for this protein indicates it contains two introns in its coding sequence, which is consistent with the eukaryotic gene structure typically found in S. pombe. Researchers should be aware that proper splicing verification is essential when working with recombinant expression, as the gene structure contains typical 5′- and 3′-splice consensus elements (GTAAGTA and TAG) . When designing experiments with this gene, these introns must be considered, especially when expressing the protein in prokaryotic systems that lack splicing machinery.

What expression systems are suitable for producing recombinant SPAC14C4.10c?

Multiple expression systems can be employed for the recombinant production of SPAC14C4.10c, each with distinct advantages and considerations:

What fusion tags are recommended for SPAC14C4.10c expression and purification?

Several fusion tags can enhance the expression, solubility, and purification of recombinant SPAC14C4.10c:

The choice of tag should be guided by the downstream applications and the protein's characteristics . For instance, if SPAC14C4.10c proves difficult to express in soluble form, MBP or TrxA tags may be beneficial. For localization studies, GFP fusion would be advantageous. Multiple constructs with different tags should be tested in parallel to determine optimal expression conditions. Researchers should also consider the potential impact of the tag on protein function and include appropriate controls with tag-cleaved protein in functional assays.

How should researchers optimize purification protocols for recombinant SPAC14C4.10c?

Purification of recombinant SPAC14C4.10c requires a strategic approach based on protein characteristics and fusion tags:

• Initial assessment: Begin with small-scale expression trials to determine optimal conditions (temperature, induction time, inducer concentration).

• Solubility screening: Test various lysis buffers with different detergents if the protein has predicted membrane domains.

• Purification strategy development:

  • For His-tagged constructs: Use IMAC (Immobilized Metal Affinity Chromatography) with gradient elution

  • For GST-tagged constructs: Apply GSH-agarose affinity chromatography

  • For FLAG-tagged constructs: Utilize anti-FLAG immunoaffinity chromatography

• Secondary purification: Implement size exclusion chromatography (SEC) or ion exchange chromatography for higher purity.

• Quality control: Verify purity by SDS-PAGE and protein identity by Western blot or mass spectrometry .

For membrane proteins, special considerations include using mild detergents (DDM, CHAPS) throughout purification. Protein purity should be determined by SDS-PAGE, and quantity by Bradford/BCA/A280 assays . Researchers should optimize buffer conditions (pH, salt concentration, additives) to maintain protein stability and function throughout purification. Additionally, if the protein tends to aggregate, the addition of glycerol (5-10%) or specific stabilizing agents may improve yield and quality.

How can researchers design experiments to characterize the function of SPAC14C4.10c?

A systematic approach to characterizing SPAC14C4.10c function could include:

• Bioinformatic analysis:

  • Sequence homology searches against characterized proteins

  • Domain and motif identification

  • Secondary structure prediction

  • Evolutionary conservation analysis

• Genetic approaches:

  • Gene knockout/knockdown studies in S. pombe

  • Phenotypic analysis of mutant strains

  • Complementation studies

  • Synthetic lethality screens

• Biochemical characterization:

  • Subcellular localization using GFP-fusion or immunostaining

  • Protein-protein interaction studies (Y2H, BioID, co-IP)

  • Post-translational modification analysis

  • Enzymatic activity assays based on bioinformatic predictions

• High-throughput approaches:

  • RNA-seq to identify genes affected by SPAC14C4.10c deletion

  • Proteomics to identify interacting partners

  • Metabolomics to identify affected metabolic pathways

This multi-faceted approach, combining computational predictions with experimental validation, increases the likelihood of successfully determining the protein's function . Researchers should develop a hypothesis-driven experimental plan while remaining open to unexpected findings. Incorporating the scientific process framework, as outlined in student-scientist project models, can help maintain methodological rigor throughout the characterization process .

What approaches can identify potential binding partners of SPAC14C4.10c?

Several complementary techniques can help identify potential binding partners:

These approaches should be used in combination for cross-validation . For membrane proteins like SPAC14C4.10c (based on sequence analysis), specialized techniques such as split-ubiquitin Y2H or MYTH (membrane yeast two-hybrid) may be more appropriate. Researchers should validate identified interactions using reciprocal co-immunoprecipitation or functional assays. Computational predictions of protein-protein interactions based on co-expression, co-evolution, or structural modeling can help prioritize candidates for experimental validation.

How can CRISPR-Cas9 gene editing be applied to study SPAC14C4.10c function?

CRISPR-Cas9 provides powerful approaches for studying SPAC14C4.10c function in S. pombe:

• Complete gene knockout:

  • Design sgRNAs targeting the 5' region of the coding sequence

  • Include a selection marker for efficient screening

  • Create a clean deletion by removing the entire coding sequence

• Domain-specific mutagenesis:

  • Introduce point mutations in predicted functional domains

  • Use homology-directed repair with donor templates containing desired mutations

  • Generate a panel of mutants affecting different protein regions

• Endogenous tagging:

  • C- or N-terminal fusion with fluorescent proteins for localization studies

  • Addition of affinity tags for interaction studies

  • Introduction of inducible degrons for temporal control of protein levels

• CRISPRi for conditional knockdown:

  • Use catalytically dead Cas9 (dCas9) fused to repressors

  • Enable titratable repression to study dosage effects

  • Create conditional phenotypes in essential genes

For S. pombe specifically, researchers should optimize transformation protocols and sgRNA design based on S. pombe codon usage and PAM preferences . Phenotypic analysis of CRISPR-edited strains should include growth rates under various conditions, cell morphology, cell cycle progression, and stress responses. Complementation with wild-type protein can confirm phenotype specificity, while rescue experiments with orthologs from other species can provide evolutionary insights.

How should researchers integrate -omics data to infer SPAC14C4.10c function?

Multi-omics data integration provides a comprehensive approach to inferring SPAC14C4.10c function:

• Transcriptomics integration:

  • Analyze differential gene expression in knockout/knockdown cells

  • Identify co-expressed genes across conditions

  • Compare expression patterns with genes of known function

• Proteomics integration:

  • Map protein-protein interaction networks

  • Identify post-translational modifications

  • Analyze protein abundance changes in different conditions

• Metabolomics integration:

  • Detect metabolic changes in mutant strains

  • Identify accumulated or depleted metabolites

  • Map affected metabolic pathways

• Integration strategies:

  • Pathway enrichment analysis across multiple -omics datasets

  • Network analysis to identify functional modules

  • Machine learning approaches to identify patterns across datasets

Researchers should implement data integration tools like Cytoscape, DAVID, or specialized multi-omics platforms to visualize and analyze relationships across datasets . When interpreting results, consideration should be given to both direct and indirect effects of SPAC14C4.10c perturbation. Time-course experiments can help distinguish primary from secondary effects. Validation of key findings using targeted approaches remains essential despite the comprehensive nature of -omics data.

How can researchers predict potential functions of SPAC14C4.10c using bioinformatics?

Bioinformatic approaches offer valuable insights into potential functions of uncharacterized proteins:

• Sequence-based analysis:

  • PSI-BLAST for detecting distant homologs

  • HMMER for identifying conserved domains

  • PROSITE for motif recognition

  • Disorder prediction (IUPred, PONDR)

• Structure-based prediction:

  • Ab initio modeling (Rosetta, QUARK)

  • Homology modeling (SWISS-MODEL, Phyre2)

  • AlphaFold2 for accurate structure prediction

  • Active site prediction based on structural features

• Evolutionary analysis:

  • Phylogenetic profiling to identify co-evolving genes

  • Evolutionary rate analysis to identify functional constraints

  • Synteny analysis to detect conserved genomic context

• Functional inference methods:

  • Gene Ontology enrichment of similar proteins

  • Protein-protein interaction network analysis

  • Text mining of scientific literature

  • Integrated function prediction platforms (SIFTER, FunFams)

When applying these methods to SPAC14C4.10c, researchers should consider that the protein's uncharacterized status may indicate either unique functions or distant relationships to known proteins . Predictions should be treated as hypotheses to guide experimental design rather than definitive functional assignments. Confidence scores provided by prediction algorithms should be carefully evaluated, and consensus approaches using multiple tools often yield more reliable predictions.

How should researchers interpret contradictory data regarding SPAC14C4.10c function?

When faced with contradictory data about SPAC14C4.10c function, researchers should:

• Evaluate methodological differences:

  • Compare experimental conditions (media, temperature, strain backgrounds)

  • Assess technique limitations and specificity

  • Examine statistical power and reproducibility

• Consider biological complexity:

  • Protein multifunctionality and context-dependent functions

  • Redundancy and compensatory mechanisms

  • Indirect effects versus direct functions

• Reconciliation strategies:

  • Design experiments to directly test competing hypotheses

  • Implement orthogonal techniques to validate findings

  • Control for strain background effects through backcrossing

  • Perform epistasis analysis with related genes

• Documentation and reporting:

  • Transparently report contradictory findings

  • Discuss possible explanations for discrepancies

  • Present evidence supporting and opposing each interpretation

When working with uncharacterized proteins like SPAC14C4.10c, contradictions often reflect incomplete understanding rather than experimental errors . Researchers should embrace this complexity while systematically narrowing down potential functions. Collaborative approaches involving labs with different expertise can help resolve contradictions through complementary experimental strategies. Finally, researchers should remain open to the possibility that SPAC14C4.10c may have multiple distinct functions depending on cellular context or environmental conditions.

What controls are essential when studying recombinant SPAC14C4.10c?

Robust experimental design for SPAC14C4.10c studies requires appropriate controls:

For interaction studies, researchers should include both positive controls (known interacting proteins) and negative controls (proteins unlikely to interact) . When performing localization studies, co-staining with established organelle markers provides essential reference points. For functional assays, dose-response relationships and time-course analyses strengthen causal inferences. Additionally, researchers should include technical replicates to assess method reliability and biological replicates to account for natural variation, adhering to the scientific process framework utilized in academic research training .

What are the challenges in comparing SPAC14C4.10c orthologs across species?

Cross-species comparison of SPAC14C4.10c orthologs presents several challenges:

• Ortholog identification issues:

  • Low sequence conservation in rapidly evolving proteins

  • Presence of paralogs complicating one-to-one relationships

  • Gene duplication and loss events across lineages

• Functional divergence considerations:

  • Neofunctionalization after duplication events

  • Adaptation to species-specific cellular environments

  • Changes in interaction partners across species

• Technical challenges:

  • Different expression systems required for different species

  • Varying genetic manipulation tools across model organisms

  • Species-specific post-translational modifications

• Analytical approaches:

  • Reciprocal BLAST for initial ortholog identification

  • Synteny analysis to confirm genomic context conservation

  • Complementation assays to test functional conservation

Researchers should use multiple sequence alignment tools specialized for distantly related sequences (e.g., MUSCLE, T-Coffee) and consider structure-based alignments when sequence similarity is low . Phylogenetic analysis should include appropriate outgroups and use models that account for varying evolutionary rates across protein regions. When testing functional conservation, expression of orthologs in the S. pombe background can directly assess complementation of SPAC14C4.10c deletion phenotypes, providing strong evidence for functional equivalence despite sequence divergence.

How can researchers investigate post-translational modifications of SPAC14C4.10c?

Investigating post-translational modifications (PTMs) of SPAC14C4.10c requires specialized approaches:

• PTM prediction and targeting:

  • Computational prediction of likely modification sites

  • Conservation analysis of putative modification motifs

  • Literature review of modifications in related proteins

• Experimental detection methods:

  • Mass spectrometry-based proteomics (MS/MS, PTM-specific enrichment)

  • Western blotting with PTM-specific antibodies

  • Radioactive labeling (e.g., 32P for phosphorylation)

  • Chemical labeling strategies (e.g., biotin switch for nitrosylation)

• Functional significance assessment:

  • Site-directed mutagenesis of modified residues

  • Temporal analysis during cell cycle or stress responses

  • Inhibitor studies targeting specific modifying enzymes

  • In vitro enzymatic assays with modified and unmodified protein

• PTM dynamics studies:

  • Quantitative proteomics across conditions

  • Pulse-chase experiments to determine modification turnover

  • Correlation with protein activity, localization, or interactions

For membrane proteins like SPAC14C4.10c may be, specialized extraction and enrichment protocols are required to maintain PTMs during preparation . When designing mutagenesis studies, researchers should consider both phosphomimetic (e.g., S to D/E) and non-phosphorylatable (e.g., S to A) mutations to assess functional impacts. Integration of PTM data with structural information can provide insights into how modifications might alter protein conformation, interaction surfaces, or enzymatic activity, contributing to a more complete understanding of SPAC14C4.10c regulation and function.

How can SPAC14C4.10c research be structured as a training project for students?

SPAC14C4.10c research provides an excellent framework for student training in molecular biology and biochemistry:

• Project structure for undergraduate research:

  • Begin with bioinformatic analysis and hypothesis generation

  • Progress to recombinant protein expression and purification

  • Advance to functional characterization based on predictions

  • Culminate in integrative data analysis and presentation

• Implementation of research training framework:

  • Scaffold learning with increasingly complex techniques

  • Incorporate research proposal writing and peer review

  • Maintain detailed laboratory journals for reproducibility

  • Culminate with poster presentations modeling scientific meetings

• Assessment strategies:

  • Formative assessments through laboratory journal reviews

  • Peer review of research proposals using grading rubrics

  • Self-reflection worksheets to promote metacognition

  • Summative assessment through final presentations

• Timeline structure:

  • Weeks 1-3: Background research and bioinformatics

  • Weeks 4-6: Cloning and construct preparation

  • Weeks 7-9: Protein expression optimization

  • Weeks 10-12: Functional assays

  • Weeks 13-15: Data analysis and presentation preparation

This structure aligns with proven student-scientist curriculum models that integrate inquiry-based research experiences with professional development activities . Research on uncharacterized proteins like SPAC14C4.10c is particularly valuable for training as it emphasizes the discovery process, teaches students to deal with ambiguity, and models real scientific investigation rather than cookbook laboratory exercises.

What data should be included in publications focusing on SPAC14C4.10c characterization?

Publications characterizing SPAC14C4.10c should include comprehensive data sets:

• Sequence and structural information:

  • Complete sequence with accession numbers

  • Domain architecture diagrams

  • Structural models or experimental structures

  • Evolutionary conservation analysis

• Expression and purification details:

  • Full construct designs including tags and linkers

  • Expression conditions optimization data

  • Purification strategy with yield and purity assessment

  • Stability and storage condition evaluations

• Functional characterization:

  • Phenotypic analysis of deletion/knockdown strains

  • Localization data with appropriate controls

  • Interaction partner identification and validation

  • Biochemical activity assays with statistical analysis

• Supporting data formats:

  • Representative images of key findings

  • Quantitative data in table format

  • Statistical analysis details

  • Raw data availability statement

Following the NIH guidelines for data tables in research publications ensures comprehensive reporting . Researchers should prepare tables that clearly present both positive and negative results, avoiding publication bias. For S. pombe-specific work, adherence to the community's gene and protein nomenclature standards is essential. Additionally, researchers should consider depositing raw data in appropriate repositories (e.g., ProteomeXchange for proteomics data, GEO for transcriptomics) to enhance reproducibility and enable meta-analyses.