Recombinant Schizosaccharomyces pombe Uncharacterized protein C19D5.10c (SPAC19D5.10c)

What is known about the SPAC19D5.10c protein in S. pombe?

SPAC19D5.10c is an uncharacterized protein located on Chromosome III of Schizosaccharomyces pombe. Current genomic data indicates that the protein has no experimentally validated functional domains or motifs. Homology searches against public databases (e.g., Pfam, InterPro) do not reveal significant matches to known protein families . The full-length protein consists of 87 amino acids, and recombinant versions are typically expressed with tags (such as His-tags) for purification purposes .

Why is S. pombe a valuable model organism for studying uncharacterized proteins?

S. pombe serves as an excellent model organism for several reasons:

• It has a relatively small genome (~12.8Mb) contained mainly in three chromosomes plus the mitochondrial genome

• Approximately 5000 genes are present, with 70% having human orthologs

• It offers excellent genomic resources through PomBase, including a high-quality reference genome and genome browser

• It is particularly valuable for cell cycle, DNA replication, and DNA damage research

• The organism is inexpensive to grow, proliferates rapidly, and is amenable to genetic manipulation

What resources are available for researching SPAC19D5.10c?

PomBase (http://www.pombase.org/) is the primary model organism database for S. pombe, offering:

• Genome sequence and feature information

• Integration with genome-wide datasets

• Comprehensive gene-oriented manual curation of published literature

• Tools to interrogate available data

• Community support and curation resources

Additional resources include:

• KEGG database (entry: spo:SPAC19D5.10c)

• String database for potential protein-protein interactions

• Yeast deletion libraries and genetic screening tools

What experimental methods are recommended for initial characterization of SPAC19D5.10c?

For initial characterization of an uncharacterized protein like SPAC19D5.10c, a stepwise approach is recommended:

• Knockout phenotyping:

  • Generate SPAC19D5.10c deletion strains using homologous recombination

  • Assess viability and growth defects under various conditions

  • Use methods similar to those in synthetic genetic array (SGA) analyses

• Subcellular localization:

  • Express SPAC19D5.10c with fluorescent protein tags (e.g., GFP, mCherry)

  • Perform immunolocalization with specific antibodies

  • Use methanol fixation protocols for immunofluorescence labeling

• Proteomic analysis:

  • Perform co-immunoprecipitation (co-IP) followed by mass spectrometry

  • Use cross-linking approaches to identify transient interactions

  • Apply sucrose density gradient centrifugation for cellular fractionation studies

• Transcriptome analysis:

  • Assess expression patterns under different conditions using RNA-seq

  • Compare expression profiles with other genes of interest using microarray data

How can recombinant SPAC19D5.10c protein be efficiently expressed and purified?

For optimal expression and purification of SPAC19D5.10c:

• Expression system selection:

  • E. coli systems are commonly used for initial attempts with His-tagged constructs

  • Consider S. pombe expression systems for proper post-translational modifications

  • Use codon-optimized sequences to enhance expression yields

• Optimization strategies:

  • If expression levels are low in bacterial systems, consider applying computational design strategies to improve core packing, surface polarity, and backbone rigidity

  • Test multiple tags (His, GST, MBP) as fusion partners to improve solubility

  • Vary induction conditions (temperature, IPTG concentration, induction time)

• Purification protocols:

  • Implement a two-step purification strategy (e.g., affinity chromatography followed by size exclusion)

  • Include protease inhibitors during lysis to prevent degradation

  • Test multiple buffer conditions to enhance protein stability

How can genome-wide screens be designed to identify genetic interactions of SPAC19D5.10c?

For comprehensive genetic interaction mapping:

• Synthetic Genetic Array (SGA) methodology:

  • Generate a query strain containing SPAC19D5.10c deletion or conditional expression

  • Cross this strain systematically with a deletion library collection (~7000 strains)

  • Analyze growth phenotypes to identify synthetic lethal (SL) or synthetic suppressor (SS) interactions

  • Apply statistical analysis using tools like ScreenMill software to quantify colony sizes and normalize data

• Data analysis pipeline:

  Analysis Step	Key Methods	Software Tools
  Initial screening	Growth comparison (induced vs. non-induced)	ScreenMill
  Hit ranking	Statistical significance (P≤0.05)	Excel
  Ortholog identification	Cross-reference with multiple databases	PomBase, OrthoMCL, InParanoid8, Homologene
  Network construction	Protein-protein interaction mapping	String-db (confidence level 0.900)
  Validation	Targeted gene deletion and complementation	PCR-based methods

• Integration with existing networks:

  • Extend primary networks by adding first-degree neighbors

  • Focus on interactions based on experimental data at high confidence levels

  • Extract new hits corresponding to at least two previous "orphans" interacting through a nearest neighbor

What approaches can identify potential functional roles of SPAC19D5.10c through transcriptomic analysis?

For transcriptomic analysis of SPAC19D5.10c function:

• Differential expression analysis:

  • Compare wild-type vs. SPAC19D5.10c deletion/overexpression strains using RNA-seq

  • Apply methods similar to those used in identifying differentially expressed genes in S. pombe mutants

  • Analyze data using statistical tools to identify significantly upregulated or downregulated genes

• Cell cycle-regulated expression:

  • Investigate whether SPAC19D5.10c is periodically expressed during the cell cycle

  • Implement cell synchronization methods (e.g., nitrogen starvation, hydroxyurea block)

  • Collect samples at regular intervals for RNA extraction and analysis

  • Use clustering approaches to group genes with similar expression patterns

• Regulatory motif identification:

  • Search for regulatory elements in the SPAC19D5.10c promoter region

  • Apply computational motif discovery programs like Multiple Em for Motif Elicitation (MEME)

  • Validate potential regulatory elements through reporter assays (e.g., lacZ reporters)

• Cross-species comparison:

  • Compare expression patterns with orthologous genes in other yeast species

  • Analyze conservation of regulatory mechanisms across species

What strategies can determine if SPAC19D5.10c has a role in RNA interference pathways in S. pombe?

To investigate potential roles in RNAi pathways:

• Genetic interaction analysis:

  • Test interactions with known RNAi components (e.g., Ago1, Dcr1)

  • Create double mutants and assess phenotypes related to RNAi function

  • Examine heterochromatin formation at centromeres, a process dependent on RNAi in S. pombe

• Protein interaction studies:

  • Perform co-immunoprecipitation with FLAG-tagged SpAgo1

  • Analyze interaction partners using two-dimensional gel electrophoresis and mass spectrometry

  • Test whether SPAC19D5.10c associates with RNAi machinery components

• Functional assays:

  • Create reporter gene silencing constructs

  • Test the effect of SPAC19D5.10c deletion on reporter gene silencing

  • Examine centromeric small RNA production in SPAC19D5.10c mutants

• Phosphorylation analysis:

  • Apply computational prediction of phosphorylation sites

  • Investigate whether SPAC19D5.10c is a substrate for kinases involved in RNAi

  • Test whether kinase mutants affect SPAC19D5.10c function

What biochemical approaches can determine the potential enzymatic activity of SPAC19D5.10c?

For enzymatic activity characterization:

• Activity screening panels:

  • Test the purified protein against various substrate classes (glucans, mannans, nucleic acids)

  • Employ colorimetric or fluorometric assays to detect potential enzymatic activities

  • Consider that S. pombe cell wall contains β-glucans (28%) and α-glucans (46-54%)

• Structure prediction and analysis:

  • Use computational approaches to predict potential catalytic sites

  • Apply homology modeling based on structurally similar proteins

  • Generate mutations of predicted catalytic residues for functional validation

• Protein interaction mapping:

  • Identify binding partners that might suggest functional roles

  • Use biochemical techniques such as pull-downs with tagged versions of the protein

  • Apply proteomics approaches to identify protein complexes containing SPAC19D5.10c

How can post-translational modifications of SPAC19D5.10c be characterized?

For PTM analysis:

• Phosphorylation analysis:

  • Predict potential phosphorylation sites using computational tools

  • Perform phosphoproteomics analysis of purified protein

  • Investigate regulation by specific kinases using inhibitors or kinase mutants

• Glycosylation studies:

  • Test for potential N- and O-glycosylation using glycosidase treatments (e.g., EndoH)

  • Investigate whether SPAC19D5.10c contains S/T-rich regions prone to O-mannosylation

  • Analyze glycosylation patterns in wild-type versus glycosylation pathway mutants

• Other modifications:

  • Examine potential ubiquitination or SUMOylation through proteomic approaches

  • Test stability of the protein in various mutant backgrounds (e.g., proteasome mutants)

  • Analyze protein turnover rates under different conditions

How can multi-omics data be integrated to develop hypotheses about SPAC19D5.10c function?

For multi-omics data integration:

• Data collection strategy:

  • Generate proteomics, transcriptomics, and interactomics data for SPAC19D5.10c

  • Collect data across different conditions (e.g., stress, cell cycle phases)

  • Apply standardized protocols for data generation and processing

• Integration approaches:

  • Use computational frameworks that combine multiple data types

  • Apply machine learning methods to identify patterns across datasets

  • Develop network models that incorporate diverse data sources

• Validation experiments:

  • Design targeted experiments to test hypotheses generated from integrated analyses

  • Prioritize experiments based on strongest predictions from multi-omics integration

  • Apply statistical methods to evaluate confidence in predictions

What computational approaches can predict the structure and function of SPAC19D5.10c?

For computational structure-function prediction:

• Sequence analysis:

  • Apply sensitive homology detection methods (e.g., PSI-BLAST, HHpred)

  • Use machine learning approaches to identify remote homologs

  • Examine conserved regions that might indicate functional sites

• Structure prediction:

  • Implement modern deep learning approaches for protein structure prediction

  • Generate structural models using comparative modeling or ab initio methods

  • Analyze predicted structures for potential functional sites

• Function prediction:

  • Integrate structural predictions with conservation analysis

  • Apply sequence-based function prediction tools

  • Use computational design strategies to test functional hypotheses

• Stability optimization strategies:

  Computational Design Approach	Application to SPAC19D5.10c	Expected Outcome
  Core packing optimization	Identify destabilizing core residues	Enhanced thermostability
  Surface polarity improvement	Redesign surface residues	Improved solubility
  Backbone rigidity enhancement	Target flexible regions	Reduced conformational entropy
  Energy function application	Calculate ΔΔG values for mutations	Prediction of stabilizing mutations

What are the most valuable resources for studying uncharacterized proteins in S. pombe?

Key resources include:

• Databases and repositories:

  • PomBase: The primary resource for S. pombe genomic and functional data (http://www.pombase.org/)

  • String-db: For protein interaction network analysis (www.string-db.org)

  • KEGG: For metabolic pathway integration (entry: spo:SPAC19D5.10c)

• Community resources:

  • S. pombe deletion libraries: Comprehensive collections covering >90% of non-essential genes

  • PomBase community curation: Expert-validated functional annotations

  • Shared protocols and methodologies for S. pombe research

• Analysis tools:

  • ScreenMill software: For genetic interaction screen analysis

  • Statistical packages for RNA-seq and proteomic data analysis

  • Computational prediction tools for protein structure and function

How can research on SPAC19D5.10c contribute to understanding human biology?

Given that 70% of S. pombe genes have human orthologs , research on SPAC19D5.10c could:

• Identify conserved functions:

  • Determine if human orthologs exist using sensitive homology detection methods

  • Investigate whether these orthologs perform similar functions in human cells

  • Apply cross-species complementation tests to validate functional conservation

• Elucidate fundamental biological processes:

  • Study roles in conserved pathways like cell cycle regulation or DNA replication

  • Investigate potential roles in stress response mechanisms

  • Examine contributions to cellular compartmentalization and organization

• Develop methodologies:

  • Establish approaches for characterizing other uncharacterized proteins

  • Develop integrated analysis pipelines applicable to human studies

  • Create model systems for testing hypotheses relevant to human biology