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      A multi-targeted approach to identify potential flavonoids against three targets in the SARS-CoV-2 life cycle

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          Abstract

          The advent and persistence of the Severe Acute Respiratory Syndrome Coronavirus – 2 (SARS-CoV-2)-induced Coronavirus Disease (COVID-19) pandemic since December 2019 has created the largest public health emergency in over a century. Despite the administration of multiple vaccines across the globe, there continues to be a lack of approved efficacious non-prophylactic interventions for the disease. Flavonoids are a class of phytochemicals with historically established antiviral, anti-inflammatory and antioxidative properties that are effective against cancers, type 2 diabetes mellitus, and even other human coronaviruses. To identify the most promising bioactive flavonoids against the SARS-CoV-2, this article screened a virtual library of 46 bioactive flavonoids against three promising targets in the SARS-CoV-2 life cycle: human TMPRSS2 protein, 3CLpro, and PLpro. By examining the effects of glycosylation and other structural-activity relationships, the presence of sugar moiety in flavonoids significantly reduces its binding energy. It increases the solubility of flavonoids leading to reduced toxicity and higher bioavailability. Through protein-ligand contact profiling, it was concluded that naringin formed more hydrogen bonds with TMPRSS2 and 3CLpro.

          In contrast, hesperidin formed a more significant number of hydrogen bonds with PLpro. These observations were complimented by the 100 ns molecular dynamics simulation and binding free energy analysis, which showed a considerable stability of docked bioflavonoids in the active site of SARS-CoV-2 target proteins. Finally, the binding affinity and stability of the selected docked complexes were compared with the reference ligands (camostat for TMPRSS2, GC376 for 3CLpro, and GRL0617 for PLpro) that strongly inhibit their respective SARS-COV-2 targets. Overall analysis revealed that the selected flavonoids could be potential therapeutic agents against SARS-CoV-2. Naringin showed better affinity and stability for TMPRSS2 and 3CLpro, whereas hesperidin showed a better binding relationship and stability for PLpro.

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          UCSF Chimera--a visualization system for exploratory research and analysis.

          Eric F. Pettersen, Thomas Goddard, Conrad Huang … (2004)
          The design, implementation, and capabilities of an extensible visualization system, UCSF Chimera, are discussed. Chimera is segmented into a core that provides basic services and visualization, and extensions that provide most higher level functionality. This architecture ensures that the extension mechanism satisfies the demands of outside developers who wish to incorporate new features. Two unusual extensions are presented: Multiscale, which adds the ability to visualize large-scale molecular assemblies such as viral coats, and Collaboratory, which allows researchers to share a Chimera session interactively despite being at separate locales. Other extensions include Multalign Viewer, for showing multiple sequence alignments and associated structures; ViewDock, for screening docked ligand orientations; Movie, for replaying molecular dynamics trajectories; and Volume Viewer, for display and analysis of volumetric data. A discussion of the usage of Chimera in real-world situations is given, along with anticipated future directions. Chimera includes full user documentation, is free to academic and nonprofit users, and is available for Microsoft Windows, Linux, Apple Mac OS X, SGI IRIX, and HP Tru64 Unix from http://www.cgl.ucsf.edu/chimera/. Copyright 2004 Wiley Periodicals, Inc.
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            An interactive web-based dashboard to track COVID-19 in real time

            Ensheng Dong, Hongru Du, Lauren Gardner (2020)
            In December, 2019, a local outbreak of pneumonia of initially unknown cause was detected in Wuhan (Hubei, China), and was quickly determined to be caused by a novel coronavirus, 1 namely severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The outbreak has since spread to every province of mainland China as well as 27 other countries and regions, with more than 70 000 confirmed cases as of Feb 17, 2020. 2 In response to this ongoing public health emergency, we developed an online interactive dashboard, hosted by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, Baltimore, MD, USA, to visualise and track reported cases of coronavirus disease 2019 (COVID-19) in real time. The dashboard, first shared publicly on Jan 22, illustrates the location and number of confirmed COVID-19 cases, deaths, and recoveries for all affected countries. It was developed to provide researchers, public health authorities, and the general public with a user-friendly tool to track the outbreak as it unfolds. All data collected and displayed are made freely available, initially through Google Sheets and now through a GitHub repository, along with the feature layers of the dashboard, which are now included in the Esri Living Atlas. The dashboard reports cases at the province level in China; at the city level in the USA, Australia, and Canada; and at the country level otherwise. During Jan 22–31, all data collection and processing were done manually, and updates were typically done twice a day, morning and night (US Eastern Time). As the outbreak evolved, the manual reporting process became unsustainable; therefore, on Feb 1, we adopted a semi-automated living data stream strategy. Our primary data source is DXY, an online platform run by members of the Chinese medical community, which aggregates local media and government reports to provide cumulative totals of COVID-19 cases in near real time at the province level in China and at the country level otherwise. Every 15 min, the cumulative case counts are updated from DXY for all provinces in China and for other affected countries and regions. For countries and regions outside mainland China (including Hong Kong, Macau, and Taiwan), we found DXY cumulative case counts to frequently lag behind other sources; we therefore manually update these case numbers throughout the day when new cases are identified. To identify new cases, we monitor various Twitter feeds, online news services, and direct communication sent through the dashboard. Before manually updating the dashboard, we confirm the case numbers with regional and local health departments, including the respective centres for disease control and prevention (CDC) of China, Taiwan, and Europe, the Hong Kong Department of Health, the Macau Government, and WHO, as well as city-level and state-level health authorities. For city-level case reports in the USA, Australia, and Canada, which we began reporting on Feb 1, we rely on the US CDC, the government of Canada, the Australian Government Department of Health, and various state or territory health authorities. All manual updates (for countries and regions outside mainland China) are coordinated by a team at Johns Hopkins University. The case data reported on the dashboard aligns with the daily Chinese CDC 3 and WHO situation reports 2 for within and outside of mainland China, respectively (figure ). Furthermore, the dashboard is particularly effective at capturing the timing of the first reported case of COVID-19 in new countries or regions (appendix). With the exception of Australia, Hong Kong, and Italy, the CSSE at Johns Hopkins University has reported newly infected countries ahead of WHO, with Hong Kong and Italy reported within hours of the corresponding WHO situation report. Figure Comparison of COVID-19 case reporting from different sources Daily cumulative case numbers (starting Jan 22, 2020) reported by the Johns Hopkins University Center for Systems Science and Engineering (CSSE), WHO situation reports, and the Chinese Center for Disease Control and Prevention (Chinese CDC) for within (A) and outside (B) mainland China. Given the popularity and impact of the dashboard to date, we plan to continue hosting and managing the tool throughout the entirety of the COVID-19 outbreak and to build out its capabilities to establish a standing tool to monitor and report on future outbreaks. We believe our efforts are crucial to help inform modelling efforts and control measures during the earliest stages of the outbreak.
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              SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules

              Antoine Daina, Olivier Michielin, Vincent Zoete (2017)
              To be effective as a drug, a potent molecule must reach its target in the body in sufficient concentration, and stay there in a bioactive form long enough for the expected biologic events to occur. Drug development involves assessment of absorption, distribution, metabolism and excretion (ADME) increasingly earlier in the discovery process, at a stage when considered compounds are numerous but access to the physical samples is limited. In that context, computer models constitute valid alternatives to experiments. Here, we present the new SwissADME web tool that gives free access to a pool of fast yet robust predictive models for physicochemical properties, pharmacokinetics, drug-likeness and medicinal chemistry friendliness, among which in-house proficient methods such as the BOILED-Egg, iLOGP and Bioavailability Radar. Easy efficient input and interpretation are ensured thanks to a user-friendly interface through the login-free website http://www.swissadme.ch. Specialists, but also nonexpert in cheminformatics or computational chemistry can predict rapidly key parameters for a collection of molecules to support their drug discovery endeavours.
              Article 0 comments Cited 2624 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): Comput Biol Med
                Journal ID (iso-abbrev): Comput Biol Med
                Title: Computers in Biology and Medicine
                Publisher: The Author(s). Published by Elsevier Ltd.
                ISSN (Print): 0010-4825
                ISSN (Electronic): 1879-0534
                Publication date PMC-release: 11 January 2022
                Publication date (Print): March 2022
                Publication date (Electronic): 11 January 2022
                Volume: 142
                Page: 105231
                Affiliations
                [a ]School of Biotechnology, Jawaharlal Nehru University, New Delhi, 110067, India
                [b ]Center for Bioinformatics, Computational and Systems Biology, Pathfinder Research and Training Foundation, Greater Noida, India
                [c ]Weill Cornell Medicine-Qatar, Education City, Qatar Foundation, Doha, 24144, Qatar
                [d ]Department of Biotechnology, School of Engineering & Technology, Sharda University, Greater Noida, Uttar Pradesh, 201310, India
                Author notes
                [∗ ]Corresponding author.
                [∗∗ ]Corresponding author.
                Article
                Publisher Item ID: S0010-4825(22)00023-3 Publisher ID: 105231
                DOI: 10.1016/j.compbiomed.2022.105231
                PMC ID: 8750703
                PubMed ID: 35032740
                SO-VID: 6ff44d85-8585-4794-8341-654f87e2fded
                Copyright © © 2022 The Author(s)
                License:

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

                History
                Date received : 12 November 2021
                Date revision received : 8 January 2022
                Date accepted : 8 January 2022
                Categories
                Subject: Article

                Keywords: natural products,covid-19,computational studies,covid-19 drug discovery,glycosylated flavonoids,hesperidin,naringin,in silico,sars-cov-2,tmprss2,3clpro,plpro
                Data availability:
                Keywords: natural products, covid-19, computational studies, covid-19 drug discovery, glycosylated flavonoids, hesperidin, naringin, in silico, sars-cov-2, tmprss2, 3clpro, plpro

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