Heavy metal binding sites in river water

Nature ◽  
1975 ◽  
Vol 256 (5516) ◽  
pp. 399-401 ◽  
Author(s):  
S. RAMAMOORTHY ◽  
D. J. KUSHNER
Keyword(s):  
Heavy Metal ◽  
River Water ◽  
Metal Binding ◽  
Binding Sites ◽  
Metal Binding Sites ◽  
Heavy Metal Binding

Heavy metal binding by mycorrhizal fungi

1994 ◽  
Vol 92 (2) ◽  
pp. 364-368 ◽  
Author(s):  
Ulrich Galli ◽  
Hannes Schuepp ◽  
Christian Brunold
Keyword(s):  
Heavy Metal ◽  
Metal Binding ◽  
Mycorrhizal Fungi ◽  
Heavy Metal Binding

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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