A putative novel starch-binding domain revealed by in silico analysis of the N-terminal domain in bacterial amylomaltases from the family GH77

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.

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

Molecules ◽

10.3390/molecules26185704 ◽

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.

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The starch-binding domain family CBM41-An in silico analysis of evolutionary relationships

Proteins Structure Function and Bioinformatics ◽

10.1002/prot.25309 ◽

2017 ◽

Vol 85 (8) ◽

pp. 1480-1492 ◽

Cited By ~ 8

Author(s):

Štefan Janeček ◽

Katarína Majzlová ◽

Birte Svensson ◽

E. Ann MacGregor

Keyword(s):

In Silico ◽

In Silico Analysis ◽

Binding Domain ◽

Evolutionary Relationships ◽

Domain Family ◽

Silico Analysis

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In silico analysis of the interactions of certain flavonoids with the receptor-binding domain of 2019 novel coronavirus and cellular proteases and their pharmacokinetic properties

Journal of Biomolecular Structure and Dynamics ◽

10.1080/07391102.2020.1840444 ◽

2020 ◽

pp. 1-15

Author(s):

Erman Salih Istifli ◽

Paulo A. Netz ◽

Arzuhan Sihoglu Tepe ◽

Mehmet Tahir Husunet ◽

Cengiz Sarikurkcu ◽

...

Keyword(s):

Receptor Binding ◽

In Silico ◽

In Silico Analysis ◽

Binding Domain ◽

Receptor Binding Domain ◽

Pharmacokinetic Properties ◽

Novel Coronavirus ◽

Silico Analysis

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Assessment of the Interaction of Aggregatin Protein with Amyloid-Beta (Aβ) at the Molecular Level via In Silico Analysis

Acta Chimica Slovenica ◽

10.17344/acsi.2020.6175 ◽

2020 ◽

Vol 67 (4) ◽

pp. 1262-1272

Author(s):

Nail Besli ◽

Guven Yenmis

Keyword(s):

Amyloid Beta ◽

In Silico ◽

Susceptibility Gene ◽

3D Structure ◽

In Silico Analysis ◽

Imaging Genetics ◽

Dimensional Structure ◽

Aβ Peptide ◽

Terminal Domain ◽

Silico Analysis

Alzheimer’s disease is a major neurodegenerative illness whose prevalence is increasing worldwide but the molecular mechanism remains unclear. There is some scientific evidence that the molecular complexity of Alzheimer’s pathophysiology is associated with the formation of extracellular amyloid-beta plaques in the brain. A novel cross- phenotype association analysis of imaging genetics reported a brain atrophy susceptibility gene, namely FAM222A and the protein Aggregatin encoded by FAM222A interacts with amyloid-beta (Aβ)-peptide (1-42) through its N-terminal Aβ binding domain and facilitates Aβ aggregation. The function of Aggregatin protein is unknown, and its three-dimensional structure has not been analyzed experimentally yet. Our goal was to investigate the interaction of Aggregatin with Aβ in detail by in silico analysis, including the 3D structure prediction analysis of Aggregatin protein by homology modeling. Our analysis verified the interaction of the C-terminal domain of model protein with the N-terminal domain of Aβ. This is the first attempt to demonstrate the interaction of Aggregatin with the Aβ. These results confirmed in vitro and in vivo study reports claiming FAM222A helping to ease the aggregating of the Aβ-peptide.

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In Silico Analysis and Expression of Influenza Virus 3M2e-HA2 Chimer Protein Fused to C-Terminal Domain of Leishmania major HSP70

Jundishapur Journal of Microbiology ◽

10.5812/jjm.13777 ◽

2018 ◽

Vol 11 (8) ◽

Author(s):

Elahe Nazeri ◽

Behrokh Farahmand ◽

Fatemeh Fotouhi ◽

Mehrdad Hashemi ◽

Najme Taheri ◽

...

Keyword(s):

Influenza Virus ◽

In Silico ◽

Leishmania Major ◽

In Silico Analysis ◽

Terminal Domain ◽

Silico Analysis

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A systematic review with in silico analysis on transcriptomic profile of gallbladder carcinoma

Seminars in Oncology ◽

10.1053/j.seminoncol.2020.02.012 ◽

2020 ◽

Vol 47 (6) ◽

pp. 398-408

Author(s):

Sonam Tulsyan ◽

Showket Hussain ◽

Balraj Mittal ◽

Sundeep Singh Saluja ◽

Pranay Tanwar ◽

...

Keyword(s):

Systematic Review ◽

Gallbladder Carcinoma ◽

In Silico ◽

In Silico Analysis ◽

Transcriptomic Profile ◽

Silico Analysis

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In Silico Analysis of Single Nucleotide Polymorphism in Human Prothrombin Gene

10.1055/s-0039-1680245 ◽

2019 ◽

Author(s):

I. Farah ◽

A. El-Mubark ◽

M. Osman ◽

A. Soliman ◽

F. Ali ◽

...

Keyword(s):

Single Nucleotide Polymorphism ◽

In Silico ◽

In Silico Analysis ◽

Nucleotide Polymorphism ◽

Single Nucleotide ◽

Prothrombin Gene ◽

Silico Analysis

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Enalos Suite of Tools: Enhancing Cheminformatics and Nanoinfor - matics through KNIME

Current Medicinal Chemistry ◽

10.2174/0929867327666200727114410 ◽

2020 ◽

Vol 27 (38) ◽

pp. 6523-6535 ◽

Cited By ~ 3

Author(s):

Antreas Afantitis ◽

Andreas Tsoumanis ◽

Georgia Melagraki

Keyword(s):

Data Analysis ◽

Virtual Screening ◽

In Silico ◽

Model Development ◽

In Silico Analysis ◽

Material Design ◽

Efficient Solutions ◽

Biological Data ◽

Cost Efficient ◽

Silico Analysis

Drug discovery as well as (nano)material design projects demand the in silico analysis of large datasets of compounds with their corresponding properties/activities, as well as the retrieval and virtual screening of more structures in an effort to identify new potent hits. This is a demanding procedure for which various tools must be combined with different input and output formats. To automate the data analysis required we have developed the necessary tools to facilitate a variety of important tasks to construct workflows that will simplify the handling, processing and modeling of cheminformatics data and will provide time and cost efficient solutions, reproducible and easier to maintain. We therefore develop and present a toolbox of >25 processing modules, Enalos+ nodes, that provide very useful operations within KNIME platform for users interested in the nanoinformatics and cheminformatics analysis of chemical and biological data. With a user-friendly interface, Enalos+ Nodes provide a broad range of important functionalities including data mining and retrieval from large available databases and tools for robust and predictive model development and validation. Enalos+ Nodes are available through KNIME as add-ins and offer valuable tools for extracting useful information and analyzing experimental and virtual screening results in a chem- or nano- informatics framework. On top of that, in an effort to: (i) allow big data analysis through Enalos+ KNIME nodes, (ii) accelerate time demanding computations performed within Enalos+ KNIME nodes and (iii) propose new time and cost efficient nodes integrated within Enalos+ toolbox we have investigated and verified the advantage of GPU calculations within the Enalos+ nodes. Demonstration data sets, tutorial and educational videos allow the user to easily apprehend the functions of the nodes that can be applied for in silico analysis of data.

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