Recombinant Full Length Saccharomyces cerevisiae Uncharacterized protein C1Q_03362 (C1Q_03362) is a 72-amino acid protein (UniProt ID: C7GSI6) that is produced recombinantly with an N-terminal His tag expressed in E. coli. The full amino acid sequence is: MSKHKHEWTESVANSGPASILSYCASSILMTVTNKFVVNLDNFNMNFVMLFVQSLVCTVTLCILRIVGVANF. The protein is typically supplied as a lyophilized powder with greater than 90% purity as determined by SDS-PAGE .
The protein should be stored at -20°C/-80°C upon receipt, with aliquoting necessary for multiple use to avoid repeated freeze-thaw cycles which can compromise protein integrity. For working solutions, store aliquots at 4°C for up to one week. The lyophilized protein is typically stored in Tris/PBS-based buffer containing 6% Trehalose at pH 8.0 .
For optimal reconstitution:
Briefly centrifuge the vial prior to opening to bring contents to the bottom
Reconstitute the protein in deionized sterile water to a concentration of 0.1-1.0 mg/mL
Add glycerol to a final concentration of 5-50% (50% is recommended as default)
Prepare multiple aliquots for long-term storage at -20°C/-80°C
The basic workflow involves:
Cell culturing of S. cerevisiae under various conditions
RNA extraction and quality control
Microarray or RNA-seq analysis to detect expression levels
Data normalization and statistical analysis
Validation of expression patterns using RT-qPCR
Comparative analysis with known genes related to cellular functions
Based on comparative studies, two protocols have been found effective with Protocol A showing superior results:
Protocol A (Preferred - Urea/Thiourea Extraction):
Resuspend 100 mg wet cell pellets in lysis buffer (6 M urea, 2 M thiourea, 50 mM ammonium bicarbonate, protease inhibitors)
Disrupt cells via multiple rounds of sonication on ice
Reduce proteins with 10 mM dithiothreitol at room temperature for one hour
Alkylate with 20 mM iodoacetamide at room temperature for 30 minutes in dark
First digest with Lys-C for three hours
Dilute 10-fold with 20 mM ammonium bicarbonate (pH 8.5)
Further digest overnight at 37°C with MS-grade trypsin (enzyme:protein ratio ~1:50)
Acidify tryptic peptides with 5% formic acid to pH ≤3
Desalt using Poros Oligo R3 reverse-phase micro-columns
This protocol has been shown to yield approximately 40% more protein identifications than alternative methods .
For robust quantitative proteomics:
Experimental Design:
Use minimum three independent biological replicates per condition
Include at least three technical replicates per biological sample
Include appropriate controls (wild-type, vector-only, etc.)
Mass Spectrometry Analysis:
Implement label-free quantitative (LFQ) mass spectrometric approaches
Analyze samples on high-resolution instruments (e.g., Q-Exactive)
Process data using established platforms (e.g., MaxQuant)
Data Processing:
Convert LFQ values to log2 scale and normalize by subtraction of means
Retain proteins detected in at least 5 sample runs among 9+ in any condition
Perform missing value imputation using normal distribution (width 0.3, downshift 1.8)
Apply statistical analysis (Student's t-test) to identify significantly changed proteins
Consider proteins with p-value <0.05 and fold-change >2 as significantly responsive
A comprehensive data mining approach should include:
Dataset Collection and Processing:
Gather microarray/RNA-seq datasets related to S. cerevisiae under various conditions
Normalize data and perform quality control
Focus on datasets with manipulated fermentation conditions (e.g., Mg²⁺ and Cu²⁺ supplementation)
Machine Learning Implementation:
Apply multiple algorithms (minimum 11 recommended) from platforms like RapidMiner
Identify discriminative genes between conditions (e.g., improved vs. repressed ethanol production)
Select probe sets identified by at least 5 different algorithms
Validate top-ranked selective genes through Principal Component Analysis and heatmap clustering
Functional Analysis:
Construct decision tree models to identify key genes (100% performance)
Perform gene ontology enrichment to identify related biological processes
Analyze pathway involvement through enrichment analysis
Identify regulatory networks using clustering analysis of transcription factors
This approach has successfully identified genes involved in carbohydrate metabolism, oxidative phosphorylation, and ethanol fermentation in S. cerevisiae .
A systematic approach for in-frame deletion includes:
Design and Construction:
Design an in-frame deletion of the coding sequence except first four and last four codons
Join ~900-1000 bp 5' flanking region (with first four codons) with ~900-1000 bp 3' flanking region (with last four codons) to a suicide plasmid
Use PCR with primers containing 25 bp 5' overlapping regions between vector arms and gene fragments
Verification and Transfer:
Control PCR product quality via nanodrop and gel electrophoresis
Use HiFi assembly master mix for the assembly reaction
Transform into appropriate E. coli strain (e.g., S17-1)
Confirm assembled deletion alleles via restriction enzyme digestion and DNA sequencing
Conjugate resultant plasmids into wild-type S. cerevisiae
Selection and Confirmation:
When faced with contradictory findings, implement this structured approach:
Systematic Literature Review:
Collect comprehensive set of publications on C1Q_03362
Extract specific claims about protein function, localization, or interactions
Identify claims that directly contradict each other
Contradiction Analysis:
Categorize contradictions by type (functional, structural, regulatory)
Examine experimental conditions across studies (strains, media, analysis methods)
Note any study characteristics that correlate with contradictory results
Resolution Strategy:
Design experiments that specifically address variables differing between contradictory studies
Implement standardized protocols for protein handling and analysis
Consider multiple analytical approaches to validate findings
Advanced Analysis:
A multi-layered bioinformatic strategy should include:
Approach | Methods | Expected Outcomes |
---|---|---|
Sequence Analysis | BLAST searches, Multiple sequence alignment, Motif scanning | Identification of conserved domains, Functional elements, Similar characterized proteins |
Structural Prediction | Ab initio modeling, Homology modeling, Binding site prediction | 3D structure models, Potential binding pockets, Functional predictions based on structural homology |
Interaction Network | Protein-protein interaction predictions, Integration with yeast interactome | Potential binding partners, Functional association networks, Pathway involvement |
Expression Analysis | Co-expression patterns, Expression changes under stress | Functionally related genes, Condition-specific regulation, Metabolic pathway correlations |
Evolutionary Analysis | Phylogenetic profiling, Selection pressure analysis, Ortholog identification | Evolutionary conservation, Functional constraints, Taxonomic distribution |
For optimal expression and purification:
Expression System Optimization:
Test multiple E. coli strains (BL21, Rosetta, Arctic Express)
Optimize induction conditions (temperature, IPTG concentration, duration)
Consider fusion partners (His-tag position, additional solubility tags)
Test expression in eukaryotic systems for proper post-translational modifications
Purification Strategy:
Implement IMAC (Immobilized Metal Affinity Chromatography) as primary purification
Include secondary purification steps:
Ion exchange chromatography to remove charged contaminants
Size exclusion chromatography to ensure homogeneity
Optimize buffer conditions to maintain protein stability
Consider on-column refolding for inclusion body purification
Quality Control:
A comprehensive investigation requires:
Predictive Analysis:
Use bioinformatic tools to predict potential PTM sites
Focus on common yeast modifications (phosphorylation, glycosylation, acetylation)
Compare predictions across multiple algorithms
Experimental Detection:
Perform specialized digestion protocols to preserve modifications
Implement enrichment strategies for specific PTMs
Use high-resolution mass spectrometry with ETD/HCD fragmentation
Consider targeted approaches for predicted modification sites
Validation:
For accurate localization studies:
Fluorescent Protein Fusion Approaches:
Create C-terminal and N-terminal GFP/mCherry fusions
Validate fusion protein function compared to wild-type
Perform live-cell imaging under various conditions
Co-localize with known compartment markers
Immunolocalization:
Generate specific antibodies against C1Q_03362
Optimize fixation and permeabilization for yeast cells
Implement super-resolution microscopy techniques
Perform co-localization with organelle markers
Biochemical Fractionation:
Perform subcellular fractionation to isolate cellular compartments
Validate fractions using compartment-specific markers
Detect C1Q_03362 in fractions via Western blotting
Confirm localization through multiple independent approaches
Implement a multi-approach strategy:
Yeast Two-Hybrid Screening:
Create bait constructs with different protein domains
Screen against genomic or cDNA libraries
Validate interactions through multiple reporter systems
Confirm with reciprocal bait-prey configurations
Affinity Purification-Mass Spectrometry:
Perform tandem affinity purification of tagged C1Q_03362
Implement crosslinking strategies to capture transient interactions
Analyze by high-resolution mass spectrometry
Use quantitative approaches (SILAC, TMT) to distinguish specific interactors
Filter against common contaminant databases
Proximity Labeling:
Create fusion proteins with BioID or APEX2
Induce proximity-dependent biotinylation
Purify biotinylated proteins and identify by MS
Validate key interactions using orthogonal methods
For addressing production issues:
Low Yield Troubleshooting:
Verify expression construct sequence integrity
Optimize codon usage for E. coli expression
Test multiple growth media formulations
Adjust induction conditions (OD, temperature, duration)
Consider autoinduction media for gradual protein expression
Degradation Prevention:
A comprehensive characterization includes:
Primary Structure Verification:
Peptide mass fingerprinting by MS
N-terminal sequencing
Intact mass analysis to confirm full-length protein
Secondary/Tertiary Structure Analysis:
Circular dichroism for secondary structure elements
Fluorescence spectroscopy for tertiary structure integrity
Thermal shift assays for stability assessment
Limited proteolysis to identify stable domains
Functional Characterization:
Activity assays (once function is established)
Binding studies with potential interaction partners
Stability studies under various buffer conditions
Dynamic light scattering for homogeneity assessment
Develop assays through systematic approaches:
Bioinformatic-Guided Assay Development:
Use structural predictions to identify potential active sites
Search for conserved domains with known functions
Design assays based on predicted biochemical activities
Test activity with structurally related substrates
Unbiased Screening Approaches:
Screen against substrate libraries (peptides, metabolites)
Test for common enzymatic activities (hydrolase, transferase)
Assess binding to cellular extracts or fractionated components
Implement label-free interaction detection methods
Phenotypic Assays:
Compare wild-type and knockout strains under various conditions
Analyze metabolic profiles of mutant strains
Assess transcriptional responses to gene deletion
Evaluate stress responses in the presence/absence of the protein