569 Prognostic Impact of Splicing Variants in Pediatric Brain Tumors: in Silico Analysis From High Density Microarrays

2012 ◽  
Vol 48 ◽  
pp. 174
Author(s):  
O. Pérez-Gonzalez ◽  
M. Macias-Vega ◽  
R. Cardenas-Cardos ◽  
A. Marhx-Bracho ◽  
R. Rivera-Luna
2018 ◽  
Vol 166 ◽  
pp. 80-90 ◽  
Author(s):  
Nejat Mahdieh ◽  
Sepideh Mikaeeli ◽  
Reza Shervin Badv ◽  
Azadeh Gharehzadeh Shirazi ◽  
Majid Maleki ◽  
...  

2015 ◽  
Vol 308 (1) ◽  
pp. C51-C60 ◽  
Author(s):  
Peter K. Lauf ◽  
Tariq Alqahtani ◽  
Karin Flues ◽  
Jaroslaw Meller ◽  
Norma C. Adragna

In silico analysis predicts interaction between Na-K-ATPase (NKA) and Bcl-2 protein canonical BH3- and BH1-like motifs, consistent with NKA inhibition by the benzo-phenanthridine alkaloid chelerythrine, a BH3 mimetic, in fetal human lens epithelial cells (FHLCs) (Lauf PK, Heiny J, Meller J, Lepera MA, Koikov L, Alter GM, Brown TL, Adragna NC. Cell Physiol Biochem 31: 257–276, 2013). This report establishes proof of concept: coimmunoprecipitation and immunocolocalization showed unequivocal and direct physical interaction between NKA and Bcl-2 proteins. Specifically, NKA antibodies (ABs) coimmunoprecipitated BclXL (B-cell lymphoma extra large) and BAK (Bcl-2 antagonist killer) proteins in FHLCs and A549 lung cancer cells. In contrast, both anti-Bcl-2 ABs failed to pull down NKA. Notably, the molecular mass of BAK1 proteins pulled down by NKA and BclXL ABs appeared to be some 4-kDa larger than found in input monomers. In silico analysis predicts these higher molecular mass BAK1 proteins as alternative splicing variants, encoding 42 amino acid (aa) larger proteins than the known 211-aa long canonical BAK1 protein. These BAK1 variants may constitute a pool separate from that forming mitochondrial pores by specifically interacting with NKA and BclXL proteins. We propose a NKA-Bcl-2 protein ternary complex supporting our hypothesis for a special sensor role of NKA in Bcl-2 protein control of cell survival and apoptosis.


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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