The location of the divalent metal binding sites and the light chain subunits of vertebrate myosin

Biochemistry ◽  
1977 ◽  
Vol 16 (1) ◽  
pp. 59-67 ◽  
Author(s):  
Clive R. Bagshaw
Keyword(s):  
Light Chain ◽  
Metal Binding ◽  
Binding Sites ◽  
Divalent Metal ◽  
Metal Binding Sites

MONOCLONAL ANTIBODIES THAT RECOGNIZE Ca2+-INDUCED CONFORMER OF PROTEIN C, INDEPENDENT OF GLA RESIDUES

1987 ◽  
Author(s):  
T Sugo ◽  
S Tanabe ◽  
K Shinoda ◽  
M Matsuda
Keyword(s):  
Monoclonal Antibodies ◽  
Light Chain ◽  
Metal Binding ◽  
Binding Sites ◽  
Plasma Proteins ◽  
Protein C ◽  
The Other ◽  
Metal Binding Sites ◽  
Direct Elisa ◽  
Gla Domain

Monoclonal antibodies (MCA’s) were prepared against human protein C (PC) according to Köhler & Milstein, and those that recognize the Ca2+-dependent PC conformers were screened by direct ELISA in the presence of 2 mM either CaCl2 or EDTA. Out of nine MCAߣs thus screened, five MCA's designated as HPC-1˜5, respectively, were found to react with PC in the presence of Ca2+ but not EDTA. By SDS-PAGE coupled with Western Blotting performed in the presence of 2 mM CaCl2, we found that two MCA’s HPC-1 and 2, recognized the light chain, and two others, HPC-3 and 4, recognized the heavy chain of PC. But another MCA, HPC-5 was found to react with only non-reduced antigens. Further study showed that HPC-1 and 5 failed to react with the Gla-domainless PC, i.e. PC from which the N-terminal Gla-domain of the light chain had been cleaved off by α-chymotrypsin. However, all the other three MCA's retained the reactivity with the antigen in the presence of Ca2+ even after the Gla-domain had been removed. The binding of these MCA’s to PC in the presence of Ca2+ was found to be saturable with respect to the Ca2+ concentration and the half maximal binding for each MCA was calculated to be about 0.5+mM. Moreover, many other divalent cations such as Mg2+, Mn2+ , Ba2+, Zn2+, Co2+, Sr2+, were found to substitute for Ca2+ in inducing the metal ion-dependent but Gla-domain-independent conformer of PC.Cross-reactivity to other vitamin K-aependent plasma proteins was examined by direct ELISA; HPC-2 and 3 reacted solely to PC, but HPC-1 and 4 also reacted with prothrombin and HPC-5 with both prothrombin and factor X.These findings indicated that there are two or more metal binding sites besides the Gla-domain, possibly one in the light chain and the other(s) in the heavy chain. The presence of these metal binding sites may contribute to the unique conformer of vitamin K-dependent plasma proteins including protein C.

scholarly journals Rational design of metal-binding sites in domain-swapped myoglobin dimers

2021 ◽  
Vol 217 ◽  
pp. 111374
Author(s):  
Satoshi Nagao ◽  
Ayaka Idomoto ◽  
Naoki Shibata ◽  
Yoshiki Higuchi ◽  
Shun Hirota
Keyword(s):  
Metal Binding ◽  
Binding Sites ◽  
Rational Design ◽  
Metal Binding Sites

scholarly journals Electron transfer pathways in photoexcited lanthanide(III) complexes of picolinate ligands

2021 ◽  
Author(s):  
Daniel Kovacs ◽  
Daniel Kocsi ◽  
Jordann A. L. Wells ◽  
Salauat R. Kiraev ◽  
Eszter Borbas
Keyword(s):  
Electron Transfer ◽  
Metal Binding ◽  
Binding Sites ◽  
Secondary Amide ◽  
Metal Binding Sites ◽  
Electron Transfer Pathways

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...

scholarly journals Machine learning differentiates enzymatic and non-enzymatic metals in proteins

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ryan Feehan ◽  
Meghan W. Franklin ◽  
Joanna S. G. Slusky
Keyword(s):  
Machine Learning ◽  
Metal Binding ◽  
Binding Sites ◽  
Active Sites ◽  
De Novo ◽  
Enzyme Design ◽  
Metal Binding Sites ◽  
Ensemble Machine Learning ◽  
Machine Learning Model ◽  
Physicochemical Features

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.

The metal-binding sites of glycose phosphates

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