Site Selection in Tandem Arrays of Metal-Binding Domains

Effect of His-Tag on Expression, Purification, and Structure of Zinc Finger Protein, ZNF191(243-368)

Zinc finger proteins are associated with hereditary diseases and cancers. To obtain an adequate amount of zinc finger proteins for studying their properties, structure, and functions, many protein expression systems are used. ZNF191(243-368) is a zinc finger protein and can be fused with His-tag to generate fusion proteins such as His6-ZNF191(243-368) and ZNF191(243-368)-His8. The purification of His-tag protein using Ni-NTA resin can overcome the difficulty of ZNF191(243-368) separation caused by inclusion body formation. The influences of His-tag on ZNF191(243-368) properties and structure were investigated using spectrographic techniques and hydrolase experiment. Our findings suggest that insertion of a His-tag at the N-terminal or C-terminal end of ZNF191(243-368) has different effects on the protein. Therefore, an expression system should be considered based on the properties and structure of the protein. Furthermore, the hydrolase activity of ZNF191(243-368)-His8 has provided new insights into the design of biological functional molecules.

Ability of GHTD-amide and analogs to enhance insulin activity through zinc chelation and dispersal of insulin oligomers

GHTD-amide is a tetrapeptide originally isolated from human urine that has hypoglycemic activity. Insulin occurs in secretory granules of beta cells as zinc-stabilized hexamers and must disperse to monomeric form in order to bind to its receptor. The aim of this study was to identify whether GHTD-amide and an analog called ISF402 (VHTD-amide) reduce blood glucose through enhancement of insulin activity by dispersing oligomers of insulin. Peptides containing the HTD-amide sequence and a free alpha-amino group were optimal at binding Zn(2+) and adopting secondary structure in the presence of Zn(2+). Binding was concentration dependent and resulted in a 1:1 Zn:peptide complex. In vitro the tetrapeptides dispersed hexameric insulin to dimers and monomers. GHTD-amide and ISF402 potentiated the activity of hexameric insulin when co-injected into insulin resistant Zucker rats. Injection of peptides with insulin caused reductions in blood glucose and C-peptide significantly larger than achieved with insulin alone, and serum insulin time profiles were also altered consistent with a reduced clearance or enhanced dispersal of the injected insulin. Insulin potentiation by ISF402 was reduced when lispro insulin, which does not form zinc-stabilized hexamers, was used in place of hexameric zinc insulin. In conclusion, GHTD-amide and ISF402 are zinc binding peptides that disperse hexameric insulin in vitro, and potentiate the activity of hexameric insulin more so than monomeric lispro insulin. These results suggest that dispersal of hexameric insulin through chelation of Zn(2+) contributes to the hypoglycemic activity of these tetrapeptides.

Prediction of the functional class of metal-binding proteins from sequence derived physicochemical properties by support vector machine approach

Metal-binding proteins play important roles in structural stability, signaling, regulation, transport, immune response, metabolism control, and metal homeostasis. Because of their functional and sequence diversity, it is desirable to explore additional methods for predicting metal-binding proteins irrespective of sequence similarity. This work explores support vector machines (SVM) as such a method. SVM prediction systems were developed by using 53,333 metal-binding and 147,347 non-metal-binding proteins, and evaluated by an independent set of 31,448 metal-binding and 79,051 non-metal-binding proteins. The computed prediction accuracy is 86.3%, 81.6%, 83.5%, 94.0%, 81.2%, 85.4%, 77.6%, 90.4%, 90.9%, 74.9% and 78.1% for calcium-binding, cobalt-binding, copper-binding, iron-binding, magnesium-binding, manganese-binding, nickel-binding, potassium-binding, sodium-binding, zinc-binding, and all metal-binding proteins respectively. The accuracy for the non-member proteins of each class is 88.2%, 99.9%, 98.1%, 91.4%, 87.9%, 94.5%, 99.2%, 99.9%, 99.9%, 98.0%, and 88.0% respectively. Comparable accuracies were obtained by using a different SVM kernel function. Our method predicts 67% of the 87 metal-binding proteins non-homologous to any protein in the Swissprot database and 85.3% of the 333 proteins of known metal-binding domains as metal-binding. These suggest the usefulness of SVM for facilitating the prediction of metal-binding proteins. Our software can be accessed at the SVMProt server http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi.