Protein Phosphatase PP2C Identification in Entamoeba spp

Entamoeba histolytica is the causative agent of amoebiasis, and Entamoeba dispar is its noninvasive morphological twin. Entamoeba invadens is a reptilian parasite. In the present study, Western blot, phosphatase activity, immunofluorescence, and bioinformatic analyses were used to identify PP2C phosphatases of E. histolytica, E. dispar, and E. invadens. PP2C was identified in trophozoites of all Entamoeba species and cysts of E. invadens. Immunoblotting using a Leishmania mexicana anti-PP2C antibody recognized a 45.2 kDa PP2C in all species. In E. histolytica and E. invadens, a high molecular weight element PP2C at 75 kDa was recognized, mainly in cysts of E. invadens. Immunofluorescence demonstrated the presence of PP2C in membrane and vesicular structures in the cytosol of all species analyzed. The ~75 kDa PP2C of Entamoeba spp. shows the conserved domain characteristic of phosphatase enzymes (according to in silico analysis). Possible PP2C participation in the encystation process was discussed.

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Working with Proteins in silico: A Review of Online Available Tools for Basic Identification of Proteins

Turkish Journal of Agriculture - Food Science and Technology ◽

10.24925/turjaf.v5i1.65-70.926 ◽

2017 ◽

Vol 5 (1) ◽

pp. 65

Author(s):

Caner Yavuz ◽

Zahide Neslihan Öztürk

Keyword(s):

Molecular Weight ◽

Amino Acid ◽

In Silico ◽

In Silico Analysis ◽

Tips And Tricks ◽

Post Translational Modifications ◽

Starting Point ◽

Protein Research ◽

Experimental Approaches ◽

Silico Analysis

Increase in online available bioinformatics tools for protein research creates an important opportunity for scientists to reveal characteristics of the protein of interest by only starting from the predicted or known amino acid sequence without fully depending on experimental approaches. There are many sophisticated tools used for diverse purposes; however, there are not enough reviews covering the tips and tricks in selecting and using the correct tools as the literature mainly state the promotion of the new ones. In this review, with the aim of providing young scientists with no specific experience on protein work a reliable starting point for in silico analysis of the protein of interest, we summarized tools for annotation, identification of motifs and domains, determination isoelectric point, molecular weight, subcellular localization, and post-translational modifications by focusing on the important points to be considered while selecting from online available tools.

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miR-22 Modulates Lenalidomide Activity by Counteracting MYC Addiction in Multiple Myeloma

Cancers ◽

10.3390/cancers13174365 ◽

2021 ◽

Vol 13 (17) ◽

pp. 4365

Author(s):

Daniele Caracciolo ◽

Caterina Riillo ◽

Giada Juli ◽

Francesca Scionti ◽

Katia Todoerti ◽

...

Keyword(s):

Multiple Myeloma ◽

Western Blot ◽

Cell Viability ◽

Chromatin Immunoprecipitation ◽

In Silico ◽

Synthetic Lethality ◽

Inverse Correlation ◽

In Silico Analysis ◽

Predictive Biomarker ◽

Silico Analysis

Background: MYC is a master regulator of multiple myeloma (MM) by orchestrating several pro-tumoral pathways, including reprograming of the miRNA transcriptome. MYC is also involved in the acquirement of resistance to anti-MM drugs, including immunomodulatory imide drugs (IMiDs). Methods: In silico analysis was performed on MM proprietary and on public MMRF-CoMMpass datasets. Western blot and chromatin immunoprecipitation (ChIP) experiments were performed to validate miR-22 repression induced by MYC. Cell viability and apoptosis assays were used to evaluate lenalidomide sensitization after miR-22 overexpression. Results: We found an inverse correlation between MYC and miR-22 expression, which is associated with poor outcome in IMiD-treated MM patients. Mechanistically, we showed that MYC represses transcription of miR-22, which, in turn, targets MYC, thus establishing a feed-forward loop. Interestingly, we found that IMiD lenalidomide increases miR-22 expression by reducing MYC repression and, most importantly, that the combination of lenalidomide with miR-22 mimics results in a synergistic direct and NK-mediated cytotoxic activity. Conclusions: Taken together, our findings indicate that: (1) low miR-22 expression could represent a potential predictive biomarker of poor lenalidomide response in MM patients; and (2) miR-22 reduces MYC oncogenic activity, thus triggering a novel synthetic lethality loop, which sensitizes MM cells to lenalidomide.

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In silico analysis of fluoride transport proteins from prokaryotic and eukaryotic organisms

International Journal of Advances in Scientific Research ◽

10.7439/ijasr.v3i2.3822 ◽

2017 ◽

Vol 3 (2) ◽

pp. 26

Author(s):

Ramchander Merugu ◽

Sireesha Radarapu ◽

Sandhya Rani Dasari ◽

Jyothi Mandala

Keyword(s):

Molecular Weight ◽

In Silico ◽

In Silico Analysis ◽

Transport Proteins ◽

Instability Index ◽

Molecular Weights ◽

Low Concentrations ◽

Aliphatic Index ◽

Eukaryotic Organisms ◽

Silico Analysis

In silico analysis of Fluoride transporter proteins obtained from database are presented in this study. The composition of alanine and glycine was high while low concentrations of aspartic acid, cysteine and glutamine were seen when compared to other aminoacids. Molecular weight of fungal transporters was the highest while the bacterial showed relatively less molecular weights. The instability index of all the proteins was less than 40 showing that all of them are stable except that of Neurospora and Bifidobacterium. Aliphatic index was found to be within a range of 65 to 100.

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A systematic review with in silico analysis on transcriptomic profile of gallbladder carcinoma

Seminars in Oncology ◽

10.1053/j.seminoncol.2020.02.012 ◽

2020 ◽

Vol 47 (6) ◽

pp. 398-408

Author(s):

Sonam Tulsyan ◽

Showket Hussain ◽

Balraj Mittal ◽

Sundeep Singh Saluja ◽

Pranay Tanwar ◽

...

Keyword(s):

Systematic Review ◽

Gallbladder Carcinoma ◽

In Silico ◽

In Silico Analysis ◽

Transcriptomic Profile ◽

Silico Analysis

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In Silico Analysis of Single Nucleotide Polymorphism in Human Prothrombin Gene

10.1055/s-0039-1680245 ◽

2019 ◽

Author(s):

I. Farah ◽

A. El-Mubark ◽

M. Osman ◽

A. Soliman ◽

F. Ali ◽

...

Keyword(s):

Single Nucleotide Polymorphism ◽

In Silico ◽

In Silico Analysis ◽

Nucleotide Polymorphism ◽

Single Nucleotide ◽

Prothrombin Gene ◽

Silico Analysis

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Enalos Suite of Tools: Enhancing Cheminformatics and Nanoinfor - matics through KNIME

Current Medicinal Chemistry ◽

10.2174/0929867327666200727114410 ◽

2020 ◽

Vol 27 (38) ◽

pp. 6523-6535 ◽

Cited By ~ 3

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.

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