Structural bioinformatics used to predict the protein targets of remdesivir and flavones in SARS-CoV-2 infection

2021 ◽  
Vol 17 ◽  
Author(s):  
Avram Speranta ◽  
Laura Manoliu ◽  
Catalina Sogor ◽  
Maria Mernea ◽  
Corina Duda Seiman ◽  
...  

Background: During the current SARS-CoV-2 pandemic, the identification of effective antiviral drugs is crucial. Unfortunately, no specific treatment or vaccine is available to date. Objective: Here, we aimed to predict the interactions between SARS-CoV-2 proteins and protein targets from the human body for some flavone molecules (kaempferol, morin, pectolinarin, myricitrin, and herbacetin) in comparison to synthetic compounds (hydroxychloroquine, remdesivir, ribavirin, ritonavir, AMD-070, favipiravir). Methods: Using MOE software and advanced bioinformatics and cheminformatics portals, we conducted an extensive analysis based on various structural and functional features of compounds, such as their amphiphilic field, flexibility, and steric features. The structural similarity analysis of natural and synthetic compounds was performed using Tanimoto coefficients. The interactions of some compounds with SARS-CoV-2 3CLprotease or RNA-dependent RNA polymerase were described using 2D protein-ligand interaction diagrams based on known crystal structures. The potential targets of considered compounds were identified using the SwissTargetPrediction web tool. Results: Our results showed that remdesivir, pectolinarin, and ritonavir present a strong structural similarity which may be correlated to their similar biological activity. As common molecular targets of compounds in the human body, ritonavir, kaempferol, morin, and herbacetin can activate multidrug resistance-associated proteins, while remdesivir, ribavirin, and pectolinarin appear as ligands for adenosine receptors. Conclusion: Our evaluation recommends remdesivir, pectolinarin, and ritonavir as promising anti-SARS-CoV-2 agents.

Author(s):  
Xiaodong Pang ◽  
Linxiang Zhou ◽  
Lily Zhang ◽  
Lina Xu ◽  
Xinyi Zhang

Author(s):  
Lennart Gundelach ◽  
Christofer S Tautermann ◽  
Thomas Fox ◽  
Chris-Kriton Skylaris

The accurate prediction of protein-ligand binding free energies with tractable computational methods has the potential to revolutionize drug discovery. Modeling the protein-ligand interaction at a quantum mechanical level, instead of...


RSC Advances ◽  
2019 ◽  
Vol 9 (14) ◽  
pp. 7757-7766 ◽  
Author(s):  
Yao Wu ◽  
Xin-Ying Gao ◽  
Xin-Hui Chen ◽  
Shao-Long Zhang ◽  
Wen-Juan Wang ◽  
...  

Our study gains insight into the development of novel specific ABCG2 inhibitors, and develops a comprehensive computational strategy to understand protein ligand interaction with the help of AlphaSpace, a fragment-centric topographic mapping tool.


2019 ◽  
Vol 122 ◽  
pp. 289-297 ◽  
Author(s):  
Thaís Meira Menezes ◽  
Sinara Mônica Vitalino de Almeida ◽  
Ricardo Olímpio de Moura ◽  
Gustavo Seabra ◽  
Maria do Carmo Alves de Lima ◽  
...  

RSC Advances ◽  
2015 ◽  
Vol 5 (84) ◽  
pp. 68829-68838 ◽  
Author(s):  
Hossein Farrokhpour ◽  
Vahid Pakatchian ◽  
Abdolreza Hajipour ◽  
Fatemeh Abyar ◽  
Alireza Najafi Chermahini ◽  
...  

A part of signal transducer smoothened (SMO) protein including antitumor agent LY2940680. The site of this antitumor was considered for the docking of 716 ligands.


2017 ◽  
Vol 73 (3) ◽  
pp. 279-285
Author(s):  
Charlotte M. Deane ◽  
Ian D. Wall ◽  
Darren V. S. Green ◽  
Brian D. Marsden ◽  
Anthony R. Bradley

In this work, two freely available web-based interactive computational tools that facilitate the analysis and interpretation of protein–ligand interaction data are described. Firstly,WONKA, which assists in uncovering interesting and unusual features (for example residue motions) within ensembles of protein–ligand structures and enables the facile sharing of observations between scientists. Secondly,OOMMPPAA, which incorporates protein–ligand activity data with protein–ligand structural data using three-dimensional matched molecular pairs.OOMMPPAAhighlights nuanced structure–activity relationships (SAR) and summarizes available protein–ligand activity data in the protein context. In this paper, the background that led to the development of both tools is described. Their implementation is outlined and their utility using in-house Structural Genomics Consortium (SGC) data sets and openly available data from the PDB and ChEMBL is described. Both tools are freely available to use and download at http://wonka.sgc.ox.ac.uk/WONKA/ and http://oommppaa.sgc.ox.ac.uk/OOMMPPAA/.


2020 ◽  
Author(s):  
Ben Geoffrey A S ◽  
Pavan Preetham Valluri ◽  
Akhil Sanker ◽  
Rafal Madaj ◽  
Host Antony Davidd ◽  
...  

<p>Network data is composed of nodes and edges. Successful application of machine learning/deep learning algorithms on network data to make node classification and link prediction has been shown in the area of social networks through which highly customized suggestions are offered to social network users. Similarly one can attempt the use of machine learning/deep learning algorithms on biological network data to generate predictions of scientific usefulness. In the present work, compound-drug target interaction data set from bindingDB has been used to train machine learning/deep learning algorithms which are used to predict the drug targets for any PubChem compound queried by the user. The user is required to input the PubChem Compound ID (CID) of the compound the user wishes to gain information about its predicted biological activity and the tool outputs the RCSB PDB IDs of the predicted drug target. The tool also incorporates a feature to perform automated <i>In Silico</i> modelling for the compounds and the predicted drug targets to uncover their protein-ligand interaction profiles. The programs fetches the structures of the compound and the predicted drug targets, prepares them for molecular docking using standard AutoDock Scripts that are part of MGLtools and performs molecular docking, protein-ligand interaction profiling of the targets and the compound and stores the visualized results in the working folder of the user. The program is hosted, supported and maintained at the following GitHub repository </p> <p><a href="https://github.com/bengeof/Compound2Drug">https://github.com/bengeof/Compound2Drug</a></p>


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