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      In Silico Analysis of Inhibiting Papain-like Protease from SARS-CoV-2 by Using Plant-Derived Peptides

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          Abstract

          SARS-CoV-2 is a corona virus that has been the cause for one of the deadliest pandemics of history, started since 2019. Suppressing the activity of the critical enzymes in the SARS-CoV-2 could potentially inhibit a vital step in viral life cycle. Papain-like protease (PLpro) could be regarded as a critical enzyme in viral replication of SARS-CoV-2. In this research, it was aimed to suppress the activity of PLpro enzyme by using potential plant-derived protease inhibitor peptides. For this purpose, 11 plant derived peptides that could potentially inhibit protease activity were selected from literature. The structures of the PLpro and the peptide ligands were acquired from PDB (protein data bank) and after structural optimization, were docked by using HADDOCK 2.4 program. Analyzing the results indicated that VcTI from Veronica hederifolia provides effective molecular interactions at both liable Zn site and classic active site of PLpro, making it a potential inhibitory ligand for this enzyme that could be used for halting the replication of SARS-CoV-2. Molecular dynamic assay confirmed that the selected receptor and ligand complex was stable. Future in vitro and in vivo investigations are required to verify the efficiency of this compound as a potential therapeutic against SARS-CoV-2 infection.

          Supplementary Information

          The online version contains supplementary material available at 10.1007/s10989-021-10331-8.

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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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            GROMACS 4:  Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation.

            Berk Hess, Carsten Kutzner, David van der Spoel … (2008)
            Molecular simulation is an extremely useful, but computationally very expensive tool for studies of chemical and biomolecular systems. Here, we present a new implementation of our molecular simulation toolkit GROMACS which now both achieves extremely high performance on single processors from algorithmic optimizations and hand-coded routines and simultaneously scales very well on parallel machines. The code encompasses a minimal-communication domain decomposition algorithm, full dynamic load balancing, a state-of-the-art parallel constraint solver, and efficient virtual site algorithms that allow removal of hydrogen atom degrees of freedom to enable integration time steps up to 5 fs for atomistic simulations also in parallel. To improve the scaling properties of the common particle mesh Ewald electrostatics algorithms, we have in addition used a Multiple-Program, Multiple-Data approach, with separate node domains responsible for direct and reciprocal space interactions. Not only does this combination of algorithms enable extremely long simulations of large systems but also it provides that simulation performance on quite modest numbers of standard cluster nodes.
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              Is Open Access

              The HADDOCK2.2 Web Server: User-Friendly Integrative Modeling of Biomolecular Complexes.

              G van Zundert, J Rodrigues, M Trellet … (2016)
              The prediction of the quaternary structure of biomolecular macromolecules is of paramount importance for fundamental understanding of cellular processes and drug design. In the era of integrative structural biology, one way of increasing the accuracy of modeling methods used to predict the structure of biomolecular complexes is to include as much experimental or predictive information as possible in the process. This has been at the core of our information-driven docking approach HADDOCK. We present here the updated version 2.2 of the HADDOCK portal, which offers new features such as support for mixed molecule types, additional experimental restraints and improved protocols, all of this in a user-friendly interface. With well over 6000 registered users and 108,000 jobs served, an increasing fraction of which on grid resources, we hope that this timely upgrade will help the community to solve important biological questions and further advance the field. The HADDOCK2.2 Web server is freely accessible to non-profit users at http://haddock.science.uu.nl/services/HADDOCK2.2.
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                Author and article information

                Contributors
                Reza Mirnejad: rmirnejad@bmsu.ac.ir
                Journal
                Journal ID (nlm-ta): Int J Pept Res Ther
                Journal ID (iso-abbrev): Int J Pept Res Ther
                Title: International Journal of Peptide Research and Therapeutics
                Publisher: Springer Netherlands (Dordrecht )
                ISSN (Print): 1573-3149
                ISSN (Electronic): 1573-3904
                Publication date (Electronic): 9 December 2021
                Publication date (Print): 2022
                Volume: 28
                Issue: 1
                Electronic Location Identifier: 24
                Affiliations
                [1 ]GRID grid.411521.2, ISNI 0000 0000 9975 294X, Molecular Biology Research Center, , Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, ; Tehran, Iran
                [2 ]GRID grid.411750.6, ISNI 0000 0001 0454 365X, Department of Biotechnology, Faculty of Biological Science and Technology, , University of Isfahan, ; Isfahan, Iran
                [3 ]GRID grid.411521.2, ISNI 0000 0000 9975 294X, Applied Biotechnology Research Center, , Baqiyatallah University of Medical Sciences, ; Tehran, Iran
                Article
                Publisher ID: 10331
                DOI: 10.1007/s10989-021-10331-8
                PMC ID: 8655715
                SO-VID: 6ff9c511-4aa8-44de-8203-582c8e7a7696
                Copyright © © The Author(s), under exclusive licence to Springer Nature B.V. 2021
                License:

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                Date accepted : 21 October 2021
                Categories
                Subject: Article
                Custom metadata
                issue-copyright-statement © Springer Nature B.V. 2022

                ScienceOpen disciplines: Biochemistry
                Keywords: plant-derived peptides,in silico,sars-cov-2,papain-like protease
                Data availability:
                ScienceOpen disciplines: Biochemistry
                Keywords: plant-derived peptides, in silico, sars-cov-2, papain-like protease

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