ABSTRACT
Background: The multitargeted computational approach for the design of drugs to treat severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) lung infection from herbal sources may lead to compound(s) that is/are safe (derived from natural sources), effective (act on predefined targets) and broad spectrum (active in both, adult and juvenile population). Objective(s): The present work aims at developing a specific and effective treatment for a lung infection in both the adult and juvenile population, caused due to SARS-CoV-2 through a computational approach. Method(s): A systematic virtual screening of 27 phytoconstituents from 11 Indian herbs with antiviral, anti-inflammatory, and immunomodulatory activity was performed. After applying the Lipinski rule of five, 19 compounds that fitted well were subjected to molecular docking studies using Molegro virtual docker 6.0 with two targets viz. SARS-CoV-2 main protease (Mpro) (PDB ID 6LU7) and ACE receptor (PDB ID 6M0J). The best-docked complexes were used to develop a merged feature pharmacophore using Lig-andscout software, to know the structural requirements to develop multitarget inhibitor(s) of SARS-CoV-2. Drug likeliness and ADMET studies were also performed. Result(s): The results revealed that Syringin, a glycoside from Tinospora cordifolia, has a good binding affinity towards both targets as compared to Remdesivir. Furthermore, drug likeliness and ADMET studies established its better bioavailability and low toxicity. Conclusion(s): The pharmacophores developed from protein-ligand complexes provided an important understanding to design multitarget inhibitor(s) of SARS-CoV-2 to treat COVID-19 lung infection in both the adult and juvenile populations. Syringin may be subjected to further wet-lab studies to establish the results obtained through in-silico studies.Copyright © 2022 Bentham Science Publishers.
ABSTRACT
Background: Since December 2019, COVID-19 has become a new health crisis in the world and has been declared a public health emergency of international concern by WHO. In search of anti-COVID treatment regimen, we applied molecular docking approach in order to identify the natural compounds that may have potential for anti-COVID treatment with specific target and selective inhibitory mechanism. Our goal is to identify the potential anti-COVID compounds based on virtual screening of the protein of spike glycoprotein as virtual inhibition target. Method(s): Molecular docking was carried out by using Molergo Virtual Docker. 35 compounds from different plant sources were selected and docked in the enzyme pocket. Result(s): The docking result revealed that some of the compounds exhibited good potency against the virus and can be used further for developing new drug regimen. Conclusion(s): The compounds of natural origin could be a good target and can be used as lead compounds for the treatment of this dreadful disease. Copyright © 2023 Bentham Science Publishers.
ABSTRACT
Objective: The study aimed to obtain active compounds from Cymbopogon nardus as candidates for protease inhibitor of SARS-CoV-2 virus by assessing the ligand-binding affinity in the binding pocket of SARS-CoV-2 main protease protein. Methods: Molecular docking as a protease inhibitor of SARS-CoV-2 was carried using computational software Molegro Virtual Docker (MVD);computational docking was carried using receptors with Protein Data Bank (PDB) were also used to compare the affinity strength of the test compounds against the protease receptor (code of 5R81). The compounds of Cymbopogon nardus were optimized before docking using ChemDraw and minimized energy using Chem3D. Visualization of the docking result by using Discovery Studio and pkCSM was utilized to perform a pharmacokinetic and toxicological analysis (ADMET). Results: The result showed geranyl acetate, elemol, citronellal, and citronellyl acetate compounds from Cymbopogon nardus has a rerank score more negative than native ligand from 5R81 receptor as a protease inhibitor of SARS-CoV-2. Conclusion: Cymbopogon nardus can be developed as an antivirus with the mechanism of a protease inhibitor of SARS-CoV-2 candidates after further experimental tests.
ABSTRACT
Background: The ability to accurately predict Hb deferral would be useful in avoiding adverse effects of blood donation deferrals: anaemia caused to donors, extra costs to blood services, demotivated donors. To evaluate the performance of various prediction models in an unbiased manner, it would be beneficial to be able to run the same prediction models in exactly same way in different blood services, using their respective data. Aims: Our aim is to develop a software package for Hb deferral prediction that is easy to install and use. To further control the comparison of different models and datasets, we also define the format and preprocessing of the input data. The source code is released with a permissive licence, which enables adaptation and extension by the user. (Table Presented) Methods: The Docker platform (www.docker.com), which is available for all major operating systems, allows creating self-contained software container images without any external software dependencies. Our software is provided as a Docker container, which means it can be used anywhere where Docker is installed. Our prediction methods are based on standard R packages, with each method placed in a separate R notebook (Rmd). The models are fitted, unseen data is predicted and results are visualized by rendering these Rmd files as html and pdf reports, which contain text, tables and figures. The user interface of the software, accessible through any web browser, is based on the standard html, css, javascript and websocket technologies. In addition to the html and pdf reports, the output also includes the raw prediction results as csv files to allow arbitrary postprocessing, for example, pooling the results from different countries. Results: An extensible and easy to use software package was developed that is available both as a source code from GitHub (https:// github.com/FRCBS/Hb-predictorćontainer) and as a ready to use Docker container image from DockerHub (https://hub.docker.com/r/ toivoja/hb-predictor), that can be run anywhere after installing Docker. The container has already been applied in practice in a comparison study between four countries: Finland, the Netherlands, Belgium and South-Africa. Since the software is transferrable, no sharing of private data was needed since each participant country ran the container on their own data using their own Linux or Windows machines. During this international collaboration, the container was extensively tested, streamlined and one additional prediction model was included, making the total number of models four. Summary/Conclusions: The use of the Docker container allows unbiased comparison of performance of several prediction models between blood services, since the implementation, input format and preprocessing are fixed. However, as the container cannot anticipate all scenarios where it will be used, some additional preprocessing, such as selecting an appropriate time-window, may be needed. By subsetting data before analysis, a user can test numerous scenarios, for example: is there a difference in my donation data before and during the COVID19 pandemic;does change in the donation process cause a change in the prediction outcome;are predictors of haemoglobin different on women younger than 30 years in comparison to older women? To enforce data protection, the container can be run in a closed environment without any internet connection, and we have ensured that the produced html and pdf reports contain only summary level data.