The Role of GMXCXXC Metal Binding Sites in the Copper-induced Redistribution of the Menkes Protein

The Wilson protein (ATP7B) is a copper-transporting CPx-type ATPase defective in the copper toxicity disorder Wilson disease. In hepatocytes, ATP7B delivers copper to apo-ceruloplasmin and mediates the excretion of excess copper into bile. These distinct functions require the protein to localize at two different subcellular compartments. At the trans-Golgi network, ATP7B transports copper for incorporation into apo-ceruloplasmin. When intracellular copper levels are increased, ATP7B traffics to post-Golgi vesicles in close proximity to the canalicular membrane to facilitate biliary copper excretion. In the present study, we investigated the role of the six N-terminal MBSs (metal-binding sites) in the trafficking process. Using site-directed mutagenesis, we mutated or deleted various combinations of the MBSs and assessed the effect of these changes on the localization and trafficking of ATP7B. Results show that the MBSs required for trafficking are the same as those previously found essential for the copper transport function. Either MBS 5 or MBS 6 alone was sufficient to support the redistribution of ATP7B to vesicular compartments. The first three N-terminal motifs were not required for copper-dependent intracellular trafficking and could not functionally replace sites 4–6 when placed in the same sequence position. Furthermore, the N-terminal region encompassing MBSs 1–5 (amino acids 64–540) was not essential for trafficking, with only one MBS close to the membrane channel, necessary and sufficient to support trafficking. Our findings were similar to those obtained for the closely related ATP7A protein, suggesting similar mechanisms for trafficking between copper-transporting CPx-type ATPases.

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Crystal Structures of Apo and Metal-Bound Forms of the UreE Protein fromHelicobacter pylori: Role of Multiple Metal Binding Sites,

Biochemistry ◽

10.1021/bi100372h ◽

2010 ◽

Vol 49 (33) ◽

pp. 7080-7088 ◽

Cited By ~ 31

Author(s):

Rong Shi ◽

Christine Munger ◽

Abdalin Asinas ◽

Stéphane L. Benoit ◽

Erica Miller ◽

...

Keyword(s):

Crystal Structures ◽

Metal Binding ◽

Binding Sites ◽

Metal Binding Sites

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BioMetAll: Identifying Metal-Binding Sites in Proteins from Backbone Preorganization

10.26434/chemrxiv.12668651 ◽

2020 ◽

Author(s):

José-Emilio Sánchez-Aparicio ◽

Laura Tiessler-Sala ◽

Lorea Velasco-Carneros ◽

Lorena Roldán-Martín ◽

Giuseppe Sciortino ◽

...

Keyword(s):

Metal Binding ◽

Binding Sites ◽

De Novo ◽

Conformational Transition ◽

Protein Backbone ◽

Metal Binding Sites ◽

Structural Match ◽

Geometric Descriptors ◽

Metal Migration

<div><div><div><p>With a large amount of research dedicated to decoding how metallic species bind to protein, in silico methods are interesting allies for experimental procedures. To date, computational predictors mostly work by identifying the best possible sequence or structural match of the target protein with metal binding templates. These approaches are fundamentally focused on the first coordination sphere of the metal. Here, we present the BioMetAll predictor that is based on a different postulate: the formation of a potential metal-binding site is related to the geometric organization of the protein backbone. We first report the set of convenient geometric descriptors of the backbone needed for the algorithm and their parametrization from a statistical analysis. Then, the successful benchmark of BioMetAll on a set of more than 50 metal-binding X-Ray structures is presented. Because BioMetAll allows structural predictions regardless of the exact geometry of the side chains, it appears extremely valuable for systems which structures (either experimental or theoretical) are not optimal for metal binding sites. We report here its application on three different challenging cases i) the modulation of metal-binding sites during conformational transition in human serum albumin, ii) the identification of possible routes of metal migration in hemocyanins, and iii) the prediction of mutations to generate convenient metal-binding sites for de novo biocatalysts. This study shows that BioMetAll offers a versatile platform for numerous fields of research at the interface between inorganic chemistry and biology, and allows to highlight the role of the preorganization of the protein backbone as a marker for metal binding.</p></div></div></div>

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BioMetAll: Identifying Metal-Binding Sites in Proteins from Backbone Preorganization

10.26434/chemrxiv.12668651.v1 ◽

2020 ◽

Author(s):

José-Emilio Sánchez-Aparicio ◽

Laura Tiessler-Sala ◽

Lorea Velasco-Carneros ◽

Lorena Roldán-Martín ◽

Giuseppe Sciortino ◽

...

Keyword(s):

Metal Binding ◽

Binding Sites ◽

De Novo ◽

Conformational Transition ◽

Protein Backbone ◽

Metal Binding Sites ◽

Structural Match ◽

Geometric Descriptors ◽

Metal Migration

<div><div><div><p>With a large amount of research dedicated to decoding how metallic species bind to protein, in silico methods are interesting allies for experimental procedures. To date, computational predictors mostly work by identifying the best possible sequence or structural match of the target protein with metal binding templates. These approaches are fundamentally focused on the first coordination sphere of the metal. Here, we present the BioMetAll predictor that is based on a different postulate: the formation of a potential metal-binding site is related to the geometric organization of the protein backbone. We first report the set of convenient geometric descriptors of the backbone needed for the algorithm and their parametrization from a statistical analysis. Then, the successful benchmark of BioMetAll on a set of more than 50 metal-binding X-Ray structures is presented. Because BioMetAll allows structural predictions regardless of the exact geometry of the side chains, it appears extremely valuable for systems which structures (either experimental or theoretical) are not optimal for metal binding sites. We report here its application on three different challenging cases i) the modulation of metal-binding sites during conformational transition in human serum albumin, ii) the identification of possible routes of metal migration in hemocyanins, and iii) the prediction of mutations to generate convenient metal-binding sites for de novo biocatalysts. This study shows that BioMetAll offers a versatile platform for numerous fields of research at the interface between inorganic chemistry and biology, and allows to highlight the role of the preorganization of the protein backbone as a marker for metal binding.</p></div></div></div>

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MetalExplorer, a Bioinformatics Tool for the Improved Prediction of Eight Types of Metal-Binding Sites Using a Random Forest Algorithm with Two- Step Feature Selection

Current Bioinformatics ◽

10.2174/2468422806666160618091522 ◽

2017 ◽

Vol 12 (6) ◽

Cited By ~ 6

Author(s):

Jiangning Song ◽

Chen Li ◽

Cheng Zheng ◽

Jerico Revote ◽

Ziding Zhang ◽

...

Keyword(s):

Feature Selection ◽

Random Forest ◽

Metal Binding ◽

Binding Sites ◽

Random Forest Algorithm ◽

Bioinformatics Tool ◽

Metal Binding Sites

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Nonequivalence of the metal binding sites of conalbumin. Calorimetric and spectrophotometric studies of aluminum binding.

Journal of Biological Chemistry ◽

10.1016/s0021-9258(19)41152-6 ◽

1975 ◽

Vol 250 (15) ◽

pp. 6022-6025 ◽

Cited By ~ 2

Author(s):

J W Donovan ◽

K D Ross

Keyword(s):

Metal Binding ◽

Binding Sites ◽

Metal Binding Sites

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

Journal of Inorganic Biochemistry ◽

10.1016/j.jinorgbio.2021.111374 ◽

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

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

Dalton Transactions ◽

10.1039/d1dt00616a ◽

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...

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Machine learning differentiates enzymatic and non-enzymatic metals in proteins

Nature Communications ◽

10.1038/s41467-021-24070-3 ◽

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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