Recombinant Mouse Glutathione S Transferase mu protein (ab86775)
Key features and details
- Expression system: Escherichia coli
- Purity: > 95% SDS-PAGE
- Suitable for: SDS-PAGE
Description
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Product name
Recombinant Mouse Glutathione S Transferase mu protein
See all Glutathione S Transferase mu proteins and peptides -
Purity
> 95 % SDS-PAGE.
purified by using conventional chromatography techniques. -
Expression system
Escherichia coli -
Protein length
Full length protein -
Animal free
No -
Nature
Recombinant -
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Species
Mouse -
Sequence
MPMILGYWNV RGLTHPIRML LEYTDSSYDE KRYTMGDAPD FDRSQWLNEK FKLGLDFPNL PYLIDGSHKI TQSNAILRYL ARKHHLDGET EEERIRADIV ENQVMDTRMQ LIMLCYNPDF EKQKPEFLKT IPEKMKLYSE FLGKRPWFAG DKVTYVDFLA YDILDQYRMF EPKCLDAFPN LRDFLARFEG LKKISAYMKS SRYIATPIFS KMAHWSNK
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Associated products
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Related Products
Specifications
Our Abpromise guarantee covers the use of ab86775 in the following tested applications.
The application notes include recommended starting dilutions; optimal dilutions/concentrations should be determined by the end user.
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Applications
SDS-PAGE
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Form
Liquid -
Concentration information loading...
Preparation and Storage
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Stability and Storage
Shipped at 4°C. Upon delivery aliquot and store at -20°C. Avoid freeze / thaw cycles.
pH: 7.40
Constituents: PBS, 0.1535% Glutathione
General Info
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Alternative names
- Glutathione S alkyltransferase
- Glutathione S aralkyltransferase
- Glutathione S aryltransferase
see all -
Function
Conjugation of reduced glutathione to a wide number of exogenous and endogenous hydrophobic electrophiles. -
Tissue specificity
Liver (at protein level). -
Sequence similarities
Belongs to the GST superfamily. Mu family.
Contains 1 GST C-terminal domain.
Contains 1 GST N-terminal domain. -
Cellular localization
Cytoplasm. - Information by UniProt
Protocols
To our knowledge, customised protocols are not required for this product. Please try the standard protocols listed below and let us know how you get on.
Datasheets and documents
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Datasheet download
References (1)
ab86775 has been referenced in 1 publication.
- Ouassil N et al. Supervised learning model predicts protein adsorption to carbon nanotubes. Sci Adv 8:eabm0898 (2022). PubMed: 34995109