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Recombinant Porcine Matrix Metalloproteinase 13 (MMP13), N-His

Cat. No.AP7F213

Product TypeAnimal Proteins

Size

Product Overview

BioVenic's Recombinant Porcine Matrix Metalloproteinase 13 (MMP13), N-His is a recombinant protein expressed from E.coli expression system. Its predicted molecular weight is 20.1 kDa. The purity is>95% (SDS-PAGE). The endotoxin level is <1 EU/μg (LAL). It can be used as a positive control or as an immunogen.

Specifications

Type Recombinant Protein
Species Swine
Expression System E.coli
Purity >95% (SDS-PAGE)
Endotoxin <1 EU/μg (LAL)
Predicted Molecular Weight 20.1 kDa
Molecular Weight 20.1 kDa
Physical State Lyophilized
Formulation PBS, pH7.4, containing 0.01% SKL, 5% Trehalose.

Target Information

Matrix metallopeptidase 13, also known as collagenase 3, is encoded by the MMP13 gene in pigs. Research found that the presence of IL-1α, IL-1RA, IL-2, IL-4, IL-6, and IL-10 and MMP-2, MMP-3, MMP-12, and MMP-13 in the synovial fluid was associated with better cartilage outcomes in a porcine model.

Protein Porcine Matrix Metalloproteinase 13 (MMP13)
Protein Synonym matrix metallopeptidase 13, MMP-13, collagenase 3, matrix metallopeptidase 13 (collagenase 3), matrix metalloproteinase-13
Gene ID 397346
UniProt ID F1SV56

Shipping and Storage

This product is shipped with dry ice. Avoid repeated freezing and thawing. Store for 4 weeks at 2-8°C, for 12 months at -80°C.

Documents

COA

To request a Certificate of Analysis, please enter the Lot No. in the search box. Note: Certificate of Analysis not available for kits.

The product is for research use only.
Not for commercial, prophylactic, diagnostic, or therapeutic applications.

User Note

  1. Always centrifuge tubes before opening. Avoid mixing by vortexing or pipetting. Reconstitute in 10 mM PBS (pH 7.4) to a concentration of 0.1-1.0 mg/mL. Aliquote the reconstituted solution to minimise freeze-thaw cycles.

References

  1. Donnenfield, J. I. et al. Predicting severity of cartilage damage in a post-traumatic porcine model: Synovial fluid and gait in a support vector machine. PloS one. 2022, 17: e0268198.
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