scholarly journals In silico analysis of SARS-CoV-2 spike glycoprotein and insights into antibody binding

2020 ◽  
Vol 6 ◽  
Author(s):  
Victor Padilla-Sanchez
Keyword(s):  
In Silico ◽  
Large Scale ◽  
In Silico Analysis ◽  
Analysis Software ◽  
Spike Glycoprotein ◽  
Molecular Visualization ◽  
Macromolecular Assemblies ◽  
Large Scale Analysis ◽  
Silico Analysis ◽  
Ucsf Chimera

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in Wuhan, China in December 2019. Since then, COVID-19, the disease caused by SARS-CoV-2, has become a rapidly spreading pandemic that has reached most countries in the world. So far, there are no vaccines or therapeutics to fight this virus. Here, I present an in silico analysis of the virus spike glycoprotein (recently determined at atomic resolution) and provide insights into how antibodies against the 2002 virus SARS-CoV might be modified to neutralize SARS-CoV-2. I ran docking experiments with Rosetta Dock to determine which substitutions in the 80R and m396 antibodies might improve the binding of these to SARS-CoV-2 and used molecular visualization and analysis software, including UCSF Chimera and Rosetta Dock, as well as other bioinformatics tools, including SWISS-MODEL. Supercomputers, including Bridges Large, Stampede and Frontera, were used for macromolecular assemblies and large scale analysis and visualization.

scholarly journals In silico analysis suggests disruption of interactions between HAMP from hepatocytes and SLC40A1 from macrophages in hepatocellular carcinoma

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Liang Hu ◽  
Chao Wu
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
In Silico ◽  
Large Scale ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
In Silico Analysis ◽  
Hepatocyte Proliferation ◽  
Upregulated Genes ◽  
Silico Analysis

Abstract Background Identification of factors associated with proliferation in the hepatocellular carcinoma (HCC) microenvironment aids in understanding the mechanisms of disease progression and provides druggable targets. Gene expression profiles of individual cells in HCC and para-carcinoma tissues can be effectively obtained using the single-cell RNA sequencing (scRNA-Seq) technique. Here, we aimed to identify proliferative hepatocytes from HCC and para-carcinoma tissues, detect differentially expressed genes between the two types of proliferative hepatocytes, and investigate their potential roles in aberrant proliferation. Results Two respective gene signatures for proliferative cells and hepatocytes were established and used to identify proliferative hepatocytes from HCC and para-carcinoma tissues based on scRNA-Seq data. Gene expression profiles between the two types of proliferative hepatocytes were compared. Overall, 40 genes were upregulated in proliferative hepatocytes from para-carcinoma tissue, whereas no upregulated genes were detected in those from HCC tissue. Twelve of the genes, including HAMP, were specifically expressed in the liver tissue. Based on previous reports, we found that HAMP modulates cell proliferation through interaction with its receptor SLC40A1. Comprehensive analysis of cells in HCC and para-carcinoma tissues revealed that: (1) HAMP is specifically expressed in hepatocytes and significantly downregulated in malignant hepatocytes; (2) a subset of macrophages expressing SLC40A1 and genes reacting to various infections is present in para-carcinoma but not in HCC tissue. We independently validated the findings with scRNA-Seq and large-scale tissue bulk RNA-Seq/microarray analyses. Conclusion HAMP was significantly downregulated in malignant hepatocytes. In addition, a subset of macrophages expressing SLC40A1 and genes reacting to various infections was absent in HCC tissue. These findings support the involvement of HAMP-SLC40A1 signaling in aberrant hepatocyte proliferation in the HCC microenvironment. The collective data from our in silico analysis provide novel insights into the mechanisms underlying HCC progression and require further validation with wet laboratory experiments.

In silico drug repurposing for the identification of antimalarial drugs as candidate inhibitors of SARS-CoV-2

2021 ◽  
Vol 19 ◽  
Author(s):  
Praveen Kumar Pasla ◽  
Pugazhenthan Thangaraju ◽  
Sree Sudha TY ◽  
Sri Chandana M ◽  
Rizwaan Abbas S
Keyword(s):  
In Silico ◽  
Large Scale ◽  
Screening Method ◽  
Drug Repurposing ◽  
In Silico Analysis ◽  
Spike Protein ◽  
Respiratory Condition ◽  
3Cl Protease ◽  
Silico Analysis ◽  
Antimalarial Compounds

: Coronavirus disease (COVID-19) is a severe acute respiratory condition that affected millions of populations worldwide in early 2020, indicating for a global health emergency.As regards the deteriorating trends in COVID-19, none of the drugs were confirmed to have substantial efficacy in the potential treatment of COVID-19 patients in large-scale trials.The purpose of this research was to identify potential antimalarial candidate molecules for the treatment of COVID and to evaluate the possible mechanism of action by in silico screening method. Insilicoscreening study of various antimalarial compounds like Amodiaquine, Chloroquine, Hydroxychloroquine, Mefloquine, Primaquine, and Atovaquone were conducted with PyRx and AutoDoc 1.5.6 tools on ACE 2 receptor, 3CL protease, Hemagglutinin esterase, Spike protein SARS HR1 motif and Papain like protease virus proteins.Based on PyRx results, Mefloquine and Atovaquone have higher docking affinity scores against virus proteins compared to other antimalarial compounds. Screening report of Atovaquone exhibited affirmative inhibition constant on Spike protein SARS HR1 motif, 3CL and Papain like protease. In silico analysis reported Atovaquone as a promising candidate for COVID 19 therapy.

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
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