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      In silico modeling for quick prediction of inhibitory activity against 3CL pro enzyme in SARS CoV diseases

      research-article
      a , b , a , a
      Journal of Biomolecular Structure & Dynamics
      Taylor & Francis
      SARS CoV-2, Covid-19, coronavirus, in silico, QSAR

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          Abstract

          As of 2 September 2020, the 2019 novel coronavirus or SARS CoV-2 has been responsible for more than 2,56,02,665 infections and 8,52,768 deaths worldwide. There has been an urgent need of newer drug discovery to tackle the situation. Severe acute respiratory syndrome-associated coronavirus 3C-like protease (or 3CL pro) is a potential target as anti-SARS agents as it plays a vital role in the viral life cycle. This study aims at developing a quantitative structure–activity relationship (QSAR) model against a group of 3CL pro inhibitors to study their structural requirements for their inhibitory activity. Further, molecular docking studies were carried out which helped in the justification of the QSAR findings. Moreover, molecular dynamics simulation study was performed for selected compounds to check the stability of interactions as suggested by the docking analysis. The current QSAR model was further used in the prediction and screening of large databases within a short time.

          Communicated by Ramaswamy H. Sarma

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          Most cited references29

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          Receptor Recognition by the Novel Coronavirus from Wuhan: an Analysis Based on Decade-Long Structural Studies of SARS Coronavirus

          The recent emergence of Wuhan coronavirus (2019-nCoV) puts the world on alert. 2019-nCoV is reminiscent of the SARS-CoV outbreak in 2002 to 2003. Our decade-long structural studies on the receptor recognition by SARS-CoV have identified key interactions between SARS-CoV spike protein and its host receptor angiotensin-converting enzyme 2 (ACE2), which regulate both the cross-species and human-to-human transmissions of SARS-CoV. One of the goals of SARS-CoV research was to build an atomic-level iterative framework of virus-receptor interactions to facilitate epidemic surveillance, predict species-specific receptor usage, and identify potential animal hosts and animal models of viruses. Based on the sequence of 2019-nCoV spike protein, we apply this predictive framework to provide novel insights into the receptor usage and likely host range of 2019-nCoV. This study provides a robust test of this reiterative framework, providing the basic, translational, and public health research communities with predictive insights that may help study and battle this novel 2019-nCoV.
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            Periodic boundary conditions inab initiocalculations

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              Human Coronavirus: Host-Pathogen Interaction

              Human coronavirus (HCoV) infection causes respiratory diseases with mild to severe outcomes. In the last 15 years, we have witnessed the emergence of two zoonotic, highly pathogenic HCoVs: severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV). Replication of HCoV is regulated by a diversity of host factors and induces drastic alterations in cellular structure and physiology. Activation of critical signaling pathways during HCoV infection modulates the induction of antiviral immune response and contributes to the pathogenesis of HCoV. Recent studies have begun to reveal some fundamental aspects of the intricate HCoV-host interaction in mechanistic detail. In this review, we summarize the current knowledge of host factors co-opted and signaling pathways activated during HCoV infection, with an emphasis on HCoV-infection-induced stress response, autophagy, apoptosis, and innate immunity. The cross talk among these pathways, as well as the modulatory strategies utilized by HCoV, is also discussed.
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                Author and article information

                Journal
                J Biomol Struct Dyn
                J Biomol Struct Dyn
                Journal of Biomolecular Structure & Dynamics
                Taylor & Francis
                0739-1102
                1538-0254
                21 September 2020
                2020
                : 1-27
                Affiliations
                [a ]Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University , Kolkata, India
                [b ]Center for Informatics, Shiv Nadar University , Dadri, Uttar Pradesh, India
                Author notes
                [#]

                These authors contributed equally to this work.

                Supplemental data for this article can be accessed online at https://doi.org/10.1080/07391102.2020.1821779.

                CONTACT Kunal Roy kunalroy_in@ 123456yahoo.com ; kunal.roy@ 123456jadavpuruniversity.in Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University , Kolkata700 032, India
                Author information
                https://orcid.org/0000-0003-4486-8074
                Article
                1821779
                10.1080/07391102.2020.1821779
                7544940
                32954984
                d871f063-7461-48eb-9225-8e976e03b818
                © 2020 Informa UK Limited, trading as Taylor & Francis Group

                This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections.

                History
                Page count
                Figures: 16, Tables: 7, Pages: 27, Words: 8655
                Categories
                Research Article
                Research Article

                sars cov-2,covid-19,coronavirus,in silico,qsar
                sars cov-2, covid-19, coronavirus, in silico, qsar

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