scholarly journals Chemical modifications of metallothionein. Preparation and characterization of polymers

1984 ◽  
Vol 221 (3) ◽  
pp. 569-575 ◽  
Author(s):  
D M Templeton ◽  
M G Cherian

Reaction of rat liver metallothionein-II with two bifunctional cross-linking reagents, glutaraldehyde and dimethyl suberimidate, produces high yields of polymeric species. It is argued that cross-linking is trapping preformed aggregates of the protein, which therefore represent a stabilized quaternary structure of metallothionein. The two polymeric species differ in a number of respects. With dimethyl suberimidate, the polymer retains all metal-binding sites of the monomer, and has an unaltered isoelectric point. Reaction with glutaraldehyde causes loss of one or two Cd2+/Zn2+-binding sites and elevates the pI. Both species are nearly spherical aggregates, in contrast with the highly asymmetrical metallothionein. Both polymers are linked through lysine residues, and the thiol groups remain reduced. The biological significance of these aggregates is discussed.

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

1984 ◽  
Vol 106 (6) ◽  
pp. 1641-1645 ◽  
Author(s):  
Kim Henrick ◽  
Leonard F. Lindoy ◽  
Mary McPartlin ◽  
Peter A. Tasker ◽  
Michael P. Wood

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