Recombinant Chromohalobacter salexigens UPF0761 membrane protein Csal_1895 (Csal_1895) is a full-length, His-tagged protein expressed in E. coli for research applications. This 410-amino acid protein (UniProt ID: Q1QWB1) is derived from C. salexigens, a halophilic γ-proteobacterium renowned for its extreme halotolerance and role as a model organism for osmoadaptation studies . While its precise biological function remains under investigation, Csal_1895 is classified as a UPF0761 family membrane protein, suggesting potential roles in stress response or ion transport .
The amino acid sequence of Csal_1895 (MRRPQLLDRRWLTIILRSLRELIQRFDAHDGLKTASALTYTTLFAVVPFMTVLYAMLSAI PSFQGISEQLQALIFSQFVPATGSALVEHLRDFSRQARSLTLIGLMFLLVTAVMMMVTVE RAFNNIWHVSRSRRGVSSFLLYWAVLTLGPLLLGSGFLLSSYLASLTLVRGAAEVLGGPV AFLRLLPLTLSFTAFVFIYMAVPNCRVRFRHAVAGAGLAALALELAKGAFSLYVTYFPSY QVIYGTFAAVPLFLVWVFLSWAIVLVGAELAAWLGERRRAEWRYWAPFWQALGVVSHLYD AHRRGQAVYDRELAMRLGARYSDVMAPLQTLGVAVQLDNDRWMLGRDLGALSLWDFQRAM PWAVPLGESSPAPEMQAIHAALQEAERHRQQVLTQPMEHLLAEGARNDSP) includes conserved domains typical of membrane-associated proteins, such as hydrophobic regions indicative of transmembrane helices .
While direct functional data on Csal_1895 is limited, studies on C. salexigens provide context for its potential roles:
Osmotic Stress Adaptation: C. salexigens employs ectoine and hydroxyectoine biosynthesis under osmotic stress, with membrane proteins modulating ion gradients and respiratory chain components . Csal_1895 may contribute to these pathways, as transcriptomic analyses reveal salinity-dependent regulation of membrane protein genes .
Thermal Stress Response: High temperatures repress ectoine degradation genes while inducing membrane protein-related transcripts, suggesting Csal_1895 could participate in thermoadaptation .
Iron Homeostasis: Membrane proteins in C. salexigens influence iron uptake and siderophore production, which indirectly affects ectoine synthesis .
Membrane Protein Studies: As a recombinant product, Csal_1895 enables structural analyses (e.g., cryo-EM, NMR) and functional assays to probe its role in haloadaptation .
Biotechnological Engineering: May serve as a scaffold for developing salt-tolerant enzymes or biosensors, leveraging C. salexigens' extremophile properties .
| Challenge | Solution |
|---|---|
| Aggregation | Use trehalose-containing buffers during reconstitution |
| Proteolysis | Avoid repeated freeze-thaw cycles; store aliquots at -80°C |
| Activity Loss | Optimize glycerol concentration (20–50%) for long-term storage |
Functional Characterization: Elucidate Csal_1895's role in ion transport or osmolyte synthesis using knockout strains .
Structural Resolution: Apply advanced techniques like single-particle cryo-EM or X-ray crystallography to determine its 3D architecture .
Biotechnological Optimization: Engineer E. coli expression systems to enhance yield and stability for industrial applications .
KEGG: csa:Csal_1895
STRING: 290398.Csal_1895
UPF0761 membrane protein Csal_1895 from Chromohalobacter salexigens is a full-length protein consisting of 410 amino acids with transmembrane domains characteristic of integral membrane proteins . The protein's structural architecture includes specific membrane-spanning regions that facilitate its insertion into cellular membranes. Understanding its primary sequence is essential for downstream applications including recombinant expression, purification strategies, and structural studies.
Multiple expression systems can be employed for recombinant production of Csal_1895, each with distinct advantages. E. coli and yeast expression systems offer the highest yields and shorter turnaround times, making them ideal for initial characterization studies and when larger quantities are required . For studies requiring post-translational modifications necessary for proper folding or activity maintenance, insect cell expression using baculovirus vectors or mammalian cell expression systems are recommended . When selecting an expression system, researchers should consider:
Required protein yield
Need for post-translational modifications
Experimental timeline constraints
Downstream application requirements
Available laboratory resources
The selection of protein tags significantly impacts purification efficiency and downstream applications. His-tagged versions of Csal_1895 have been successfully expressed and purified , enabling metal affinity chromatography-based isolation. When optimizing tag selection, consider:
Tag position (N-terminal vs. C-terminal) based on predicted membrane topology
Tag size impact on protein folding and function
Cleavage site inclusion for tag removal if necessary for structural or functional studies
Compatibility with detergent solubilization methods required for membrane proteins
Potential interference with functional domains or interaction sites
Efficient extraction of Csal_1895 from membrane fractions requires careful optimization of several parameters:
Detergent selection: Screen multiple detergents (e.g., DDM, LDAO, Triton X-100) at varying concentrations. The optimal detergent must solubilize the protein while maintaining its native conformation.
Buffer composition: Implement a systematic evaluation of pH ranges (typically 6.5-8.5), salt concentrations (100-500 mM), and stabilizing additives (glycerol, specific lipids).
Extraction time and temperature: Test extraction times (1-24 hours) at different temperatures (4°C vs. room temperature).
Mechanical disruption: Compare gentle rotation versus sonication or microfluidization for membrane disruption efficiency.
Methodology should include analytical techniques such as Western blotting and activity assays to monitor extraction efficiency across conditions.
A systematic detergent screening approach should be implemented:
| Detergent Class | Examples | Starting Concentration | Advantages | Limitations |
|---|---|---|---|---|
| Maltosides | DDM, UDM | 1-2× CMC | Mild, widely used | Larger micelles |
| Glucosides | OG, NG | 1.5-3× CMC | Smaller micelles | More denaturing |
| Zwitterionic | LDAO, FC-12 | 2-5× CMC | Effective solubilization | Potential denaturation |
| Nonionic | Triton X-100 | 1-2× CMC | Gentle extraction | UV interference |
| Cholate derivatives | Cholate, Deoxycholate | 1-2× CMC | Mimics lipid environment | pH dependent |
| Styrene Maleic Acid (SMA) | SMA copolymers | 2.5% w/v | Native lipid retention | Incompatible with divalent cations |
Protocol should include:
Small-scale extractions (1-5 mL)
Analysis by SDS-PAGE and Western blotting
Size-exclusion chromatography to evaluate oligomeric state
Activity or binding assays to confirm functional integrity
A multi-step chromatography approach typically yields the highest purity:
Primary capture: Immobilized metal affinity chromatography (IMAC) utilizing the His-tag . Optimize imidazole concentration in washing steps (20-50 mM) to remove weakly bound contaminants while retaining target protein.
Intermediate purification: Ion exchange chromatography based on Csal_1895's predicted isoelectric point. Use salt gradient elution (50-500 mM NaCl) with shallow gradients to maximize separation.
Polishing step: Size exclusion chromatography to separate monomeric protein from aggregates and to exchange into final buffer. Select column matrix based on expected molecular weight of protein-detergent complex.
Quality control metrics:
SDS-PAGE analysis (>95% purity)
Western blot confirmation
Dynamic light scattering to assess homogeneity
Mass spectrometry for identity confirmation
Determining membrane topology requires complementary experimental approaches:
Computational prediction: Utilize algorithms specifically designed for membrane proteins (TMHMM, Phobius, TOPCONS) to generate initial topology models.
Cysteine scanning mutagenesis: Systematically introduce cysteine residues at predicted loop regions followed by accessibility labeling with membrane-impermeable reagents.
Epitope insertion: Insert epitope tags (e.g., FLAG, myc) at predicted loops and termini, followed by immunofluorescence in permeabilized vs. non-permeabilized cells.
Protease protection assays: Express in membrane vesicles with defined orientation, then perform limited proteolysis with identification of protected fragments by mass spectrometry.
Fluorescence-based approaches: Utilize GFP fusion constructs with subsequent pH-sensitivity analysis or fluorescence quenching experiments.
Data integration across multiple methods provides the most reliable topology model, particularly for complex multi-spanning membrane proteins like Csal_1895.
Recent advances in computational design enable the creation of soluble analogues of membrane proteins like Csal_1895:
Deep learning pipeline implementation: Utilize robust deep learning approaches that can design complex folds and soluble analogues while maintaining core structural features .
Topology preservation: Ensure the unique membrane topology features are recapitulated in the soluble design by maintaining critical intramolecular contacts and fold characteristics .
Stability optimization: Conduct in silico stability analysis and iterative design improvements to achieve high thermal stability in solution .
Experimental validation workflow:
Expression screening in E. coli
Purification without detergents
Circular dichroism to assess secondary structure
Thermal denaturation assays to evaluate stability
Crystallization trials or NMR studies for structural validation
Functional motif integration: Incorporate native structural motifs from the membrane protein to create functional soluble analogues, potentially enabling new approaches in structural biology and drug discovery .
Crystallization of membrane proteins requires specialized approaches:
Pre-crystallization screening:
Thermal stability assays with various detergents
Monodispersity assessment by size-exclusion chromatography
Limited proteolysis to identify stable domains
Crystallization methods comparison:
| Method | Principle | Advantages | Considerations |
|---|---|---|---|
| Vapor diffusion | Gradual concentration via vapor equilibration | Traditional, widely accessible | Lower success rate with membrane proteins |
| Lipidic cubic phase | Protein reconstitution in lipidic mesophase | Mimics native environment, highly successful for GPCRs | Technically challenging, specialized equipment needed |
| Bicelle crystallization | Protein in disc-shaped lipid-detergent micelles | Intermediate between detergent and LCP methods | Composition optimization critical |
| In meso crystallization | Reconstitution into lipidic mesophases | Stabilizes membrane proteins | Complex setup, difficult crystal harvesting |
Additive screening: Systematically test lipids, cholesterol derivatives, and specific binding partners that may stabilize the protein.
Surface engineering: Consider introducing mutations to create crystal contacts or fusion proteins (e.g., T4 lysozyme) to enhance crystallizability.
Alternative approaches: If crystallization proves challenging, explore single-particle cryo-EM or solid-state NMR approaches.
Multiple complementary approaches should be employed to identify interaction partners:
Affinity purification coupled with mass spectrometry (AP-MS):
Express tagged Csal_1895 in native or heterologous systems
Perform crosslinking prior to solubilization if interactions are transient
Use appropriate controls (tag-only, inactive mutants) to filter non-specific interactions
Quantitative MS approaches (SILAC, TMT) increase confidence in results
Proximity labeling approaches:
BioID or APEX2 fusion proteins expressed in cellular context
Biotinylation of proximal proteins followed by streptavidin pulldown
MS identification of proximal proteins
Yeast two-hybrid membrane adaptations:
Split-ubiquitin membrane yeast two-hybrid
MYTH (membrane yeast two-hybrid) system
Screen against genomic libraries from Chromohalobacter salexigens
Computational predictions:
Protein-protein interaction networks
Genomic context analysis (gene neighborhood, fusion events)
Co-expression data analysis
Validation experiments:
Co-immunoprecipitation
FRET/BRET assays
Microscopy-based co-localization
Functional complementation studies
A comprehensive mutational analysis approach should include:
Rational mutation design based on:
Sequence conservation analysis across orthologs
Structural predictions or experimental structures
Computational hotspot identification
Physicochemical property considerations
High-throughput mutagenesis approaches:
Alanine-scanning mutagenesis of transmembrane regions
Deep mutational scanning coupled with functional selection
CRISPR-based saturation mutagenesis in native context
Functional readouts:
Protein expression and membrane localization assessment
Thermal stability comparisons (DSF, CPM assays)
Binding assays if ligands are known
Activity assays based on predicted function
Structural impact evaluation:
Circular dichroism to assess secondary structure changes
Intrinsic fluorescence for tertiary structure assessment
HDX-MS to identify regions with altered dynamics
MD simulations to predict conformational impacts
Data integration:
Correlation of sequence conservation with mutational sensitivity
Mapping of sensitive positions onto structural models
Identification of functional domains and critical residues
Advanced computational methods provide valuable insights into membrane protein function:
Homology modeling and threading approaches:
Identify structural templates despite low sequence identity
Generate models using membrane-protein specific protocols
Validate models using implicit membrane energy functions
Molecular dynamics simulations:
Embed protein models in explicit lipid bilayers
Perform extended simulations (100ns-1μs) to observe conformational dynamics
Identify potential water/ion channels or substrate binding sites
Apply enhanced sampling techniques (metadynamics, umbrella sampling) for energy landscapes
Machine learning applications:
Predict functional sites using conservation patterns and physicochemical properties
Identify potential ligand binding pockets
Apply deep learning models trained on membrane protein datasets
Network-based function prediction:
Integrate protein-protein interaction data
Analyze genomic context and co-expression networks
Implement guilt-by-association approaches across species
Virtual screening for functional validation:
Dock compound libraries to identified binding sites
Predict binding affinities and interaction patterns
Select candidates for experimental validation
These computational predictions generate testable hypotheses that can guide experimental design and accelerate functional characterization.
Reconstitution into artificial membrane systems enables functional studies in controlled environments:
Liposome reconstitution:
Optimize lipid composition (consider native C. salexigens membrane lipids)
Compare detergent removal methods: dialysis, Bio-Beads, cyclodextrin
Monitor protein orientation using protease protection assays
Assess protein:lipid ratios for optimal activity
Nanodiscs preparation:
Select appropriate membrane scaffold protein (MSP) variants
Optimize MSP:lipid:protein ratios
Characterize by size-exclusion chromatography and negative-stain EM
Enable single-molecule studies and controlled stoichiometry
Proteoliposome functional assays:
Develop fluorescence-based assays for transport/channel activity
Implement counterflow assays if Csal_1895 is a transporter
Design assays with physiologically relevant conditions (salt, pH)
Polymer-based systems:
Amphipols for increased stability
Styrene-maleic acid lipid particles (SMALPs) for native lipid co-extraction
Characterize by analytical ultracentrifugation and cryo-EM
Quality control metrics:
Size distribution (DLS, NTA)
Freeze-fracture electron microscopy for protein incorporation
Fluorescence recovery after photobleaching for mobility assessment
Circular dichroism to confirm retention of secondary structure
The development of soluble functional analogues represents a cutting-edge approach with significant biotechnological potential:
This approach has demonstrated high experimental success rates and represents a de facto expansion of the functional soluble fold space .