scholarly journals Predictive Pharmacological Activity of Galangal Rhizome (Alpinia galanga (L.) Willd.) Through in Silico Analysis as an Effort to Accelerate The Research of Indonesian Medicinal Plants

el–Hayah ◽  
2020 ◽  
Vol 7 (4) ◽  
pp. 160-166
Author(s):  
Ahmad Shobrun Jamil ◽  
Mujahidin Ahmad
Keyword(s):  
Biological Activity ◽  
In Silico ◽  
In Silico Analysis ◽  
Chemical Compounds ◽  
Scientific Data ◽  
Raw Material ◽  
Cellular Mechanisms ◽  
Alpinia Galanga ◽  
Plant Chemical ◽  
Silico Analysis

Indonesia has high biodiversity, especially plant species. There are many benefits that can be Obtained from various plants that grow in Indonesia, one of which is as a health supplement or medicinal raw material. Fast researches are important in the use of these plants so that bio-based products can be widely accepted. One of the important fast methods in analysing the benefits of plant chemical compounds is the in-silico prediction utilizing metadata spread over various pages providing scientific data about plants, their chemical compound content and biological activity. This study was focused on predictively observing the biological activity of the compounds in the rhizome of Alpinia galanga. The research method is by analysing metadata from various sources. Data on the content of chemical compounds can be accessed through the page https://phytochem.nal.usda.gov/, classification of metabolite compounds contained in plants using http://classyfire.wishartlab.com/, prediction of absorption, distribution, metabolism and excretion (ADME) uses http://www.swissadme.ch/, to determine the relationship between plant compounds and body proteins, http://www.swisstargetprediction.ch/ and prediction of cellular mechanisms seen through https://string-db.org. Based on in silico analysis by utilizing some of the above software, it can be seen that the rhizome of the Alpinia galangal plant has 80 active compounds, 47 have high bioavailability and 9 compounds with tight cell proteins. Based on in silico exploration, it is also known that A. galangal has potential as an antioxidant, antimicrobial, anti-cancer and various other pharmacological activities

scholarly journals In Silico Analysis of Antiviral Activity of Analog Curcumin Compounds

2020 ◽  
Vol 5 (3) ◽  
pp. 114-121
Author(s):  
Esti Mulatsari ◽  
Titiek Martati ◽  
Esti Mumpuni ◽  
Nidya Luciana Dewi
Keyword(s):  
Biological Activity ◽  
Molecular Docking ◽  
Antiviral Activity ◽  
In Silico ◽  
In Silico Analysis ◽  
Curcumin Analog ◽  
Compound Control ◽  
Docking Method ◽  
Silico Analysis ◽  
Molecular Docking Method

Some studies state that curcumin analog compounds can improve the bioavailability and biological activity of curcumin. One of the methods to predict the bioactivity of curcumin was computational using molecular docking method. This study has done bioactivity tests of curcumin analog compounds as antiviral using the molecular docking method with the software used are PLANTS, YASARA, MarvinSketch, and Pymol for visualization. This study used analog curcumin compounds derived from previous research. This study used five different viral reseptor types. The maraviroc, docosanol, ribavirin, and zanamivir were used as compound control in this research. The validated target protein consists of 5 (five) receptors with PDB codes 1V2I, 4WEG, 2HWI, 2QAD, and 3ALP. Based on this research, compounds that are predicted active as antiviral on each receptors that are: 2,5-bis(3,5-ditertbutyl-4-hydroxy benzyl)cyclopentanone (1V2I), 1,7- diphenyl-1,6-heptadiene-3,5-dione (4WEG), 1,7-bis(3,4-dibenzyloxiphenyl)-1,6-heptadiene-3,5-dione (2HWI), and 2,5-bis(3,5-ditertbutyl-4-hydroxybenzyl)cyclopentanone (3ALP). 

scholarly journals In-silico Analysis of Chemical Compounds in Green Tea Working on Activators PPAR-γ as Antiobesity

2019 ◽  
Vol 17 (2) ◽  
pp. 251
Author(s):  
Faridah Faridah ◽  
Esti Mumpuni ◽  
Yudha Iswara Yunanto
Keyword(s):  
Molecular Docking ◽  
Green Tea ◽  
In Silico ◽  
In Silico Analysis ◽  
Chemical Compounds ◽  
Ppar Γ ◽  
Silico Analysis ◽  
Native Ligand

Teh hijau dikenal banyak memiliki manfaat dan umum digunakan masyarakat sebagai antiobesitas, tetapi senyawa aktif yang berpotensi sebagai antiobesitas. Tujuan penelitian untuk mencari bahwa senyawa kimia yang terdapat dalam tanaman teh hijau mempunyai aktivitas sebagai antiobesitas pada reseptor PPAR-γ. Metode penelitian dilakukan dengan cara analisis in silico melalui molecular docking terhadap senyawa yang terdapat dalam tanaman teh hijau untuk mencari senyawa aktif dan memodelkan interaksi senyawa aktif pada reseptor yang berperan sebagai antiobesitas. Software yang digunakan adalah PLANTS, YASARA, ChemSketch, dan Pymol. Mula-mula dilakukan validasi internal pada reseptor PPAR-γ dengan kode 2ATH. Proses docking dilakukan terhadap native ligand, senyawa pembanding dan masing-masing senyawa uji dengan reseptor PPAR-γ yang sama, dan senyawa pembanding yang digunakan sebagai kontrol positif ialah Pioglitazone. Hasil penelitian menunjukan sisi aktif terdapat 3 senyawa aktif dengan sisi aktif ikatan ligan pada reseptor PPAR-γ yaitu ARG288, LYS367, PHE363, HIS323, HIS449, ILE326, MET364, LEU340, CYS285, SER342. Terdapat 3 senyawa aktif yaitu epigalokatekin-3-galat, epikatekin-3-galat dan teaflavin sebagai antiobesitas dengan mekanisme kerja mengaktivasi PPAR-γ.

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

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

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

2020 ◽  
Vol 27 (38) ◽  
pp. 6523-6535 ◽  
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.

In-Silico Analysis of Chromone Containing Sulfonamide Derivatives as Human Carbonic Anhydrase Inhibitors

2013 ◽  
Vol 9 (4) ◽  
pp. 608-616 ◽  
Author(s):  
Zaheer Ul-Haq ◽  
Saman Usmani ◽  
Uzma Mahmood ◽  
Mariya al-Rashida ◽  
Ghulam Abbas
Keyword(s):  
Carbonic Anhydrase ◽  
In Silico ◽  
In Silico Analysis ◽  
Carbonic Anhydrase Inhibitors ◽  
Human Carbonic Anhydrase ◽  
Silico Analysis

In-Vitro and In-Silico Characterization of Xylose Reductase from Emericella nidulans

2019 ◽  
Vol 13 (2) ◽  
pp. 159-170 ◽  
Author(s):  
Vishal Ahuja ◽  
Aashima Sharma ◽  
Ranju Kumari Rathour ◽  
Vaishali Sharma ◽  
Nidhi Rana ◽  
...  
Keyword(s):  
Production Process ◽  
In Silico ◽  
Xylose Reductase ◽  
In Silico Analysis ◽  
Emericella Nidulans ◽  
Higher Temperature ◽  
In Silico Characterization ◽  
Silico Analysis

Background: Lignocellulosic residues generated by various anthropogenic activities can be a potential raw material for many commercial products such as biofuels, organic acids and nutraceuticals including xylitol. Xylitol is a low-calorie nutritive sweetener for diabetic patients. Microbial production of xylitol can be helpful in overcoming the drawbacks of traditional chemical production process and lowring cost of production. Objective: Designing efficient production process needs the characterization of required enzyme/s. Hence current work was focused on in-vitro and in-silico characterization of xylose reductase from Emericella nidulans. Methods: Xylose reductase from one of the hyper-producer isolates, Emericella nidulans Xlt-11 was used for in-vitro characterization. For in-silico characterization, XR sequence (Accession No: Q5BGA7) was used. Results: Xylose reductase from various microorganisms has been studied but the quest for better enzymes, their stability at higher temperature and pH still continues. Xylose reductase from Emericella nidulans Xlt-11 was found NADH dependent and utilizes xylose as its sole substrate for xylitol production. In comparison to whole cells, enzyme exhibited higher enzyme activity at lower cofactor concentration and could tolerate higher substrate concentration. Thermal deactivation profile showed that whole cell catalysts were more stable than enzyme at higher temperature. In-silico analysis of XR sequence from Emericella nidulans (Accession No: Q5BGA7) suggested that the structure was dominated by random coiling. Enzyme sequences have conserved active site with net negative charge and PI value in acidic pH range. Conclusion: Current investigation supported the enzyme’s specific application i.e. bioconversion of xylose to xylitol due to its higher selectivity. In-silico analysis may provide significant structural and physiological information for modifications and improved stability.

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