scholarly journals In silico analysis of bacteriophage tail tubular proteins suggests a putative sugar binding site and a catalytic mechanism

2019 ◽  
Vol 92 ◽  
pp. 8-16 ◽  
Author(s):  
Wieslaw Swietnicki ◽  
Ewa Brzozowska
2020 ◽  
Vol 17 ◽  
Author(s):  
Mojtaba Mortazavi ◽  
Saman Hosseinkhani ◽  
Masoud Torkzadeh-Mahani ◽  
Safa Lotfi ◽  
Rahman Emamzadeh ◽  
...  

: Bioluminescence is the production and emission of light by the luciferase enzymes in a living organism. The luciferases were identified in different domains of life, but the Lampyridae luciferases are considered for biotechnological and clinical applications. Recently, the new Iranian luciferase gene from the Lampyroidea maculata has been cloned and characterized. In this study, in silico analysis of this enzyme as the codon usage bias parameters (CAI, CBI, ENC, and rare codons) were conducted. Furthermore, the 3D structure of this enzyme was modeled in the I-TASSER web server and the status of these rare codons in this model was studied using SPDBV and PyMOL software. In the following, the substrate-binding site was studied using the AutoDock Vina. By molecular modeling, some rare codons were identified that may have a critical role in the structure and function of this enzyme. The GC3% of the CDs was 17/304 and GC3 Skewness was 0.115. The molecular docking analysis recognizes some residues that yield closely related to the DLSA binding site. By these analyses, a new understanding of the sequence and structure of this enzyme was created, and our findings can be used in some fields of clinical and industrial biotechnology. This bioinformatics analysis plays an important role in the design of the new recombinant enzyme.


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

Sign in / Sign up

Export Citation Format

Share Document