Specific metal binding sites on calcified concretions in epithelial cells of the clam kidney

1981 ◽  
Vol 37 (7) ◽  
pp. 752-753 ◽  
Author(s):  
N. G. Carmichael ◽  
S. C. Bondy
Keyword(s):  
Epithelial Cells ◽  
Metal Binding ◽  
Binding Sites ◽  
Metal Binding Sites ◽  
Specific Metal

scholarly journals Comparison of the sedimentation and gel-filtration behaviour of human apotransferrin and its copper and iron complexes

1973 ◽  
Vol 133 (4) ◽  
pp. 749-754 ◽  
Author(s):  
Peter A. Charlwood
Keyword(s):  
Metal Binding ◽  
Binding Sites ◽  
Equilibrium Dialysis ◽  
Gel Filtration ◽  
Sedimentation Velocity ◽  
Copper Binding ◽  
Metal Binding Sites ◽  
Stokes Radius ◽  
Effect Of Copper ◽  
Specific Metal

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.

Supramolecular assembly of a hierarchically structured 3D potassium vanadate framework

CrystEngComm ◽  
2021 ◽  
Author(s):  
Simon Greiner ◽  
Montaha Anjass ◽  
Carsten Streb
Keyword(s):  
Solid State ◽  
Metal Binding ◽  
Binding Sites ◽  
Supramolecular Assembly ◽  
Metal Binding Sites ◽  
Solid State Materials ◽  
Specific Metal

We report how supramolecular host-guest assembly can be leveraged to close the gap between soluble polyoxometalates and functional solid-state materials. A polyoxovanadate cluster with specific metal binding sites is used...

scholarly journals CheckMyMetal: a macromolecular metal-binding validation tool

2017 ◽  
Vol 73 (3) ◽  
pp. 223-233 ◽  
Author(s):  
Heping Zheng ◽  
David R. Cooper ◽  
Przemyslaw J. Porebski ◽  
Ivan G. Shabalin ◽  
Katarzyna B. Handing ◽  
...  
Keyword(s):  
Metal Binding ◽  
Binding Sites ◽  
Metal Ion ◽  
Data Bank ◽  
Biological Processes ◽  
Metal Binding Site ◽  
Metal Binding Sites ◽  
Metal Site ◽  
Validation Tool ◽  
Specific Metal

Metals are essential in many biological processes, and metal ions are modeled in roughly 40% of the macromolecular structures in the Protein Data Bank (PDB). However, a significant fraction of these structures contain poorly modeled metal-binding sites.CheckMyMetal(CMM) is an easy-to-use metal-binding site validation server for macromolecules that is freely available at http://csgid.org/csgid/metal_sites. TheCMMserver can detect incorrect metal assignments as well as geometrical and other irregularities in the metal-binding sites. Guidelines for metal-site modeling and validation in macromolecules are illustrated by several practical examples grouped by the type of metal. These examples showCMMusers (and crystallographers in general) problems they may encounter during the modeling of a specific metal ion.

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.

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