scholarly journals In silico analysis of a potential antidiabetic phytochemical erythrin against therapeutic targets of diabetes

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Madhushree M. V. Rao ◽  
T. P. N. Hariprasad
2020 ◽  
Vol 10 (10) ◽  
pp. 666
Author(s):  
Debasmita Mukhopadhyay ◽  
Bashair M. Mussa

Background: Neuroinvasion of severe acute respiratory syndrome coronavirus (SARS-CoV) is well documented and, given the similarities between this virus and SARS-CoV-2, it seems that the neurological impairment that is associated with coronavirus disease 2019 (COVID-19) is due to SARS-CoV-2 neuroinvasion. Hypothalamic circuits are exposed to the entry of the virus via the olfactory bulb and interact centrally with crucial respiratory nuclei. Hypothalamic microRNAs are considered as potential biomarkers and modulators for various diseases and future therapeutic targets. The present study aims to investigate the microRNAs that regulate the expression of hypothalamic angiotensin-converting enzyme 2 (ACE2) and transmembrane serine protease 2 (TMPRSS2), essential elements for SARS-CoV-2 cell entry. Methods: To determine potential hypothalamic miRNAs that can directly bind to ACE2 and TMPRSS2, multiple target bioinformatics prediction algorithms were used, including miRBase, Target scan, and miRWalk2.029. Results: Our in silico analysis has revealed that, although there are over 5000 hypothalamic miRNAs, around 31 miRNAs and 29 miRNAs have shown binding sites and strong binding capacity against ACE2 and TMPRSS2, respectively. Conclusion: These novel potential hypothalamic miRNAs can be used to identify new therapeutic targets to treat neurological symptoms in COVID-19 patients via regulation of ACE2 and TMPRSS2 expression.


2016 ◽  
Vol 22 (15) ◽  
pp. 3903-3914 ◽  
Author(s):  
Allison R. Hanaford ◽  
Tenley C. Archer ◽  
Antoinette Price ◽  
Ulf D. Kahlert ◽  
Jarek Maciaczyk ◽  
...  

2016 ◽  
Vol 18 (suppl 3) ◽  
pp. iii120.3-iii120
Author(s):  
Allison Hanaford ◽  
Tenley Archer ◽  
Antoinette Price ◽  
Ulf Kahlert ◽  
Jarek Maciaczyk ◽  
...  

2021 ◽  
Vol 22 (23) ◽  
pp. 12765
Author(s):  
Nadine de Godoy Torso ◽  
João Kleber Novais Pereira ◽  
Marília Berlofa Visacri ◽  
Pedro Eduardo Nascimento Silva Vasconcelos ◽  
Pía Loren ◽  
...  

The purpose of this systematic review was to map out and summarize scientific evidence on dysregulated microRNAs (miRNAs) that can be possible biomarkers or therapeutic targets for cisplatin nephrotoxicity and have already been tested in humans, animals, or cells. In addition, an in silico analysis of the two miRNAs found to be dysregulated in the majority of studies was performed. A literature search was performed using eight databases for studies published up to 4 July 2021. Two independent reviewers selected the studies and extracted the data; disagreements were resolved by a third and fourth reviewers. A total of 1002 records were identified, of which 30 met the eligibility criteria. All studies were published in English and reported between 2010 and 2021. The main findings were as follows: (a) miR-34a and miR-21 were the main miRNAs identified by the studies as possible biomarkers and therapeutic targets of cisplatin nephrotoxicity; (b) the in silico analysis revealed 124 and 131 different strongly validated targets for miR-34a and miR-21, respectively; and (c) studies in humans remain scarce.


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.


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