scholarly journals In silico modeling of biochemical pathways

2021 ◽  
Vol 4 (s1) ◽  
Author(s):  
Paolo Milazzo ◽  
Roberta Gori ◽  
Alessio Micheli ◽  
Lucia Nasti ◽  
Marco Podda
Keyword(s):  
Kinetic Parameters ◽  
In Silico ◽  
In Silico Analysis ◽  
Reaction Rates ◽  
Reaction Networks ◽  
Interaction Patterns ◽  
Biochemical Pathways ◽  
Stochastic Simulation Algorithms ◽  
Dynamical Properties ◽  
In Silico Models

We present in silico modeling methods for the investigation of dynamical properties of biochemical pathways, that are chemical reaction networks underlying cell functioning. Since pathways are (complex) dynamical systems, in-silico models are often studied by applying numerical integration techniques for Ordinary Differential Equations (ODEs), or stochastic simulation algorithms. However, these techniques require a rather accurate knowledge of the kinetic parameters of the modeled chemical reactions. Moreover, in the case of very complex reaction networks, in silico analysis can become unfeasible from the computational viewpoint. Consequently, in the last few years several approaches have been proposed that focus on estimating or predicting dynamical properties from the analysis of the structure of the biochemical pathway. This means that the analysis focuses more on the interaction patterns than on the kinetic parameters, and this usually makes it possible to deduce the role of each molecule and how each molecule qualitatively influences each other, by abstracting away from quantitative details about concentrations and reaction rates.

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 Study of 1, 4 Alpha Glucan Branching Enzyme and Substrate Docking Studies

2020 ◽  
Vol 17 (1) ◽  
pp. 40-50
Author(s):  
Farzane Kargar ◽  
Amir Savardashtaki ◽  
Mojtaba Mortazavi ◽  
Masoud Torkzadeh Mahani ◽  
Ali Mohammad Amani ◽  
...  
Keyword(s):  
Phylogenetic Tree ◽  
In Silico ◽  
Alpha Helix ◽  
In Silico Analysis ◽  
Glycogen Storage ◽  
Docking Studies ◽  
Random Coil ◽  
Special Topic ◽  
Industrial Biotechnology ◽  
In Silico Study

Background: The 1,4-alpha-glucan branching protein (GlgB) plays an important role in the glycogen biosynthesis and the deficiency in this enzyme has resulted in Glycogen storage disease and accumulation of an amylopectin-like polysaccharide. Consequently, this enzyme was considered a special topic in clinical and biotechnological research. One of the newly introduced GlgB belongs to the Neisseria sp. HMSC071A01 (Ref.Seq. WP_049335546). For in silico analysis, the 3D molecular modeling of this enzyme was conducted in the I-TASSER web server. Methods: For a better evaluation, the important characteristics of this enzyme such as functional properties, metabolic pathway and activity were investigated in the TargetP software. Additionally, the phylogenetic tree and secondary structure of this enzyme were studied by Mafft and Prabi software, respectively. Finally, the binding site properties (the maltoheptaose as substrate) were studied using the AutoDock Vina. Results: By drawing the phylogenetic tree, the closest species were the taxonomic group of Betaproteobacteria. The results showed that the structure of this enzyme had 34.45% of the alpha helix and 45.45% of the random coil. Our analysis predicted that this enzyme has a potential signal peptide in the protein sequence. Conclusion: By these analyses, a new understanding was developed related to the sequence and structure of this enzyme. Our findings can further be used in some fields of clinical and industrial biotechnology.

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 Silico Study of Potential Cross-Kingdom Plant MicroRNA Based Regulation in Chronic Myeloid Leukemia

2020 ◽  
Vol 17 (2) ◽  
pp. 125-132
Author(s):  
Marjanu Hikmah Elias ◽  
Noraziah Nordin ◽  
Nazefah Abdul Hamid
Keyword(s):  
Targeted Therapy ◽  
In Silico ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Target Prediction ◽  
In Silico Analysis ◽  
Microrna Target ◽  
Good Target

Background: Chronic Myeloid Leukaemia (CML) is associated with the BCRABL1 gene, which plays a central role in the pathogenesis of CML. Thus, it is crucial to suppress the expression of BCR-ABL1 in the treatment of CML. MicroRNA is known to be a gene expression regulator and is thus a good candidate for molecularly targeted therapy for CML. Objective: This study aims to identify the microRNAs from edible plants targeting the 3’ Untranslated Region (3’UTR) of BCR-ABL1. Methods: In this in silico analysis, the sequence of 3’UTR of BCR-ABL1 was obtained from Ensembl Genome Browser. PsRNATarget Analysis Server and MicroRNA Target Prediction (miRTar) Server were used to identify miRNAs that have binding conformity with 3’UTR of BCR-ABL1. The MiRBase database was used to validate the species of plants expressing the miRNAs. The RNAfold web server and RNA COMPOSER were used for secondary and tertiary structure prediction, respectively. Results: In silico analyses revealed that cpa-miR8154, csi-miR3952, gma-miR4414-5p, mdm-miR482c, osa-miR1858a and osa-miR1858b show binding conformity with strong molecular interaction towards 3’UTR region of BCR-ABL1. However, only cpa-miR- 8154, osa-miR-1858a and osa-miR-1858b showed good target site accessibility. Conclusion: It is predicted that these microRNAs post-transcriptionally inhibit the BCRABL1 gene and thus could be a potential molecular targeted therapy for CML. However, further studies involving in vitro, in vivo and functional analyses need to be carried out to determine the ability of these miRNAs to form the basis for targeted therapy for CML.

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.

Sign in / Sign up

Export Citation Format

Share Document