NMR Structure of Varkud Satellite Ribozyme Stem−Loop V in the Presence of Magnesium Ions and Localization of Metal-Binding Sites†,‡

Biochemistry ◽  
2006 ◽  
Vol 45 (35) ◽  
pp. 10591-10605 ◽  
Author(s):  
Dean O. Campbell ◽  
Patricia Bouchard ◽  
Geneviève Desjardins ◽  
Pascale Legault
Keyword(s):  
Metal Binding ◽  
Binding Sites ◽  
Nmr Structure ◽  
Magnesium Ions ◽  
Stem Loop ◽  
Metal Binding Sites ◽  
Varkud Satellite Ribozyme

scholarly journals Metal Binding Sites in Plant Soluble Inorganic Pyrophosphatases. An Example of the Use of ROSETTA Design and Hidden Markov Models to Guide the Homology Modeling of Proteins

2017 ◽  
Vol 56 (1) ◽  
Author(s):  
Luis Rosales-León Rosales-León ◽  
Eric Edmundo Hernández-Domínguez ◽  
Samantha Gaytán-Mondragón ◽  
Rogelio Rodríguez-Sotres
Keyword(s):  
Homology Modeling ◽  
Metal Binding ◽  
Binding Sites ◽  
In Silico ◽  
Markov Models ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Magnesium Ions ◽  
Three Dimensional Structure ◽  
Metal Binding Sites

In contrast to their counterparts in bacteria and animals the soluble inorganic pyrophosphatases from plant cells are active as monomers. The isoforms 1 and 4 from <em>Arabidopsis thaliana</em> have been characterized with more detail, but their three-dimensional structure is unavailable. Here, a recently published protocol (ROSETTA design-HMMer), is used to guide well-known techniques for homology-modeling, in the production of reliable models for the three-dimensional structure of these two arabidopsis isoforms. Their interaction with magnesium ions and pyrophosphate is analyzed <em>in silico</em>in silico.

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

scholarly journals Developing Remote Metal Binding Sites in Heteropolymolybdates

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