scholarly journals Bioinformatics of Metalloproteins and Metalloproteomes

Molecules ◽  
2020 ◽  
Vol 25 (15) ◽  
pp. 3366
Author(s):  
Yan Zhang ◽  
Junge Zheng
Keyword(s):  
Comparative Genomics ◽  
Metal Binding ◽  
Computational Prediction ◽  
Bioinformatic Analysis ◽  
Research Progress ◽  
Evolutionary Patterns ◽  
Cellular Processes ◽  
Metal Binding Sites ◽  
Domains Of Life ◽  
Systematic Understanding

Trace metals are inorganic elements that are required for all organisms in very low quantities. They serve as cofactors and activators of metalloproteins involved in a variety of key cellular processes. While substantial effort has been made in experimental characterization of metalloproteins and their functions, the application of bioinformatics in the research of metalloproteins and metalloproteomes is still limited. In the last few years, computational prediction and comparative genomics of metalloprotein genes have arisen, which provide significant insights into their distribution, function, and evolution in nature. This review aims to offer an overview of recent advances in bioinformatic analysis of metalloproteins, mainly focusing on metalloprotein prediction and the use of different metals across the tree of life. We describe current computational approaches for the identification of metalloprotein genes and metal-binding sites/patterns in proteins, and then introduce a set of related databases. Furthermore, we discuss the latest research progress in comparative genomics of several important metals in both prokaryotes and eukaryotes, which demonstrates divergent and dynamic evolutionary patterns of different metalloprotein families and metalloproteomes. Overall, bioinformatic studies of metalloproteins provide a foundation for systematic understanding of trace metal utilization in all three domains of life.

Recent development of machine learning methods in sumoylation sites prediction

2021 ◽  
Vol 28 ◽  
Author(s):  
Yi-Wei Zhao ◽  
Shihua Zhang ◽  
Hui Ding
Keyword(s):  
Machine Learning ◽  
Molecular Mechanisms ◽  
Prediction Models ◽  
Computational Prediction ◽  
Research Progress ◽  
Post Translational Modification ◽  
Sumoylation Site ◽  
Cellular Processes ◽  
Online Tools ◽  
Experimental Approaches

: Sumoylation of proteins is an important reversible post-translational modification of proteins and mediates a variety of cellular processes. Sumo-modified proteins can change their subcellular localization, activity and stability. In addition, it also plays an important role in various cellular processes such as transcriptional regulation and signal transduction. The abnormal sumoylation is involved in many diseases, including neurodegeneration and immune-related diseases, as well as the development of cancer. Therefore, identification of the sumoylation site (SUMO site) is fundamental to understanding their molecular mechanisms and regulatory roles. In contrast to labor-intensive and costly experimental approaches, computational prediction of sumoylation sites in silico also attracted much attention for its accuracy, convenience and speed. At present, many computational prediction models have been used to identify SUMO sites, but these contents have not been comprehensively summarized and reviewed. Therefore, the research progress of relevant models is summarized and discussed in this paper. We will briefly summarize the development of bioinformatics methods on sumoylation site prediction. We will mainly focus on the benchmark dataset construction, feature extraction, machine learning method, published results and online tools. We hope the review will provide more help for wet-experimental scholars.

scholarly journals The CopA2-Type P1B-Type ATPase CcoI Serves as Central Hub for cbb3-Type Cytochrome Oxidase Biogenesis

2021 ◽  
Vol 12 ◽  
Author(s):  
Andreea Andrei ◽  
Maria Agostina Di Renzo ◽  
Yavuz Öztürk ◽  
Alexandra Meisner ◽  
Noel Daum ◽  
...  
Keyword(s):  
Cytochrome Oxidase ◽  
Metal Binding ◽  
Binding Sites ◽  
Rhodobacter Capsulatus ◽  
Metal Transporters ◽  
Supported Membrane ◽  
Metal Binding Sites ◽  
Domains Of Life ◽  
Cu Homeostasis

Copper (Cu)-transporting P1B-type ATPases are ubiquitous metal transporters and crucial for maintaining Cu homeostasis in all domains of life. In bacteria, the P1B-type ATPase CopA is required for Cu-detoxification and exports excess Cu(I) in an ATP-dependent reaction from the cytosol into the periplasm. CopA is a member of the CopA1-type ATPase family and has been biochemically and structurally characterized in detail. In contrast, less is known about members of the CopA2-type ATPase family, which are predicted to transport Cu(I) into the periplasm for cuproprotein maturation. One example is CcoI, which is required for the maturation of cbb3-type cytochrome oxidase (cbb3-Cox) in different species. Here, we reconstituted purified CcoI of Rhodobacter capsulatus into liposomes and determined Cu transport using solid-supported membrane electrophysiology. The data demonstrate ATP-dependent Cu(I) translocation by CcoI, while no transport is observed in the presence of a non-hydrolysable ATP analog. CcoI contains two cytosolically exposed N-terminal metal binding sites (N-MBSs), which are both important, but not essential for Cu delivery to cbb3-Cox. CcoI and cbb3-Cox activity assays in the presence of different Cu concentrations suggest that the glutaredoxin-like N-MBS1 is primarily involved in regulating the ATPase activity of CcoI, while the CopZ-like N-MBS2 is involved in Cu(I) acquisition. The interaction of CcoI with periplasmic Cu chaperones was analyzed by genetically fusing CcoI to the chaperone SenC. The CcoI-SenC fusion protein was fully functional in vivo and sufficient to provide Cu for cbb3-Cox maturation. In summary, our data demonstrate that CcoI provides the link between the cytosolic and periplasmic Cu chaperone networks during cbb3-Cox assembly.

scholarly journals Fe-S cofactors in the SARS-CoV-2 RNA-dependent RNA polymerase are potential antiviral targets

Science ◽  
2021 ◽  
pp. eabi5224
Author(s):  
Nunziata Maio ◽  
Bernard A. P. Lafont ◽  
Debangsu Sil ◽  
Yan Li ◽  
J. Martin Bollinger ◽  
...  
Keyword(s):  
Rna Polymerase ◽  
Metal Binding ◽  
Rna Dependent Rna Polymerase ◽  
Iron Sulfur Clusters ◽  
Metal Binding Sites ◽  
Iron Sulfur ◽  
Cryo Electron Microscopy ◽  
Metal Cofactors ◽  
Antiviral Targets ◽  
Viral Helicase

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causal agent of coronavirus disease 2019 (COVID-19), uses an RNA-dependent RNA polymerase (RdRp) for the replication of its genome and the transcription of its genes. We found that the catalytic subunit of the RdRp, nsp12, ligates two iron-sulfur metal cofactors in sites that were modeled as zinc centers in the available cryo-electron microscopy structures of the RdRp complex. These metal binding sites are essential for replication and for interaction with the viral helicase. Oxidation of the clusters by the stable nitroxide TEMPOL caused their disassembly, potently inhibited the RdRp, and blocked SARS-CoV-2 replication in cell culture. These iron-sulfur clusters thus serve as cofactors for the SARS-CoV-2 RdRp and are targets for therapy of COVID-19.

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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