Targeting CD44, ABCG2 and CD133 markers using aptamers: in silico analysis of CD133 extracellular domain 2 and its aptamer

RSC Advances ◽  
2016 ◽  
Vol 6 (38) ◽  
pp. 32115-32123 ◽  
Author(s):  
Nithya Subramanian ◽  
Balachandran Akilandeswari ◽  
Anjali Bhutra ◽  
Mohamed Alameen ◽  
Umashankar Vetrivel ◽  
...  

Truncated CSC marker aptamers penetrate tumor spheres and inhibits cell proliferation; a bioinformatics approach to decipher their structural interactions.

Bioimpacts ◽  
2021 ◽  
Author(s):  
Ysrafil Ysrafil ◽  
Indwiani Astuti

Introduction: MicroRNAs (miRNAs) are short-sequence RNAs that regulate gene expression by targeting messenger RNAs (mRNAs). Recent studies reveal that miRNA-324-5p plays an important role in worsening the ovarian cancer prognosis when the expression is very high. This study aimed to develop a miRNA targeted therapy by targeting the miRNA-324-5p function as a miRNA-324-5p inhibitor. Methods: Chitosan nanoparticles were used for antimiRNA-324-5p delivery into SKOV3 cell lines formulated by ionic gelation method. Antiproliferative effect of CS-NPs-antimiRNA was assessed by the MTT Assay. A mechanism study assessed the anticancer effect of the formula. In silico analysis used miRTar.Human and StarmiRDB combined with Genecard to predict the target genes of antimiR. Hawkdock web server was used to analyze protein-protein interactions that were further validated by quantitative polymerase chain reaction (qPCR). Results: The results of qPCR analysis showed endogenous miRNA-324-5p decreased after 24-hour transfection of antagonist miRNA. Furthermore, the MTT assay results showed that antimiRNA was able to inhibit SKOV3 cell proliferation (80 nM 68.13%, P<0.05). In silico analysis found miRNA-324-5p can regulate MEN1 and indirectly repress Gli1 mRNA. Validation results confirmed antimiR can decrease GLI1 mRNA expression. Conclusion: Our results showed antimiRNA-324-5p can act as a microRNA-based therapy to inhibit ovarian cancer proliferation by the reduction of GLI1 expression.


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

2019 ◽  
Vol 13 (2) ◽  
pp. 159-170 ◽  
Author(s):  
Vishal Ahuja ◽  
Aashima Sharma ◽  
Ranju Kumari Rathour ◽  
Vaishali Sharma ◽  
Nidhi Rana ◽  
...  

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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