scholarly journals Glutathione-mediated transfer of Cu(I) into phytochelatins

1995 ◽  
Vol 307 (3) ◽  
pp. 697-705 ◽  
Author(s):  
R K Mehra ◽  
P Mulchandani

Room temperature luminescence attributable to Cu(I)-thiolate clusters has been used to probe the transfer of Cu(I) from Cu(I)-glutathione complex to rabbit liver thionein-II and plant metal-binding peptides phytochelatins (gamma-Glu-Cys)2Gly, (gamma-Glu-Cys)3Gly and (gamma-Glu-Cys)4Gly. Reconstitutions were also performed using CuC1. The Cu(I)-binding stoichiometry of metallothionein or phytochelatins was generally independent of the Cu(I) donor. However, the luminescence of the reconstituted metallothionein or phytochelatins was higher when Cu(I)-GSH was the donor. This higher luminescence is presumably due to the stabilizing effect of GSH on Cu(I)-thiolate clusters. As expected, 12 Cu(I) ions were bound per molecule of metallothionein. The Cu(I) binding to phytochelatins depended on their chain length; the binding stoichiometries being 1.25, 2.0 and 2.5 for (gamma-Glu-Cys)2Gly, (gamma-Glu-Cys)3Gly and (gamma-Glu-Cys)4Gly respectively at neutral pH. A reduced stoichiometry for the longer phytochelatins was observed at alkaline pH. No GSH was found to associate with phytochelatins by a gel-filtration assay. The Cu(I) binding to (gamma-Glu-Cys)2Gly and (gamma-Glu-Cys)3Gly occurred in a biphasic manner in the sense that the relative luminescence increased approximately linearly with the amount of Cu(I) up to a certain molar ratio whereafter luminescence increased dramatically upon the binding of additional Cu(I). The luminescence intensity declined once the metal-binding sites were saturated. In analogy with the studies on metallothioneins, biphasic luminescence suggests the formation of two types of Cu(I) clusters in phytochelatins.

1973 ◽  
Vol 133 (4) ◽  
pp. 749-754 ◽  
Author(s):  
Peter A. Charlwood

Equilibrium-dialysis experiments showed that Tris or citrate in the solution prevented copper from occupying completely the specific metal-binding sites on human transferrin. Differential measurements of sedimentation velocity under conditions where two atoms of copper per molecule of protein were bound showed an increase in s020,w, relative to that of the apoprotein, practically the same as that produced by two atoms of iron. Gel-filtration experiments made under the same conditions to investigate the effect of copper binding on the Stokes radius of the protein showed merely that it lost most of the metal as it passed down the column.


2021 ◽  
Vol 217 ◽  
pp. 111374
Author(s):  
Satoshi Nagao ◽  
Ayaka Idomoto ◽  
Naoki Shibata ◽  
Yoshiki Higuchi ◽  
Shun Hirota

2021 ◽  
Author(s):  
Daniel Kovacs ◽  
Daniel Kocsi ◽  
Jordann A. L. Wells ◽  
Salauat R. Kiraev ◽  
Eszter Borbas

A series of luminescent lanthanide(III) complexes consisting of 1,4,7-triazacyclononane frameworks and three secondary amide-linked carbostyril antennae were synthesised. The metal binding sites were augmented with two pyridylcarboxylate donors yielding octadentate...


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ryan Feehan ◽  
Meghan W. Franklin ◽  
Joanna S. G. Slusky

AbstractMetalloenzymes are 40% of all enzymes and can perform all seven classes of enzyme reactions. Because of the physicochemical similarities between the active sites of metalloenzymes and inactive metal binding sites, it is challenging to differentiate between them. Yet distinguishing these two classes is critical for the identification of both native and designed enzymes. Because of similarities between catalytic and non-catalytic  metal binding sites, finding physicochemical features that distinguish these two types of metal sites can indicate aspects that are critical to enzyme function. In this work, we develop the largest structural dataset of enzymatic and non-enzymatic metalloprotein sites to date. We then use a decision-tree ensemble machine learning model to classify metals bound to proteins as enzymatic or non-enzymatic with 92.2% precision and 90.1% recall. Our model scores electrostatic and pocket lining features as more important than pocket volume, despite the fact that volume is the most quantitatively different feature between enzyme and non-enzymatic sites. Finally, we find our model has overall better performance in a side-to-side comparison against other methods that differentiate enzymatic from non-enzymatic sequences. We anticipate that our model’s ability to correctly identify which metal sites are responsible for enzymatic activity could enable identification of new enzymatic mechanisms and de novo enzyme design.


2009 ◽  
pp. 7934 ◽  
Author(s):  
Kathrin Gilg ◽  
Tobias Mayer ◽  
Natascha Ghaschghaie ◽  
Peter Klüfers

2003 ◽  
Vol 2003 (13) ◽  
pp. 2406-2412 ◽  
Author(s):  
Pierre R. Marcoux ◽  
Bernold Hasenknopf ◽  
Jacqueline Vaissermann ◽  
Pierre Gouzerh

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