scholarly journals In Silico analysis of Escherichia coli polyphosphate kinase (PPK) as a novel antimicrobial drug target and its high throughput virtual screening against PubChem library

2013 ◽  
Vol 9 (10) ◽  
pp. 518-523 ◽  
Author(s):  
Saurav Bhaskar Saha ◽  
◽  
Vivek Verma
Keyword(s):  
Escherichia Coli ◽  
Virtual Screening ◽  
High Throughput ◽  
In Silico ◽  
Drug Target ◽  
In Silico Analysis ◽  
Antimicrobial Drug ◽  
Polyphosphate Kinase ◽  
Silico Analysis ◽  
High Throughput Virtual Screening

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.

A novel compound active against SARS-CoV-2 targeting Uridylate-specific endoribonuclease (NendoU/NSP15): In silico and in vitro investigations

2021 ◽  
Author(s):  
Sumit Kumar ◽  
Yash Gupta ◽  
Samantha Zak ◽  
Charu Upadhyay ◽  
Neha Sharma ◽  
...  
Keyword(s):  
Virtual Screening ◽  
High Throughput ◽  
In Silico ◽  
Specific Enzyme ◽  
Cyclic Phosphates ◽  
High Throughput Virtual Screening

NendoU (NSP15) is an Mn(2+)-dependent, uridylate-specific enzyme, which leaves 2'-3'-cyclic phosphates 5' to the cleaved bond. Our in-house library was subjected to high throughput virtual screening (HTVS) to identify compounds...

A structural in silico analysis of the immunogenicity of l-asparaginase from Escherichia coli and Erwinia carotovora

Biologicals ◽  
2019 ◽  
Vol 59 ◽  
pp. 47-55 ◽  
Author(s):  
Lisandra Herrera Belén ◽  
Jorge Beltrán Lissabet ◽  
Carlota de Oliveira Rangel-Yagui ◽  
Brian Effer ◽  
Gisele Monteiro ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
In Silico ◽  
In Silico Analysis ◽  
Erwinia Carotovora ◽  
Silico Analysis

In Silico Analysis for Determination and Validation of Iron-Regulated Protein from Escherichia coli

2018 ◽  
Vol 25 (4) ◽  
pp. 1523-1537 ◽  
Author(s):  
Fateme Sefid ◽  
Armina Alagheband Bahrami ◽  
Maryam Darvish ◽  
Robab Nazarpour ◽  
Zahra Payandeh
Keyword(s):  
Escherichia Coli ◽  
In Silico ◽  
In Silico Analysis ◽  
Silico Analysis

scholarly journals In Silico De Novo Curcuminoid Derivatives From the Compound Library of Natural Products Research Laboratories Inhibit COVID-19 3CLpro Activity

2020 ◽  
Vol 15 (9) ◽  
pp. 1934578X2095326
Author(s):  
Jai-Sing Yang ◽  
Jo-Hua Chiang ◽  
Shih‑Chang Tsai ◽  
Yuan-Man Hsu ◽  
Da-Tian Bau ◽  
...  
Keyword(s):  
Natural Products ◽  
Virtual Screening ◽  
Binding Affinity ◽  
High Throughput ◽  
In Silico ◽  
De Novo ◽  
Compound Library ◽  
Therapeutic Success ◽  
High Throughput Virtual Screening

The coronavirus disease 2019 (COVID‐19) outbreak caused by the 2019 novel coronavirus (2019-nCOV) is becoming increasingly serious. In March 2019, the Food and Drug Administration (FDA) designated remdesivir for compassionate use to treat COVID-19. Thus, the development of novel antiviral agents, antibodies, and vaccines against COVID-19 is an urgent research subject. Many laboratories and research organizations are actively investing in the development of new compounds for COVID-19. Through in silico high-throughput virtual screening, we have recently identified compounds from the compound library of Natural Products Research Laboratories (NPRL) that can bind to COVID-19 3Lpro polyprotein and block COVID-19 3Lpro activity through in silico high-throughput virtual screening. Curcuminoid derivatives (including NPRL334, NPRL339, NPRL342, NPRL346, NPRL407, NPRL415, NPRL420, NPRL472, and NPRL473) display strong binding affinity to COVID-19 3Lpro polyprotein. The binding site of curcuminoid derivatives to COVID-19 3Lpro polyprotein is the same as that of the FDA-approved human immunodeficiency virus protease inhibitor (lopinavir) to COVID-19 3Lpro polyprotein. The binding affinity of curcuminoid derivatives to COVID-19 3Lpro is stronger than that of lopinavir and curcumin. Among curcuminoid derivatives, NPRL-334 revealed the strongest binding affinity to COVID-19 3Lpro polyprotein and is speculated to have an anti-COVID-19 effect. In vitro and in vivo ongoing experiments are currently underway to confirm the present findings. This study sheds light on the drug design for COVID-19 3Lpro polyprotein. Basing on lead compound development, we provide new insights on inhibiting COVID-19 attachment to cells, reducing COVID-19 infection rate and drug side effects, and increasing therapeutic success rate.

scholarly journals EchoLOCATION: an in silico analysis of the subcellular locations of Escherichia coli proteins and comparison with experimentally derived locations

2008 ◽  
Vol 25 (2) ◽  
pp. 163-166 ◽  
Author(s):  
Richard S. P. Horler ◽  
Andrew Butcher ◽  
Nikitas Papangelopoulos ◽  
Peter D. Ashton ◽  
Gavin H. Thomas
Keyword(s):  
Escherichia Coli ◽  
In Silico ◽  
In Silico Analysis ◽  
Silico Analysis

scholarly journals In Silico Screening of Available Drugs Targeting Non-Small Cell Lung Cancer Targets: A Drug Repurposing Approach

Pharmaceutics ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 59
Author(s):  
Muthu Kumar Thirunavukkarasu ◽  
Utid Suriya ◽  
Thanyada Rungrotmongkol ◽  
Ramanathan Karuppasamy
Keyword(s):  
Drug Resistance ◽  
Virtual Screening ◽  
High Throughput ◽  
In Silico ◽  
Limiting Factor ◽  
Energy Calculation ◽  
Lead Compounds ◽  
Study Results ◽  
Glide Docking ◽  
High Throughput Virtual Screening

The RAS–RAF–MEK–ERK pathway plays a key role in malevolent cell progression in many tumors. The high structural complexity in the upstream kinases limits the treatment progress. Thus, MEK inhibition is a promising strategy since it is easy to inhibit and is a gatekeeper for the many malignant effects of its downstream effector. Even though MEK inhibitors are under investigation in many cancers, drug resistance continues to be the principal limiting factor to achieving cures in patients with cancer. Hence, we accomplished a high-throughput virtual screening to overcome this bottleneck by the discovery of dual-targeting therapy in cancer treatment. Here, a total of 11,808 DrugBank molecules were assessed through high-throughput virtual screening for their activity against MEK. Further, the Glide docking, MLSF and prime-MM/GBSA methods were implemented to extract the potential lead compounds from the database. Two compounds, DB012661 and DB07642, were outperformed in all the screening analyses. Further, the study results reveal that the lead compounds also have a significant binding capability with the co-target PIM1. Finally, the SIE-based free energy calculation reveals that the binding of compounds was majorly affected by the van der Waals interactions with MEK receptor. Overall, the in silico binding efficacy of these lead compounds against both MEK and PIM1 could be of significant therapeutic interest to overcome drug resistance in the near future.

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