Exploiting 3D structural templates for detection of metal-binding sites in protein structures

2007 ◽  
Vol 70 (4) ◽  
pp. 1206-1218 ◽  
Author(s):  
Kshama Goyal ◽  
Shekhar C. Mande
Keyword(s):  
Metal Binding ◽  
Binding Sites ◽  
Protein Structures ◽  
Metal Binding Sites

Prediction of transition metal-binding sites from apo protein structures

2007 ◽  
Vol 70 (1) ◽  
pp. 208-217 ◽  
Author(s):  
Mariana Babor ◽  
Sergey Gerzon ◽  
Barak Raveh ◽  
Vladimir Sobolev ◽  
Marvin Edelman
Keyword(s):  
Transition Metal ◽  
Metal Binding ◽  
Binding Sites ◽  
Protein Structures ◽  
Metal Binding Sites

scholarly journals Check your metal - not every density blob is a water molecule

2014 ◽  
Vol 70 (a1) ◽  
pp. C1483-C1483
Author(s):  
Heping Zheng ◽  
Mahendra Chordia ◽  
David Cooper ◽  
Ivan Shabalin ◽  
Maksymilian Chruszcz ◽  
...  
Keyword(s):  
Coordination Chemistry ◽  
Metal Binding ◽  
Binding Sites ◽  
Metal Ion ◽  
Protein Structures ◽  
Data Bank ◽  
Careful Examination ◽  
Biological Macromolecules ◽  
Metal Binding Sites ◽  
Heterogeneous Chemical

Metals play vital roles in both the mechanism and architecture of biological macromolecules, and are the most frequently encountered ligands (i.e. non-solvent heterogeneous chemical atoms) in the determination of macromolecular crystal structures. However, metal coordinating environments in protein structures are not always easy to check in routine validation procedures, resulting in an abundance of misidentified and/or suboptimally modeled metal ions in the Protein Data Bank (PDB). We present a solution to identify these problems in three distinct yet related aspects: (1) coordination chemistry; (2) agreement of experimental B-factors and occupancy; and (3) the composition and motif of the metal binding environment. Due to additional strain introduced by macromolecular backbones, the patterns of coordination of metal binding sites in metal-containing macromolecules are more complex and diverse than those found in inorganic or organometallic chemistry. These complications make a comprehensive library of "permitted" coordination chemistry in protein structures less feasible, and the usage of global parameters such as the bond valence method more practical, in the determination and validation of metal binding environments. Although they are relatively infrequent, there are also cases where the experimental B-factor or occupancy of a metal ion suggests careful examination. We have developed a web-based tool called CheckMyMetal [1](http://csgid.org/csgid/metal_sites/) for the quick validation of metal binding sites. Moreover, the acquired knowledge of the composition and spatial arrangement (motif) of the coordinating atoms around the metal ion may also help in the modeling of metal binding sites in macromolecular structures. All of the studies described herein were performed using the NEIGHBORHOOD SQL database [2], which connects information about all modeled non-solvent heterogeneous chemical motifs in PDB structure by vectors describing all contacts to neighboring residues and atoms. NEIGHBORHOOD has broad applications for the validation and data mining of ligand binding environments in the PDB.

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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