COVID19 - Computational Chemists Meet the Moment
2020; American Chemical Society; Volume: 60; Issue: 12 Linguagem: Inglês
10.1021/acs.jcim.0c01395
ISSN1549-960X
AutoresAdrian J. Mulholland, Rommie E. Amaro,
Tópico(s)Chemistry and Chemical Engineering
ResumoInfoMetricsFiguresRef. Journal of Chemical Information and ModelingVol 60/Issue 12Article This publication is free to access through this site. Learn More CiteCitationCitation and abstractCitation and referencesMore citation options ShareShare onFacebookX (Twitter)WeChatLinkedInRedditEmailJump toExpandCollapse EditorialDecember 28, 2020COVID19 - Computational Chemists Meet the MomentClick to copy article linkArticle link copied!Adrian J. MulhollandAdrian J. MulhollandSchool of Chemistry, Cantock's Close, Bristol BS8 1TS, United Kingdom of Great Britain and Northern IrelandMore by Adrian J. Mulhollandhttp://orcid.org/0000-0003-1015-4567Rommie E. Amaro*Rommie E. AmaroDepartment of Chemistry and Biochemistry, University of California San Diego, 3234 Urey Hall, no. 0340 9500 Gilman Drive, La Jolla, California 92093-0340, United States*Email: [email protected]More by Rommie E. Amarohttp://orcid.org/0000-0002-9275-9553Open PDFJournal of Chemical Information and ModelingCite this: J. Chem. Inf. Model. 2020, 60, 12, 5724–5726Click to copy citationCitation copied!https://pubs.acs.org/doi/10.1021/acs.jcim.0c01395https://doi.org/10.1021/acs.jcim.0c01395Published December 28, 2020 Publication History Published online 28 December 2020Published in issue 28 December 2020editorialCopyright © 2020 American Chemical Society. This publication is available under these Terms of Use. Request reuse permissionsThis publication is licensed for personal use by The American Chemical Society. ACS PublicationsCopyright © 2020 American Chemical SocietySubjectswhat are subjectsArticle subjects are automatically applied from the ACS Subject Taxonomy and describe the scientific concepts and themes of the article.Computational chemistryGeneticsMoleculesPeptides and proteinsTherapeuticsSPECIAL ISSUEThis article is part of the COVID19 - Computational Chemists Meet the Moment special issue.An extraordinary, unexpected set of circumstances brought scientists together to try to fight the COVID19 pandemic. As part of this response, since early 2020, numerous computational chemists and biophysicists have pivoted their efforts to try to gain insight into the inner workings and mechanisms of the molecules of the SARS-CoV-2 virus and host cell proteins involved in infection and disease, as well as to search for small molecules with which they may interact.Governments, research funders and agencies, computer centers, and companies recognized the potential of computational science and provided access to HPC and other computational resources for COVID19 applications. Initiatives sprang up quickly across the world to support applications including for biomolecular simulation (e.g., in the EU via the PRACE Fast Track Call for COVID19 related projects, in the UK, ARCHER time from UKRI/EPSRC via the HECBioSim and CCP-BioSim networks, through RIKEN in Japan, and using cloud resources donated by cloud providers such as AWS, Microsoft Azure, Google, and Oracle). The folding@home project used resources donated by over a million people worldwide to simulate all the viral proteins. (1) DE Shaw Research made available data from extensive simulations of viral protein targets. In the US, the COVID19 High Performance Computing Consortium made powerful computing resources available and inspired international partnerships with, e.g., Sweden and the UK. This provided more than 485 petaflops and technical and software development support. Researchers also recognized the need to link molecular simulations with experimental data and other types of modeling (e.g., refs (2−4)). Importantly, the collective effort involving scientists from many countries inspired the community to adopt a set of shared principles and commit to open science in this space. (5)This special COVID19 issue of the Journal of Chemical Information and Modeling contains contributions from groups around the world, representing a number of different techniques and perspectives on the use of computational chemistry methods, modeling, and informatics approaches for COVID19. We thank the many reviewers and journal advisory board members who committed to maintaining a high bar and being at times exceptionally expeditious in completing their reviews, in spite of the additional pressures and stress caused by the pandemic.Drug repurposing (repositioning) efforts early on jumped to the fore as potential routes to therapies: drugs already approved for other indications (i.e., already derisked) could provide the fastest route to approval and thus to therapeutic application. Oprea and colleagues present a comprehensive analysis of off-patent drug repositioning that discusses in depth the important elements involved and concludes with suggested priorities for such efforts. (6) Edwards weighs in on the prospects of finding a useful molecule from repurposing efforts, presenting a history lesson in such approaches, to provide perspective on the chances of success of repurposing, while recognizing the evident enthusiasm from the scientific community (e.g., over 3500 publications on the topic). (7) He notes that, even after two decades, there has not been a successful example of rationally discovering an off-purpose use: all known repurposed drugs have come from the collective experience and observations of perceptive clinicians. The clinical use of redemsivir, a nucleoside analog inhibitor of viral RNA-dependent RNA polymerases, for patients with COVID19 was based on a mechanistic hypothesis and has so far shown limited effectiveness. It remains to be seen if other repositioned drugs will emerge. Experience from the pandemic has also urged caution in the suggestion of potential drug candidates.Relatedly, Aloy and colleagues present an online resource to expand the set of compounds that may potentially be useful to treat COVID19, by identifying other molecules in public databases that have similar physiochemical properties to proposed COVID19 active molecules. (8) This adaptable resource can be used to broaden the space of compounds to be considered and is updated on a daily basis. Han, Li, Qiao, Liu, and colleagues present an online virtual screening tool that allows users to search and interact with in silico screening results by small molecule compound name or by SARS-CoV-2 protein. (9)Several articles look at various aspects of the SARS-CoV-2 main protease (Mpro). Garg and co-workers carry out a sequence- and structure-based analysis of Mpro, finding two mutations that affect the shape of the active site and binding features. (10) Additional screening suggests potentially useful chemical scaffolds and interactions within active site subpockets. Diaz and Suárez present a 2 μs all-atom molecular dynamics (MD) simulation of Mpro, in monomer and dimer oligomeric forms, both with and without the bound native substrate. (11) Their work indicates an unfavorable orientation of the scissile bond and catalytic triad in the monomeric form and contributes to understanding structure–function relationships in this important target. Ngo, Pham, Vu, and colleagues combine molecular docking, fast pulling of a ligand, and free energy perturbation methods to predict potential Mpro inhibiting compounds. (12) Rathi, Poonam, and co-workers perform screening studies of Mpro, together with MD simulations of top co-complexes to assess stability and interactions. (13)Mpro is also the target of a demonstration of the use of interactive simulation in virtual reality (VR) for modeling protein–ligand complexes. Glowacki, Mulholland, et al., show that this approach, involving on the fly manipulation of atomistic MD simulations, allows flexible docking of inhibitors and substrates to Mpro, giving structures in good agreement with experiments. (14) This interactive MD in VR approach provides an intuitive and accessible flexible docking method to predict the structures of protein ligand-complexes for Mpro and for other targets (data they make available to the community). Further, the pandemic has highlighted the need for new ways to collaborate and share models and data. This work also shows how researchers can work together in virtual reality using cloud-based interactive molecular simulations to study and predict how ligands bind to proteins.Two groups analyze viral mutations at a broader scale. Yin, Wei, and colleagues present a viral genotyping analysis for samples collected up until June 1, 2020, finding six subtypes spread globally. (15) Their work gives insight into the rate and persistence of mutations at a viral protein level and indicates that the envelope protein, Mpro, and endoribonuclease are relatively conserved, whereas the spike protein, nucleocapsid, and PLpro are more prone to mutation. This work will be useful for vaccine and therapeutic design and related efforts. Zhang et al. home in on the interface of the spike protein from both SARS-CoV and SARS-CoV-2 with the human ACE2 receptor, performing computational alanine scanning mutagenesis with relative free energy perturbation calculations. They present a comparative analysis of the interface hot spots that appear to drive the interaction of the spike with the host receptor. Notably, their studies were performed well in advance of the crystal structure of the SARS-CoV-2 spike-ACE2 complex; their agreement with the resulting experimental structure also gave an indication of the power of computational methods to get ahead of experiment.The final accepted manuscript in our issue is a tour de force effort by Smith and colleagues that showcases how state-of-the-art computational architectures can be used to carry out ensemble docking at scale. (16) Conventional and temperature replica exchange MD simulations of eight proteins in the SARS-CoV-2 proteome took advantage of the massively parallel architecture of the Summit supercomputer at Oak Ridge National Lab, where more than 1 ms of MD simulations were generated per day. The trajectories were clustered and subsequently used to screen the billion-compound containing Enamine Real database in parallel using AutoDock-GPU. In addition to presenting a number of insights about the dynamics of numerous targets in SARS-CoV-2, the work also points to an exciting future at the intersection of biophysical simulations with drug design and discovery and exascale computing, which sits just on the horizon.Altogether, the work presented in this special issue gives a flavor of the wide range of methods that have been brought to bear by computational molecular scientists across the world, in attempts to understand the SARS-CoV-2 virus and combat the disease that it causes. COVID19 has also shown the utility and power of sharing data, methods, and scientific knowledge sooner and more rapidly. Here, the computational chemistry community is to be applauded for their serious commitment to such principles and potentially this more open mode of scientific collaboration will be a legacy of this pandemic event. Reflecting on a difficult year, it is clear that fundamental computational chemical and molecular science has an essential role to play in tackling this and future pandemics, as well as other challenges, such as antimicrobial resistance and climate change, that humanity will inevitably face.Author InformationClick to copy section linkSection link copied!Corresponding AuthorRommie E. Amaro, Department of Chemistry and Biochemistry, University of California San Diego, 3234 Urey Hall, no. 0340 9500 Gilman Drive, La Jolla, California 92093-0340, United States, http://orcid.org/0000-0002-9275-9553, Email: [email protected]AuthorAdrian J. Mulholland, School of Chemistry, Cantock's Close, Bristol BS8 1TS, United Kingdom of Great Britain and Northern Ireland, http://orcid.org/0000-0003-1015-4567NotesViews expressed in this editorial are those of the authors and not necessarily the views of the ACS.ReferencesClick to copy section linkSection link copied! This article references 16 other publications. 1Zimmerman, M. I.; Porter, J. R.; Ward, M. D.; Singh, S.; Vithani, N.; Meller, A.; Mallimadugula, U. L.; Kuhn, C. E.; Borowsky, J. H.; Wiewiora, R. P. SARS-CoV-2 Simulations Go Exascale to Capture Spike Opening and Reveal Cryptic Pockets Across the Proteome. bioRxiv 2020, DOI: 10.1101/2020.06.27.175430 Google ScholarThere is no corresponding record for this reference.2Casalino, L.; Gaieb, Z.; Goldsmith, J. A.; Hjorth, C. K.; Dommer, A. C.; Harbison, A. M.; Fogarty, C. A.; Barros, E. P.; Taylor, B. C.; McLellan, J. S. Beyond Shielding: The Roles of Glycans in the SARS-CoV-2 Spike Protein. ACS Cent. Sci. 2020, 6 (10), 1722– 1734, DOI: 10.1021/acscentsci.0c01056 Google Scholar2Beyond Shielding: The Roles of Glycans in the SARS-CoV-2 Spike ProteinCasalino, Lorenzo; Gaieb, Zied; Goldsmith, Jory A.; Hjorth, Christy K.; Dommer, Abigail C.; Harbison, Aoife M.; Fogarty, Carl A.; Barros, Emilia P.; Taylor, Bryn C.; McLellan, Jason S.; Fadda, Elisa; Amaro, Rommie E.ACS Central Science (2020), 6 (10), 1722-1734CODEN: ACSCII; ISSN:2374-7951. (American Chemical Society) The ongoing COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in more than 28,000,000 infections and 900,000 deaths worldwide to date. Antibody development efforts mainly revolve around the extensively glycosylated SARS-CoV-2 spike (S) protein, which mediates host cell entry by binding to the angiotensin-converting enzyme 2 (ACE2). Similar to many other viral fusion proteins, the SARS-CoV-2 spike utilizes a glycan shield to thwart the host immune response. Here, we built a full-length model of the glycosylated SARS-CoV-2 S protein, both in the open and closed states, augmenting the available structural and biol. data. Multiple microsecond-long, all-atom mol. dynamics simulations were used to provide an atomistic perspective on the roles of glycans and on the protein structure and dynamics. We reveal an essential structural role of N-glycans at sites N165 and N234 in modulating the conformational dynamics of the spike's receptor binding domain (RBD), which is responsible for ACE2 recognition. This finding is corroborated by biolayer interferometry expts., which show that deletion of these glycans through N165A and N234A mutations significantly reduces binding to ACE2 as a result of the RBD conformational shift toward the "down" state. Addnl., end-to-end accessibility analyses outline a complete overview of the vulnerabilities of the glycan shield of the SARS-CoV-2 S protein, which may be exploited in the therapeutic efforts targeting this mol. machine. Overall, this work presents hitherto unseen functional and structural insights into the SARS-CoV-2 S protein and its glycan coat, providing a strategy to control the conformational plasticity of the RBD that could be harnessed for vaccine development. The glycan shield is a sugary barrier that helps the viral SARS-CoV-2 spikes to evade the immune system. Beyond shielding, two of the spike's glycans are discovered to prime the virus for infection. >> More from SciFinder ®https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXhvVOlsb3N&md5=52d499afcd7e3caa7d9e6017ffa86e453Turoňová, B.; Sikora, M.; Schürmann, C.; Hagen, W. J. H.; Welsch, S.; Blanc, F. E. C.; von Bülow, S.; Gecht, M.; Bagola, K.; Hörner, C. In Situ Structural Analysis of SARS-CoV-2 Spike Reveals Flexibility Mediated by Three Hinges. Science (Washington, DC, U. S.) 2020, 370 (6513), 203– 208, DOI: 10.1126/science.abd5223 Google Scholar3In situ structural analysis of SARS-CoV-2 spike reveals flexibility mediated by three hingesTuronova, Beata; Sikora, Mateusz; Schuermann, Christoph; Hagen, Wim J. H.; Welsch, Sonja; Blanc, Florian E. C.; von Buelow, Soeren; Gecht, Michael; Bagola, Katrin; Hoerner, Cindy; van Zandbergen, Ger; Landry, Jonathan; de Azevedo, Nayara Trevisan Doimo; Mosalaganti, Shyamal; Schwarz, Andre; Covino, Roberto; Muehlebach, Michael D.; Hummer, Gerhard; Krijnse Locker, Jacomine; Beck, MartinScience (Washington, DC, United States) (2020), 370 (6513), 203-208CODEN: SCIEAS; ISSN:1095-9203. (American Association for the Advancement of Science) The spike protein (S) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is required for cell entry and is the primary focus for vaccine development. In this study, we combined cryo-electron tomog., subtomogram averaging, and mol. dynamics simulations to structurally analyze S in situ. Compared with the recombinant S, the viral S was more heavily glycosylated and occurred mostly in the closed prefusion conformation. The stalk domain of S contains 3 hinges, giving the head unexpected orientational freedom. We propose that the hinges allow S to scan the host cell surface, shielded from antibodies by an extensive glycan coat. The structure of native S contributes to our understanding of SARS-CoV-2 infection and potentially to the development of safe vaccines. >> More from SciFinder ®https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXitVCrsbbO&md5=6464149155235d90f7a9d0972a50f4fb4Grant, O. C.; Montgomery, D.; Ito, K.; Woods, R. J. Analysis of the SARS-CoV-2 Spike Protein Glycan Shield Reveals Implications for Immune Recognition. Sci. Rep. 2020, 10 (1), 14991, DOI: 10.1038/s41598-020-71748-7 Google ScholarThere is no corresponding record for this reference. >> More from SciFinder ®https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=&md5=9874b665cc7a056b8e2f928dd31124405Amaro, R. E.; Mulholland, A. J. A Community Letter Regarding Sharing Bimolecular Simulation Data for COVID-19. J. Chem. Inf. Model. 2020, 60 (6), 2653, DOI: 10.1021/acs.jcim.0c00319 Google Scholar5A Community Letter Regarding Sharing Biomolecular Simulation Data for COVID-19Amaro, Rommie E.; Mulholland, Adrian J.Journal of Chemical Information and Modeling (2020), 60 (6), 2653-2656CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society) There is an urgent need to share our methods, models, and results openly and quickly to test findings, ensure reproducibility, test significance, eliminate dead-ends, and accelerate discovery. Sharing of data for COVID-19 applications will help connect scientists across the global biomol. simulation community, and also improve connection and communication between simulation and exptl. and clin. data and investigators. We, as a community, commit to the following principles and offer our support to others already working on open data efforts in the hope that others working on COVID-19 in biomol. simulation and other areas will adopt similar best practices. >> More from SciFinder ®https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXmsFKksrw%253D&md5=1a38fbcdcd674f2a4a5cf6ca90ad57c36Avram, S.; Curpan, R.; Halip, L.; Bora, A.; Oprea, T. I. Off-Patent Drug Repositioning. J. Chem. Inf. Model. 2020, DOI: 10.1021/acs.jcim.0c00826 Google ScholarThere is no corresponding record for this reference.7Edwards, A. What Are the Odds of Finding a COVID-19 Drug from a Lab Repurposing Screen?. J. Chem. Inf. Model. 2020, DOI: 10.1021/acs.jcim.0c00861 Google ScholarThere is no corresponding record for this reference.8Duran-Frigola, M.; Bertoni, M.; Blanco, R.; Martínez, V.; Pauls, E.; Alcalde, V.; Turon, G.; Villegas, N.; Fernández-Torras, A.; Pons, C.; Aloy, P. Bioactivity Profile Similarities to Expand the Repertoire of COVID-19 Drugs. J. Chem. Inf. Model. 2020, DOI: 10.1021/acs.jcim.0c00420 Google ScholarThere is no corresponding record for this reference.9Xu, C.; Ke, Z.; Liu, C.; Wang, Z.; Liu, D.; Zhang, L.; Wang, J.; He, W.; Xu, Z.; Li, Y. Systemic In Silico Screening in Drug Discovery for Coronavirus Disease (COVID-19) with an Online Interactive Web Server. J. Chem. Inf. Model. 2020, DOI: 10.1021/acs.jcim.0c00821 Google ScholarThere is no corresponding record for this reference.10Gahlawat, A.; Kumar, N.; Kumar, R.; Sandhu, H.; Singh, I. P.; Singh, S.; Sjöstedt, A.; Garg, P. Structure-Based Virtual Screening to Discover Potential Lead Molecules for the SARS-CoV-2 Main Protease. J. Chem. Inf. Model. 2020, DOI: 10.1021/acs.jcim.0c00546 Google ScholarThere is no corresponding record for this reference.11Suárez, D.; Díaz, N. SARS-CoV-2 Main Protease: A Molecular Dynamics Study. J. Chem. Inf. Model. 2020, DOI: 10.1021/acs.jcim.0c00575 Google ScholarThere is no corresponding record for this reference.12Ngo, S. T.; Quynh Anh Pham, N.; Thi Le, L.; Pham, D.-H.; Vu, V. V. Computational Determination of Potential Inhibitors of SARS-CoV-2 Main Protease. J. Chem. Inf. Model. 2020, DOI: 10.1021/acs.jcim.0c00491 Google ScholarThere is no corresponding record for this reference.13Kumar, S.; Sharma, P. P.; Shankar, U.; Kumar, D.; Joshi, S. K.; Pena, L.; Durvasula, R.; Kumar, A.; Kempaiah, P.; Poonam; Rathi, B. Discovery of New Hydroxyethylamine Analogs against 3CLpro Protein Target of SARS-CoV-2: Molecular Docking, Molecular Dynamics Simulation, and Structure–Activity Relationship Studies. J. Chem. Inf. Model. 2020, DOI: 10.1021/acs.jcim.0c00326 Google ScholarThere is no corresponding record for this reference.14Deeks, H. M.; Walters, R. K.; Barnoud, J.; Glowacki, D. R.; Mulholland, A. J. Interactive Molecular Dynamics in Virtual Reality Is an Effective Tool for Flexible Substrate and Inhibitor Docking to the SARS-CoV-2 Main Protease. J. Chem. Inf. Model. 2020, DOI: 10.1021/acs.jcim.0c01030 Google ScholarThere is no corresponding record for this reference.15Wang, R.; Hozumi, Y.; Yin, C.; Wei, G.-W. Decoding SARS-CoV-2 Transmission and Evolution and Ramifications for COVID-19 Diagnosis, Vaccine, and Medicine. J. Chem. Inf. Model. 2020, DOI: 10.1021/acs.jcim.0c00501 Google ScholarThere is no corresponding record for this reference.16Smith, J. C. Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19. J. Chem. Inf. 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Request reuse permissionsArticle Views4676Altmetric-Citations14Learn about these metrics closeArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated.Recommended Articles FiguresReferencesThis publication has no figures.References This article references 16 other publications. 1Zimmerman, M. I.; Porter, J. R.; Ward, M. D.; Singh, S.; Vithani, N.; Meller, A.; Mallimadugula, U. L.; Kuhn, C. E.; Borowsky, J. H.; Wiewiora, R. P. SARS-CoV-2 Simulations Go Exascale to Capture Spike Opening and Reveal Cryptic Pockets Across the Proteome. bioRxiv 2020, DOI: 10.1101/2020.06.27.175430 There is no corresponding record for this reference.2Casalino, L.; Gaieb, Z.; Goldsmith, J. A.; Hjorth, C. K.; Dommer, A. C.; Harbison, A. M.; Fogarty, C. A.; Barros, E. P.; Taylor, B. C.; McLellan, J. S. Beyond Shielding: The Roles of Glycans in the SARS-CoV-2 Spike Protein. ACS Cent. Sci. 2020, 6 (10), 1722– 1734, DOI: 10.1021/acscentsci.0c01056 2Beyond Shielding: The Roles of Glycans in the SARS-CoV-2 Spike ProteinCasalino, Lorenzo; Gaieb, Zied; Goldsmith, Jory A.; Hjorth, Christy K.; Dommer, Abigail C.; Harbison, Aoife M.; Fogarty, Carl A.; Barros, Emilia P.; Taylor, Bryn C.; McLellan, Jason S.; Fadda, Elisa; Amaro, Rommie E.ACS Central Science (2020), 6 (10), 1722-1734CODEN: ACSCII; ISSN:2374-7951. (American Chemical Society) The ongoing COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in more than 28,000,000 infections and 900,000 deaths worldwide to date. Antibody development efforts mainly revolve around the extensively glycosylated SARS-CoV-2 spike (S) protein, which mediates host cell entry by binding to the angiotensin-converting enzyme 2 (ACE2). Similar to many other viral fusion proteins, the SARS-CoV-2 spike utilizes a glycan shield to thwart the host immune response. Here, we built a full-length model of the glycosylated SARS-CoV-2 S protein, both in the open and closed states, augmenting the available structural and biol. data. Multiple microsecond-long, all-atom mol. dynamics simulations were used to provide an atomistic perspective on the roles of glycans and on the protein structure and dynamics. We reveal an essential structural role of N-glycans at sites N165 and N234 in modulating the conformational dynamics of the spike's receptor binding domain (RBD), which is responsible for ACE2 recognition. This finding is corroborated by biolayer interferometry expts., which show that deletion of these glycans through N165A and N234A mutations significantly reduces binding to ACE2 as a result of the RBD conformational shift toward the "down" state. Addnl., end-to-end accessibility analyses outline a complete overview of the vulnerabilities of the glycan shield of the SARS-CoV-2 S protein, which may be exploited in the therapeutic efforts targeting this mol. machine. Overall, this work presents hitherto unseen functional and structural insights into the SARS-CoV-2 S protein and its glycan coat, providing a strategy to control the conformational plasticity of the RBD that could be harnessed for vaccine development. The glycan shield is a sugary barrier that helps the viral SARS-CoV-2 spikes to evade the immune system. Beyond shielding, two of the spike's glycans are discovered to prime the virus for infection. >> More from SciFinder ®https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXhvVOlsb3N&md5=52d499afcd7e3caa7d9e6017ffa86e453Turoňová, B.; Sikora, M.; Schürmann, C.; Hagen, W. J. H.; Welsch, S.; Blanc, F. E. C.; von Bülow, S.; Gecht, M.; Bagola, K.; Hörner, C. In Situ Structural Analysis of SARS-CoV-2 Spike Reveals Flexibility Mediated by Three Hinges. Science (Washington, DC, U. S.) 2020, 370 (6513), 203– 208, DOI: 10.1126/science.abd5223 3In situ structural analysis of SARS-CoV-2 spike reveals flexibility mediated by three hingesTuronova, Beata; Sikora, Mateusz; Schuermann, Christoph; Hagen, Wim J. H.; Welsch, Sonja; Blanc, Florian E. C.; von Buelow, Soeren; Gecht, Michael; Bagola, Katrin; Hoerner, Cindy; van Zandbergen, Ger; Landry, Jonathan; de Azevedo, Nayara Trevisan Doimo; Mosalaganti, Shyamal; Schwarz, Andre; Covino, Roberto; Muehlebach, Michael D.; Hummer, Gerhard; Krijnse Locker, Jacomine; Beck, MartinScience (Washington, DC, United States) (2020), 370 (6513), 203-208CODEN: SCIEAS; ISSN:1095-9203. (American Association for the Advancement of Science) The spike protein (S) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is required for cell entry and is the primary focus for vaccine development. In this study, we combined cryo-electron tomog., subtomogram averaging, and mol. dynamics simulations to structurally analyze S in situ. Compared with the recombinant S, the viral S was more heavily glycosylated and occurred mostly in the closed prefusion conformation. The stalk domain of S contains 3 hinges, giving the head unexpected orientational freedom. We propose that the hinges allow S to scan the host cell surface, shielded from antibodies by an extensive glycan coat. The structure of native S contributes to our understanding of SARS-CoV-2 infection and potentially to the development of safe vaccines. >> More from SciFinder ®https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXitVCrsbbO&md5=6464149155235d90f7a9d0972a50f4fb4Grant, O. C.; Montgomery, D.; Ito, K.; Woods, R. J. Analysis of the SARS-CoV-2 Spike Protein Glycan Shield Reveals Implications for Immune Recognition. Sci. Rep. 2020, 10 (1), 14991, DOI: 10.1038/s41598-020-71748-7 There is no corresponding record for this reference. >> More from SciFinder ®https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=&md5=9874b665cc7a056b8e2f928dd31124405Amaro, R. E.; Mulholland, A. J. A Community Letter Regarding Sharing Bimolecular Simulation Data for COVID-19. J. Chem. Inf. Model. 2020, 60 (6), 2653, DOI: 10.1021/acs.jcim.0c00319 5A Community Letter Regarding Sharing Biomolecular Simulation Data for COVID-19Amaro, Rommie E.; Mulholland, Adrian J.Journal of Chemical Information and Modeling (2020), 60 (6), 2653-2656CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society) There is an urgent need to share our methods, models, and results openly and quickly to test findings, ensure reproducibility, test significance, eliminate dead-ends, and accelerate discovery. Sharing of data for COVID-19 applications will help connect scientists across the global biomol. simulation community, and also improve connection and communication between simulation and exptl. and clin. data and investigators. We, as a community, commit to the following principles and offer our support to others already working on open data efforts in the hope that others working on COVID-19 in biomol. simulation and other areas will adopt similar best practices. >> More from SciFinder ®https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXmsFKksrw%253D&md5=1a38fbcdcd674f2a4a5cf6ca90ad57c36Avram, S.; Curpan, R.; Halip, L.; Bora, A.; Oprea, T. I. Off-Patent Drug Repositioning. J. Chem. Inf. Model. 2020, DOI: 10.1021/acs.jcim.0c00826 There is no corresponding record for this reference.7Edwards, A. What Are the Odds of Finding a COVID-19 Drug from a Lab Repurposing Screen?. J. Chem. Inf. Model. 2020, DOI: 10.1021/acs.jcim.0c00861 There is no corresponding record for this reference.8Duran-Frigola, M.; Bertoni, M.; Blanco, R.; Martínez, V.; Pauls, E.; Alcalde, V.; Turon, G.; Villegas, N.; Fernández-Torras, A.; Pons, C.; Aloy, P. Bioactivity Profile Similarities to Expand the Repertoire of COVID-19 Drugs. J. Chem. Inf. Model. 2020, DOI: 10.1021/acs.jcim.0c00420 There is no corresponding record for this reference.9Xu, C.; Ke, Z.; Liu, C.; Wang, Z.; Liu, D.; Zhang, L.; Wang, J.; He, W.; Xu, Z.; Li, Y. Systemic In Silico Screening in Drug Discovery for Coronavirus Disease (COVID-19) with an Online Interactive Web Server. J. Chem. Inf. Model. 2020, DOI: 10.1021/acs.jcim.0c00821 There is no corresponding record for this reference.10Gahlawat, A.; Kumar, N.; Kumar, R.; Sandhu, H.; Singh, I. P.; Singh, S.; Sjöstedt, A.; Garg, P. Structure-Based Virtual Screening to Discover Potential Lead Molecules for the SARS-CoV-2 Main Protease. J. Chem. Inf. Model. 2020, DOI: 10.1021/acs.jcim.0c00546 There is no corresponding record for this reference.11Suárez, D.; Díaz, N. SARS-CoV-2 Main Protease: A Molecular Dynamics Study. J. Chem. Inf. Model. 2020, DOI: 10.1021/acs.jcim.0c00575 There is no corresponding record for this reference.12Ngo, S. T.; Quynh Anh Pham, N.; Thi Le, L.; Pham, D.-H.; Vu, V. V. Computational Determination of Potential Inhibitors of SARS-CoV-2 Main Protease. J. Chem. Inf. Model. 2020, DOI: 10.1021/acs.jcim.0c00491 There is no corresponding record for this reference.13Kumar, S.; Sharma, P. P.; Shankar, U.; Kumar, D.; Joshi, S. K.; Pena, L.; Durvasula, R.; Kumar, A.; Kempaiah, P.; Poonam; Rathi, B. Discovery of New Hydroxyethylamine Analogs against 3CLpro Protein Target of SARS-CoV-2: Molecular Docking, Molecular Dynamics Simulation, and Structure–Activity Relationship Studies. J. Chem. Inf. Model. 2020, DOI: 10.1021/acs.jcim.0c00326 There is no corresponding record for this reference.14Deeks, H. M.; Walters, R. K.; Barnoud, J.; Glowacki, D. R.; Mulholland, A. J. Interactive Molecular Dynamics in Virtual Reality Is an Effective Tool for Flexible Substrate and Inhibitor Docking to the SARS-CoV-2 Main Protease. J. Chem. Inf. Model. 2020, DOI: 10.1021/acs.jcim.0c01030 There is no corresponding record for this reference.15Wang, R.; Hozumi, Y.; Yin, C.; Wei, G.-W. Decoding SARS-CoV-2 Transmission and Evolution and Ramifications for COVID-19 Diagnosis, Vaccine, and Medicine. J. Chem. Inf. Model. 2020, DOI: 10.1021/acs.jcim.0c00501 There is no corresponding record for this reference.16Smith, J. C. Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19. J. Chem. Inf. Model. 2020, DOI: 10.1021/acs.jcim.0c01010 There is no corresponding record for this reference.
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