scholarly journals In silico analysis of the binding of anthelmintics to Caenorhabditis elegans P -glycoprotein 1

Author(s):  
Marion A. David ◽  
Stéphane Orlowski ◽  
Roger K. Prichard ◽  
Shaima Hashem ◽  
François André ◽  
...  
PLoS ONE ◽  
2013 ◽  
Vol 8 (8) ◽  
pp. e74425 ◽  
Author(s):  
Vânia Vilas-Boas ◽  
Renata Silva ◽  
Andreia Palmeira ◽  
Emília Sousa ◽  
Luísa Maria Ferreira ◽  
...  

2015 ◽  
Vol 11 (44) ◽  
pp. 606 ◽  
Author(s):  
QaziMohd Sajid Jamal ◽  
Babar Ali ◽  
ShowkatR Mir ◽  
Saiba Shams ◽  
NaserA Al-Wabel ◽  
...  

2020 ◽  
Vol 11 (4) ◽  
pp. 11418-11430

Heat shock proteins (HSPs) such as HSP70A, HSP90 etc. (also known as Chaperons) play an important role in folding and unfolding of proteins, an assemblage of multiprotein complexes, transportation and sorting of proteins in subcellular compartments, cell cycle control, signaling pathways, protection against stress and programmed cell death. Studies have also linked heat shock proteins with a sudden rise in temperature, which can be related to anhydrobiosis in nematodes. Considering the significance of HSPs in nematodes and bacteria, the present study was designed for their in silico analysis in Caenorhabditis elegans and Photorhabdus temperata. The availability of a vast amount of sequence data generated through various bioinformatics tools, coupled with computational biology advancements, provides an ideal framework for silico gene expression and its analysis. A detailed in silico insight into these proteins include physicochemical properties, secondary structure prediction, homology modeling, and different models. The amino acid composition data were subjected to multivariate techniques, Pearson correlation, and phylogenetic analysis. In the present study, the authors characterized different HSPs according to different stability parameters and valid structures. A detailed in silico analysis of these proteins and prediction of their activity in different conditions can be very useful in both in vitro and in vivo experiments.


2020 ◽  
Vol 47 (6) ◽  
pp. 398-408
Author(s):  
Sonam Tulsyan ◽  
Showket Hussain ◽  
Balraj Mittal ◽  
Sundeep Singh Saluja ◽  
Pranay Tanwar ◽  
...  

2020 ◽  
Vol 27 (38) ◽  
pp. 6523-6535 ◽  
Author(s):  
Antreas Afantitis ◽  
Andreas Tsoumanis ◽  
Georgia Melagraki

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.


2013 ◽  
Vol 9 (4) ◽  
pp. 608-616 ◽  
Author(s):  
Zaheer Ul-Haq ◽  
Saman Usmani ◽  
Uzma Mahmood ◽  
Mariya al-Rashida ◽  
Ghulam Abbas

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