SPCC757.15 is annotated as an uncharacterized membrane protein in S. pombe (fission yeast). Its identifier follows the Schizosaccharomyces Pombe Chromosome C (SPCC) naming convention, where:
SPCC: S. pombe Chromosome C.
757.15: Gene identifier (e.g., chromosome position or functional cluster).
Membrane proteins in S. pombe are critical for processes like nuclear envelope organization, mitochondrial function, and stress response. Key features include:
While SPCC757.15 has not been explicitly described, recombinant production of similar S. pombe membrane proteins follows standardized protocols:
Cloning and Expression:
Purification:
Validation:
Membrane Protein Instability: Requires stabilizing additives (e.g., glycerol, trehalose) .
Low Homology: SPCC757.15 may lack annotated homologs, complicating structure-function predictions .
Functional Annotation:
Structural Insights:
Cryo-EM or X-ray crystallography is needed to resolve its topology and potential ligand-binding sites.
Therapeutic Potential:
KEGG: spo:SPCC757.15
STRING: 4896.SPCC757.15.1
SPCC757.15 is an uncharacterized membrane protein from the fission yeast Schizosaccharomyces pombe with a full length of 69 amino acids . It is classified as a membrane protein, suggesting it contains hydrophobic regions that anchor it within cellular membranes. The recombinant form of this protein can be produced with a histidine tag to facilitate purification and experimental manipulation . While considered "uncharacterized," preliminary functional annotation suggests it may be associated with mitochondrial cytochrome c oxidase assembly processes with a confidence score of 0.64 .
E. coli has been successfully used as an expression host for recombinant SPCC757.15 protein production . When designing an expression system for this membrane protein, researchers should consider:
Vector selection: Choose vectors with strong, inducible promoters like T7 or tac promoters for controlled expression
Fusion tags: His-tagging has been validated for this protein and facilitates purification via metal affinity chromatography
Expression conditions: Optimize temperature, induction time, and inducer concentration to maximize yield while maintaining proper folding
Membrane protein considerations: Lower expression temperatures (15-25°C) often improve proper folding of membrane proteins
Solubilization strategies: Test various detergents to effectively extract the protein from membranes while maintaining native conformation
A multi-faceted approach to verification is recommended:
SDS-PAGE analysis: Confirms molecular weight (expected ~7-8 kDa plus tag size)
Western blotting: Using anti-His antibodies for tagged protein detection
Mass spectrometry: For accurate mass determination and sequence verification
N-terminal sequencing: To confirm the intact N-terminus
Circular dichroism: To assess secondary structure elements characteristic of membrane proteins
Size-exclusion chromatography: To evaluate homogeneity and oligomeric state
Given the current bioinformatic prediction linking SPCC757.15 to mitochondrial cytochrome c oxidase assembly , several complementary approaches are recommended:
Gene knockout/knockdown studies: Generate SPCC757.15 deletion strains in S. pombe and characterize phenotypic effects, particularly focusing on:
Mitochondrial morphology and function
Cytochrome c oxidase activity assays
Growth under respiratory versus fermentative conditions
Stress response, particularly oxidative stress
Localization studies: Employ fluorescent protein tagging (GFP/mCherry) to determine subcellular localization, with special attention to mitochondrial colocalization
Protein-protein interaction studies:
Co-immunoprecipitation with known components of cytochrome c oxidase assembly machinery
Yeast two-hybrid screening
Proximity labeling approaches (BioID/APEX)
Cross-linking mass spectrometry
Functional complementation: Test whether SPCC757.15 can rescue phenotypes in strains lacking other known cytochrome c oxidase assembly factors
A systematic experimental design approach should include:
Baseline characterization:
Measure cytochrome c oxidase activity in wild-type S. pombe
Assess mitochondrial respiration rates
Quantify ATP production
Comparative analysis:
Generate SPCC757.15 deletion strain
Compare cytochrome c oxidase activity, respiration, and ATP production between wild-type and deletion strains
Perform growth assays under different carbon sources (glucose vs. glycerol/ethanol)
Rescue experiments:
Reintroduce SPCC757.15 to knockout strain under native or inducible promoter
Test whether phenotypes are rescued
Include control with mutated versions of SPCC757.15 to identify critical residues/domains
Biochemical interaction studies:
Purify SPCC757.15 and test direct binding to cytochrome c oxidase subunits or assembly factors
Perform in vitro reconstitution assays
Control for confounding variables:
Use multiple deletion clones to rule out off-target effects
Include controls for general mitochondrial function
Test specificity by examining other respiratory chain complexes
Based on recent functional annotation research, multi-faceted bioinformatic approaches yield the most reliable predictions:
Sequence similarity analysis: PANNZER2 has been successfully used for functional annotation of uncharacterized S. pombe proteins, including SPCC757.15 . This tool:
Structural prediction:
AlphaFold2 or RoseTTAFold for 3D structure prediction
Membrane topology prediction using TMHMM or Phobius
Secondary structure prediction using JPred or PSIPRED
Evolutionary analysis:
Identification of conserved domains or motifs
Phylogenetic profiling to identify proteins with similar evolutionary patterns
Comparative genomics across yeast species
Network-based approaches:
Functional association networks (STRING database)
Co-expression analysis across multiple conditions
Genomic context analysis (gene neighborhood)
An iterative approach combining wet-lab experiments with computational refinement is recommended:
Initial bioinformatic predictions:
Experimental validation:
Design targeted experiments to test predictions
Generate new data (localization, interaction partners, phenotypic effects)
Refinement of predictions:
Update models with new experimental data
Employ machine learning approaches that incorporate diverse data types
Use Bayesian frameworks to update confidence in functional predictions
Iterative validation:
Design subsequent experiments based on refined predictions
Focus on areas of uncertainty or contradiction
Data integration platforms:
Use platforms that can integrate multiple data types (e.g., InterMine, KBase)
Develop custom pipelines for S. pombe uncharacterized proteins
Recent research has investigated the expression patterns of uncharacterized proteins, including SPCC757.15, in response to metformin treatment in S. pombe . The findings suggest:
Expression changes:
Functional context:
The predicted association with mitochondrial function aligns with known metformin mechanisms involving mitochondrial activity
Changes in expression may reflect adaptive responses to metabolic alterations induced by metformin
Research implications:
SPCC757.15 represents a potential new target for aging research
Its involvement in mitochondrial processes connects to the mitochondrial theory of aging
Understanding its precise role could illuminate mechanisms of metformin's life-extending effects
Experimental approaches:
Time-course analysis of expression changes following metformin treatment
Comparison of wild-type versus SPCC757.15 deletion strains in longevity assays
Investigation of interactions with known aging-related pathways (TOR, AMPK, sirtuins)
Aging research requires careful experimental design to account for numerous variables and complex phenotypes:
Strain selection and validation:
Use well-characterized S. pombe strains with consistent genetic backgrounds
Generate multiple independent SPCC757.15 deletion or overexpression strains
Validate genetic modifications through sequencing and expression analysis
Lifespan assay design:
Variables to control:
Growth conditions (temperature, media composition, culture density)
Cell cycle stage and synchronization
Metabolic state (respiratory vs. fermentative)
Stress factors (oxidative, nutrient availability)
Key measurements:
Lifespan (chronological and replicative)
Mitochondrial function (membrane potential, respiration rate)
ROS production and oxidative damage
ATP levels and metabolic profiles
Protein aggregation and proteostasis markers
Statistical considerations:
Appropriate sample sizes for detecting expected effect sizes
Multiple biological and technical replicates
Control for batch effects
Longitudinal measurements to capture temporal dynamics
A comparative analysis approach can provide valuable context:
Structural comparison:
Expression pattern analysis:
Co-expression network analysis:
Identify other uncharacterized proteins with similar expression patterns
Construct functional association networks
Identify clusters of co-regulated uncharacterized proteins
Evolutionary conservation:
Presence or absence of orthologs in related species
Conservation patterns across fungal lineages
Comparison to similar proteins in model organisms with better-characterized proteomes
High-throughput functional screening approaches include:
Parallel phenotypic analysis:
Generate a library of deletion strains for multiple uncharacterized proteins
Perform growth assays under various conditions (temperature, carbon source, stress)
Conduct high-content imaging for morphological phenotypes
Use flow cytometry for cell cycle and viability analysis
Pooled functional genomics:
CRISPR-based screens in S. pombe
Barcoded deletion libraries with next-generation sequencing readout
Synthetic genetic array (SGA) analysis to identify genetic interactions
Protein localization screening:
Systematic GFP tagging
High-throughput fluorescence microscopy
Automated image analysis and classification
Biochemical approaches:
Affinity purification-mass spectrometry for multiple targets
Protein microarrays for interaction screening
Activity-based protein profiling
Data integration and analysis:
Machine learning approaches to classify proteins based on multiple data types
Network-based function prediction
Clustering algorithms to identify functional groups
Working with small membrane proteins (SPCC757.15 is 69 amino acids ) presents several technical challenges:
Expression and purification challenges:
Low yield due to toxicity or improper folding
Aggregation during overexpression
Difficulty maintaining native conformation during solubilization
Solutions:
Structural characterization difficulties:
Small size makes electron microscopy challenging
Crystallization of membrane proteins is notoriously difficult
Insufficient protein material for NMR
Solutions:
Computational structure prediction (AlphaFold2)
Circular dichroism for secondary structure elements
Fusion with crystallization chaperones
Solid-state NMR approaches
Functional assay limitations:
Redundancy may mask phenotypes in deletion strains
Small size limits domains for protein-protein interactions
Difficulty distinguishing direct vs. indirect effects
Solutions:
Combine deletion with related genes to overcome redundancy
Use overexpression in addition to deletion studies
Employ sensitive reporters for subtle phenotypes
Develop in vitro reconstitution systems
A comprehensive quality control strategy should include:
Pre-experimental validation:
Sequence verification of expression constructs
Optimization of expression conditions with small-scale tests
Detergent screening for optimal solubilization
Purification quality assessment:
Functional integrity assessment:
Secondary structure analysis via circular dichroism
Thermal stability assays
Reconstitution into liposomes or nanodiscs
Binding assays with predicted interaction partners
Storage stability:
Optimization of buffer conditions
Testing of various stabilizing additives
Freeze-thaw stability analysis
Long-term activity retention measurement
Reproducibility measures:
Multiple biological replicates
Independent protein preparations
Lot-to-lot consistency checks
Detailed record-keeping of all procedures
Based on current knowledge, several research avenues appear particularly promising:
Detailed mitochondrial function studies:
Aging and longevity connections:
Structural biology approaches:
Determination of 3D structure using cryo-EM, NMR, or crystallography
Structure-function studies to identify critical domains or residues
Membrane topology and integration studies
Systems biology integration:
Positioning SPCC757.15 within broader functional networks
Multi-omics approaches to understand regulatory contexts
Computational modeling of potential functional roles
Translational relevance:
Investigation of human orthologs or functional analogs
Connections to mitochondrial disorders in humans
Potential relevance to metabolic diseases
When facing contradictory results, a systematic approach is necessary:
Critical evaluation of existing data:
Assess methodological differences between studies
Examine genetic background variations
Consider environmental and experimental conditions
Evaluate statistical robustness of conflicting findings
Triangulation approach:
Deploy multiple orthogonal techniques to assess the same function
Combine genetic, biochemical, and computational approaches
Use both gain-of-function and loss-of-function strategies
Conditional analyses:
Test function under various environmental conditions
Investigate cell cycle or development stage-specific roles
Examine genetic background dependencies
Collaboration strategies:
Establish collaborative validation studies between labs with conflicting results
Standardize protocols and reagents
Perform blinded analyses when appropriate
Mechanistic dissection:
Move beyond correlative studies to direct mechanistic tests
Develop reconstituted systems to test direct biochemical functions
Generate structure-guided mutations to test specific hypotheses