scholarly journals In Silico Analysis of Fungal and Chloride-Dependent α-Amylases within the Family GH13 with Identification of Possible Secondary Surface-Binding Sites

Molecules ◽  
2021 ◽  
Vol 26 (18) ◽  
pp. 5704
Author(s):  
Zuzana Janíčková ◽  
Štefan Janeček
Keyword(s):  
Binding Sites ◽  
In Silico ◽  
Homo Sapiens ◽  
In Silico Analysis ◽  
Surface Binding ◽  
Conserved Sequence ◽  
Evolutionary Relatedness ◽  
The Family ◽  
Tim Barrel ◽  
Silico Analysis

This study brings a detailed bioinformatics analysis of fungal and chloride-dependent α-amylases from the family GH13. Overall, 268 α-amylase sequences were retrieved from subfamilies GH13_1 (39 sequences), GH13_5 (35 sequences), GH13_15 (28 sequences), GH13_24 (23 sequences), GH13_32 (140 sequences) and GH13_42 (3 sequences). Eight conserved sequence regions (CSRs) characteristic for the family GH13 were identified in all sequences and respective sequence logos were analysed in an effort to identify unique sequence features of each subfamily. The main emphasis was given on the subfamily GH13_32 since it contains both fungal α-amylases and their bacterial chloride-activated counterparts. In addition to in silico analysis focused on eventual ability to bind the chloride anion, the property typical mainly for animal α-amylases from subfamilies GH13_15 and GH13_24, attention has been paid also to the potential presence of the so-called secondary surface-binding sites (SBSs) identified in complexed crystal structures of some particular α-amylases from the studied subfamilies. As template enzymes with already experimentally determined SBSs, the α-amylases from Aspergillus niger (GH13_1), Bacillus halmapalus, Bacillus paralicheniformis and Halothermothrix orenii (all from GH13_5) and Homo sapiens (saliva; GH13_24) were used. Evolutionary relationships between GH13 fungal and chloride-dependent α-amylases were demonstrated by two evolutionary trees—one based on the alignment of the segment of sequences spanning almost the entire catalytic TIM-barrel domain and the other one based on the alignment of eight extracted CSRs. Although both trees demonstrated similar results in terms of a closer evolutionary relatedness of subfamilies GH13_1 with GH13_42 including in a wider sense also the subfamily GH13_5 as well as for subfamilies GH13_32, GH13_15 and GH13_24, some subtle differences in clustering of particular α-amylases may nevertheless be observed.

scholarly journals Comparative in silico analysis identifies bona fide MyoD binding sites within the Myocyte Stress 1 gene promoter

2008 ◽  
Vol 9 (1) ◽  
pp. 50 ◽  
Author(s):  
Samir Ounzain ◽  
Caroline S Dacwag ◽  
Nilesh J Samani ◽  
Anthony N Imbalzano ◽  
Nelson W Chong
Keyword(s):  
Binding Sites ◽  
In Silico ◽  
Gene Promoter ◽  
In Silico Analysis ◽  
Bona Fide ◽  
Silico Analysis

In silico, Physico-chemical characterization and analysis of Sandalwood proteins

2018 ◽  
Vol 3 (02) ◽  
pp. 150-157
Author(s):  
Asad Amir ◽  
Neelesh Kapoor ◽  
Hirdesh Kumar ◽  
Mohd. Tariq ◽  
Mohd. Asif Siddiqui
Keyword(s):  
Secondary Structure ◽  
In Silico ◽  
Chemical Characterization ◽  
In Silico Analysis ◽  
Chemical Parameters ◽  
Physico Chemical ◽  
In Silico Studies ◽  
The Family ◽  
Silico Analysis

Sandalwood is a commercially and culturally important plant species belonging to the family Santalaceae and the genus Santalum. In Indian sandalwood is renowned for its oil, which is highly rated for its sweet, fragrant, persistent aroma and the fixative property which is highly demanded by the perfume industry. For better production and varieties, requires to understanding the functions of proteins, their analysis and characterization of proteins sequences and their structures, their localizations in cell and their interaction with other functional partner. Due to limited number of in silico studies on sandalwood, in the present study we have performed in silico analysis by characterization of sandalwood proteins. Total 23 proteins were obtained and characterization using UniProtKB, identifying their physico-chemical parameters using ProtParam tool and prediction of their secondary structure elements using GOR of all 23 proteins.

In silico analysis of hnRNP K binding sites in p21 mRNA

2010 ◽  
Vol 68 ◽  
pp. e354
Author(s):  
Mana Igarashi ◽  
Shingo Nakamura ◽  
Mika Kinoshita ◽  
Hirotaka J. Okano ◽  
Hideyuki Okano
Keyword(s):  
Binding Sites ◽  
In Silico ◽  
In Silico Analysis ◽  
Hnrnp K ◽  
Silico Analysis

scholarly journals Human astroviruses: in silico analysis of the untranslated region and putative binding sites of cellular proteins

2018 ◽  
Vol 46 (1) ◽  
pp. 1413-1424 ◽  
Author(s):  
Mónica De Nova-Ocampo ◽  
Mayra Cristina Soliman ◽  
Wendy Espinosa-Hernández ◽  
Cristina Velez-del Valle ◽  
Juan Salas-Benito ◽  
...  
Keyword(s):  
Binding Sites ◽  
In Silico ◽  
Untranslated Region ◽  
In Silico Analysis ◽  
Cellular Proteins ◽  
Human Astroviruses ◽  
Silico Analysis

scholarly journals Glutathione redox cycle enzymes as potential targets for heme-mediated oxidation under hemolysis: in silico analysis

2018 ◽  
Keyword(s):  
Binding Sites ◽  
In Silico ◽  
In Silico Analysis ◽  
Redox Cycle ◽  
Heme Binding ◽  
Binding Region ◽  
Active Centre ◽  
Glutathione Redox Cycle ◽  
Glutathione Redox ◽  
Silico Analysis

Glutathione (g-glutamylcysteinylglycine) redox homeostasis in human erythrocytes is dependent on the activities of glutathione peroxidase (GPX1, EC 1.11.1.9), glutathione reductase (GSR, EC 1.8.1.7), glutaredoxin 1 (GRX1) and NADPH-generating enzymes of pentose phosphate pathway, glucose-6-phosphate dehydrogenase (G6PD, EC 1.1.1.49) and 6-phosphogluconate dehydrogenase (PGD, EC 1.1.1.44). Free heme accumulation under hemolysis can affect proteins activity thereby in silico analysis of glutathione redox cycle enzymes structure was performed in order to reveal putative heme-binding sites and oxidizable cysteine residues. Protein annotations were taken from UniProt. Heme docking was performed by PatchDock with clustering RMSD 1,5 Å using PDB structures of proteins and heme. Cysteines oxidation potential was estimated by Cy-Preds. Heme binding GSR monomers (1DNC, 3DJJ, 3DK9, 2GRT) and dimers (3SQP, 2GH5) was predicted through His81 close to interchain disulfide bond and through Cys59 near FAD and GSSG binding sites. Heme-binding areas in GPX1 (2F8A) and GPX3 (2R37) also were revealed in the interchain region and in active centre (His80). GLRX1 (4RQR) was predicted to bind heme almost exclusively near the N-end in spite of accessibility of all cysteines including CPYC motif in active centre. G6PD monomer (2BH9, 5UKW) revealed heme-docking areas in NADP+ binding region and a-helix 437–447 located in dimer 2BHL at the interchain surface. Heme docking to PGD (4GWG, 4GWK) was in substrate binding region near His187. So enzymes active centres and chain interaction regions were revealed in the most of heme docking variants. From one (in PGD) to three (GSR) cysteines susceptible to oxidation were found in each protein including cysteines that were predicted to bind heme. Heme-mediated oxidative effect on glutathione redox cycle enzymes in erythrocytes and blood plasma could be an important mechanism of hemolysis progression under stress and pathologies.

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