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H0537

Millipore

HIS-Select® HF Nickel Affinity Gel

Synonym(s):

Ni-NTA HF resin

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About This Item

UNSPSC Code:
41106500
NACRES:
NA.56

form

suspension

Quality Level

technique(s)

affinity chromatography: suitable

matrix

Highly cross-linked 6% Beaded Agarose

capacity

15 mg/mL, gel binding capacity (protein)(with an approx. 30 kDa protein)

storage temp.

2-8°C

General description

HIS-Select High Flow Nickel Affinity Gel is an immobilized metal-ion affinity chromatography (IMAC) gel that is designed to specifically bind His-tag proteins (histidine containing proteins) in chromatographic systems with pressures up to 200 psi and a maximum linear flow rate of 3,000 cm/hr.

Application

Affinity gel is suitable for the purification of histidine tagged proteins (His-tag proteins) in native and denaturing conditions.
Nickel Affinity Gel for the purification of His-tag® proteins (histidine tagged proteins). HIS-Select® High Flow (HF) brings the superior selectivity of HIS-Select technology to a highly cross-linked agarose for higher flow rates and mechanical stability under pressure. HIS-Select HF is designed for production scale purification and FPLC applications. As with other HIS-Select products, the non-charged, hydrophilic linkage of the nickel-nitrilotriacetic acid (Ni-NTA) chelate group to the agarose ensures true one-step purification.

Features and Benefits

  • High selectivity for higher purity
  • Unique non-charged hydrophilic linkage reduced non-specific binding
  • Binding Capacity for histidine-tagged protein (His-tag protein) is greater than 15 mg/mL
  • Highly cross-linked agarose matrix allows for purification under higher pressures
  • Binding under denaturing or non-denaturing conditions
  • One-step purification

Physical form

1:1 suspension in a 30% ethanol solution

Preparation Note

Ethanol must be removed prior to use. Wash affinity gel in deionized water to remove the ethanol, then equilibrate with equilibration buffer that is provided.

Legal Information

FPLC is a trademark of Cytiva
HIS TAG is a registered trademark of Merck KGaA, Darmstadt, Germany
HIS-Select is a registered trademark of Merck KGaA, Darmstadt, Germany

Signal Word

Danger

Hazard Classifications

Acute Tox. 4 Oral - Aquatic Chronic 2 - Carc. 1A Inhalation - Flam. Liq. 3 - Muta. 2 - Repr. 1B - Resp. Sens. 1 - Skin Irrit. 2 - Skin Sens. 1 - STOT RE 1 - STOT SE 2

Target Organs

Eyes,Central nervous system, Respiratory Tract

WGK

WGK 3

Flash Point(F)

88.0 °F

Flash Point(C)

31.1 °C

Personal Protective Equipment

dust mask type N95 (US), Eyeshields, Gloves

Certificates of Analysis (COA)

Search for Certificates of Analysis (COA) by entering the products Lot/Batch Number. Lot and Batch Numbers can be found on a product’s label following the words ‘Lot’ or ‘Batch’.

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Obtaining of Agr2 Specific Antibodies and Determination of the Agr2 Protein Distribution Pattern during Early Embryonic Development and Tadpole Regeneration in Xenopus laevis
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l-Asparaginase (EC 3.5.1.1) is an important medical enzyme that catalysis the hydrolysis of l-asparagine to aspartic acid and ammonium. For over four decades l. asparaginase utic agent for the treatment of a variety of lymphoproliferative disorders and lymphoma such as
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Cellular signaling is often propagated by multivalent interactions. Multivalency creates avidity, allowing stable biophysical recognition. Multivalency is an attractive strategy for achieving potent binding to protein targets, as the affinity of bivalent ligands is often greater than the sum of
Finaritra Raoelijaona et al.
RNA biology, 15(9), 1174-1180 (2018-09-04)
Ribosome biogenesis requires a variety of trans-acting factors in order to produce functional ribosomal subunits. In human cells, the complex formed by the proteins hNob1 and hPno1 is crucial to the site 3 cleavage occurring at the 3'-end of 18S
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Journal of medicinal chemistry, 63(16), 8857-8866 (2020-06-12)
DNA-encoded small molecule libraries (DELs) have enabled discovery of novel inhibitors for many distinct protein targets of therapeutic value. We demonstrate a new approach applying machine learning to DEL selection data by identifying active molecules from large libraries of commercial

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