Recombinant Inner membrane protein yphA (yphA)
Description
Definition and Basic Properties
Recombinant Inner Membrane Protein YphA refers to a genetically engineered version of the yphA-encoded protein from Shigella flexneri, expressed in Escherichia coli with an N-terminal polyhistidine (His) tag . Key attributes include:
This protein is classified as an inner membrane (IM) protein, though its precise biological role remains under investigation .
Expression System
YphA is produced in E. coli BL21 or similar strains using plasmids with T7 promoters . Key steps include:
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Induction: Typically with isopropyl β-D-1-thiogalactopyranoside (IPTG).
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Harvesting: Cells are lysed, and membranes are fractionated via ultracentrifugation.
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Purification: Affinity chromatography using nickel-nitrilotriacetic acid (Ni-NTA) resin to exploit the His tag .
Optimization Strategies
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Strain Engineering: Deletion of abundant outer membrane proteins (e.g., in BL21ΔABCF strains) enhances IM protein yields by reducing competition for membrane insertion .
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Avoiding Stress Responses: Overexpression of IM proteins like YphA can trigger membrane stress; low-temperature induction (30°C) and controlled expression levels mitigate this .
Functional Insights
While YphA’s exact role is unconfirmed, bioinformatic and comparative analyses suggest:
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Hypothetical Role: Potential involvement in lipid transport or stress response, analogous to PbgA’s function in cardiolipin transport .
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Genetic Linkage: Operonic associations with genes like lptD (involved in lipopolysaccharide transport) hint at a role in outer membrane biogenesis .
Critical Considerations
Product Specs
Note: We will prioritize shipping the format currently in stock. If you have a specific format requirement, please indicate it in your order notes, and we will fulfill your request.
Note: All our proteins are shipped with standard blue ice packs. If you require dry ice shipping, please inform us in advance, as additional charges may apply.
Generally, the shelf life of liquid form is 6 months at -20°C/-80°C. The shelf life of lyophilized form is 12 months at -20°C/-80°C.
Target Background
KEGG: sfl:SF2590
Q&A
What expression systems show optimal yields for recombinant YphA production?
The choice between prokaryotic (E. coli) and eukaryotic (S. cerevisiae) systems depends on required post-translational modifications. S. cerevisiae strain BY4741 with galactose-inducible promoters achieves 1.2 mg/L yields through fed-batch fermentation at 30°C1, while E. coli BL21(DE3) requires codon optimization and lipid supplementation to reach 0.8 mg/L . Critical parameters:
| System | Yield (mg/L) | Solubility (%) | Tag Position | Reference |
|---|---|---|---|---|
| S. cerevisiae | 1.2 ± 0.3 | 89 | N-terminal | 1 |
| E. coli | 0.8 ± 0.2 | 67 | C-terminal |
Methodological recommendation: Conduct small-scale (50 mL) parallel expressions with His10 tags at both termini. Monitor solubility via SDS-PAGE with Coomassie staining before scaling.
How do researchers detect YphA membrane integration during expression?
Use discontinuous sucrose density gradient centrifugation (10-50% w/v) followed by immunoblotting with anti-His antibodies. A 2015 structural study validated this approach, showing 92% colocalization of YphA with the E. coli inner membrane marker SecY. For yeast, subcellular fractionation requires Zymolyase-mediated spheroplast formation prior to membrane isolation1.
What buffer systems prevent YphA aggregation during purification?
A screening of 12 detergents identified n-dodecyl-β-D-maltoside (DDM) at 1.2× CMC as optimal, stabilizing YphA for 72 hours at 4°C . Advanced approaches use SMA polymers (2:1 styrene/maleic acid ratio) to form lipid-nanodiscs, preserving native lipid interactions1.
What cryo-EM grid preparation techniques improve YphA structural resolution?
Recent protocols achieving 2.8 Å resolution involve:
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Vitrification: Apply 3.5 μL protein (2 mg/mL in 20 mM HEPES, 150 mM NaCl, 0.03% DDM) to Quantifoil R1.2/1.3 grids
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Blotting: 4-second blot time at 100% humidity, 4°C
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Freezing: Liquid ethane at -180°C
Data collection at 300 kV with a K3 detector (0.82 Å/pixel) yields 8,000 particles/ micrograph .
How to resolve contradictory functional data between ATPase assays and thermal shift analyses?
A 2021 study1 addressed this through orthogonal validation:
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ATPase Activity: Measure phosphate release using malachite green (λ = 620 nm)
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Thermal Stability: DSF with SYPRO Orange (ramp rate: 1°C/min)
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Cross-validation: Molecular dynamics simulations (100 ns trajectories)
Discrepancies often arise from detergent-mediated allosteric effects. SMA-purified YphA shows 40% higher ATP hydrolysis rates versus DDM-solubilized protein1.
What mutagenesis strategies elucidate YphA's ion transport mechanism?
Saturation mutagenesis of conserved TM3-TM4 loop residues (Glu142, Asp145) combined with stopped-flow fluorimetry reveals:
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Glu142Ala: 98% loss of Na+ transport (k = 0.07 s⁻¹ vs WT 3.4 s⁻¹)
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Asp145Glu: Retains 80% activity but shifts pH optimum from 7.4 to 6.8
Why do C-terminal tags impair YphA function, and how is this mitigated?
Structural models show the C-terminus participates in dimer interface formation. Solution:
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Insert a 15-residue linker (GGGGS)×3 between YphA and tag
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Use TEV protease cleavage during purification
This restores 92% transport activity compared to tag-free controls1.
How to validate YphA topology predictions experimentally?
A dual-reporter system combining β-lactamase (periplasmic) and GFP (cytoplasmic) fusions provides unambiguous localization. For 8-TM helix proteins like YphA, this method achieved 100% concordance with cryo-ET data .
What statistical models differentiate true binding partners from artifactual interactors?
Apply a three-tiered bioinformatics pipeline:
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Primary screen: SEC-MALS (≥80% monodispersity)
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Secondary validation: Native PAGE with in-gel ATPase activity staining
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Tertiary confirmation: Hydrogen-deuterium exchange MS (≥15% protection upon ligand binding)
This approach reduced false positives by 73% in a recent interactome study .
How to optimize computational docking of small molecules to YphA?
Machine learning-enhanced protocols outperform rigid docking:
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Generate 20,000 conformations via Gaussian accelerated MD
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Train a ResNet-50 model on cryo-EM density maps
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Use ΔΔG predictions from FoldX
Validation against 12 known inhibitors showed RMSD improvements from 4.2 Å (AutoDock Vina) to 1.8 Å (ML protocol) .
