Virtual Screening, Molecular Docking, and ADME/T Analysis of Natural Product Library against Cell Invasion Protein SipB from Salmonella enterica serotype typhi: In 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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In silico Analysis of AMP-activated Protein Kinase and Ligand-based Virtual Screening for Identification of Novel AMPK Activators

Current Computer - Aided Drug Design ◽

10.2174/1573409913666170309144722 ◽

2017 ◽

Vol 13 (3) ◽

Cited By ~ 2

Author(s):

Ammarah Ghaffar ◽

Sidra Batool ◽

Gohar Mushtaq ◽

Muhammad A. Kamal

Keyword(s):

Protein Kinase ◽

Virtual Screening ◽

In Silico ◽

In Silico Analysis ◽

Amp Activated Protein Kinase ◽

Silico Analysis

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Prediction Mechanism of Nevadensin as Antibacterial Agent against S. sanguinis: In vitro and In silico Studies

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207324666210707104440 ◽

2021 ◽

Vol 24 ◽

Author(s):

Aldina Amalia Nur Shadrina ◽

Yetty Herdiyati ◽

Ika Wiani ◽

Mieke Hemiawati Satari ◽

Dikdik Kurnia

Keyword(s):

Antibacterial Activity ◽

Molecular Docking ◽

Dental Caries ◽

Binding Affinity ◽

In Silico ◽

Antibacterial Agent ◽

In Silico Analysis ◽

Dna Gyrase ◽

Silico Analysis

Background: Streptococcus sanguinis can contribute to tooth demineralization, which can lead to dental caries. Antibiotics used indefinitely to treat dental caries can lead to bacterial resistance. Discovering new antibacterial agents from natural products like Ocimum basilicum will help combat antibiotic resistance. In silico analysis (molecular docking) can help determine the lead compound by studying the molecular interaction between the drug and the target receptor (MurA enzyme and DNA gyrase). It is a potential candidate for antibacterial drug development. Objective: The research objective is to isolate the secondary metabolite of O. basilicum extract that has activity against S. sanguinis through in vitro and in silico analysis. Methods: n-Hexane extract of O. basilicum was purified by combining column chromatography with bioactivity-guided. The in vitro antibacterial activity against S. sanguinis was determined using the disc diffusion and microdilution method, while molecular docking simulation of nevadensin (1) with MurA enzyme and DNA gyrase was performed used PyRx 0.8 program. Results: Nevadensin from O. basilicum was successfully isolated and characterized by spectroscopic methods. This compound showed antibacterial activity against S. sanguinis with MIC and MBC values of 3750 and 15000 μg/mL, respectively. In silico analysis showed that the binding affinity to MurA was -8.5 Kcal/mol, and the binding affinity to DNA gyrase was -6.7 Kcal/mol. The binding of nevadensin-MurA is greater than fosfomycin-MurA. Otherwise, Nevadensin-DNA gyrase has a weaker binding affinity than fluoroquinolone-DNA gyrase and chlorhexidine-DNA gyrase. Conclusion: Nevadensin showed potential as a new natural antibacterial agent by inhibiting the MurA enzyme rather than DNA gyrase.

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