In Silico Analysis the Complementarity of Tomato MicroRNA/MicroRNA* Sequences with Cucumber Mosaic Virus (CMV) Genomic RNAs

2013 ◽  
Vol 13 (6) ◽  
pp. 4421-4426 ◽  
Author(s):  
Junli Feng ◽  
Jishuang Chen
Keyword(s):  
Cucumber Mosaic Virus ◽  
Mosaic Virus ◽  
In Silico ◽  
In Silico Analysis ◽  
Genomic Rnas ◽  
Tomato Microrna ◽  
Silico Analysis

scholarly journals A Review on the Novel Coronavirus Disease based on In-silico Analysis of Various Drugs and Target Proteins

2020 ◽  
Vol 14 (suppl 1) ◽  
pp. 849-860
Author(s):  
Gauravi N. Trivedi ◽  
Janhavi T. Karlekar ◽  
Hiren A. Dhameliya ◽  
Hetalkumar Panchal
Keyword(s):  
In Silico ◽  
Close Contact ◽  
Genome Structure ◽  
In Silico Analysis ◽  
Bioactive Compound ◽  
Host Cells ◽  
World Health ◽  
Large Set ◽  
Genomic Rnas ◽  
Silico Analysis

Coronavirus Disease (COVID-19) is a new disease that emerged in Wuhan, China which spreads through close contact of people, often by small droplets produced during coughing or sneezing. Detail mechanism by which it spreads between people are under investigation. The World Health Organization (WHO) declared this disease as a pandemic after the severity of the disease increased. Many scientific reports gathered have suggested many drugs that could be potential candidates for the treatment. Although, clinical effectiveness has not been fully evaluated. In this review, we have aggregated the data from few research articles, official news websites and few review papers regarding its phylogenetic relation, genomic constitution, transmission, replication and in-silico analysis done by researchers for few potent drugs that are currently used to cure COVID-19. SARS-CoV-2 belongs to Betacoronavirus genus with Genome structure consists 14 Open Reading Frames (ORFs) that encode 27 proteins. Coronavirus replicates into the host cells having unique mechanisms like ribosome frame-shifting and synthesis of genomic and sub genomic RNAs. In-silico methods have the advantage that they can make fast predictions for a large set of compounds in a high-throughput mode and also make their prediction based on the structure of a compound even before it has been synthesized. In-silico softwares have been used to find or to improve a novel bioactive compound, which may exhibit a strong affinity to a particular target in the drug development process.

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

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

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

2020 ◽  
Vol 27 (38) ◽  
pp. 6523-6535 ◽  
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.

In-Silico Analysis of Chromone Containing Sulfonamide Derivatives as Human Carbonic Anhydrase Inhibitors

2013 ◽  
Vol 9 (4) ◽  
pp. 608-616 ◽  
Author(s):  
Zaheer Ul-Haq ◽  
Saman Usmani ◽  
Uzma Mahmood ◽  
Mariya al-Rashida ◽  
Ghulam Abbas
Keyword(s):  
Carbonic Anhydrase ◽  
In Silico ◽  
In Silico Analysis ◽  
Carbonic Anhydrase Inhibitors ◽  
Human Carbonic Anhydrase ◽  
Silico Analysis

In-Vitro and In-Silico Characterization of Xylose Reductase from Emericella nidulans

2019 ◽  
Vol 13 (2) ◽  
pp. 159-170 ◽  
Author(s):  
Vishal Ahuja ◽  
Aashima Sharma ◽  
Ranju Kumari Rathour ◽  
Vaishali Sharma ◽  
Nidhi Rana ◽  
...  
Keyword(s):  
Production Process ◽  
In Silico ◽  
Xylose Reductase ◽  
In Silico Analysis ◽  
Emericella Nidulans ◽  
Higher Temperature ◽  
In Silico Characterization ◽  
Silico Analysis

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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