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  1. Author Response

    Reviewer #1 (Public Review):

    This manuscript by Borsatto et al describes atomic-level structural details of the central core domain of non-structural protein 1 (Nsp1) of SARS-CoV-2, the virus responsible for the ongoing COVID-19 pandemic. Authors combined X-ray crystallography, fragment screening, computational modelling, and molecular dynamics simulation approaches to characterize potentially druggable pockets in Nsp1 core (aa 10-126). This study presents several notable strengths. For example, authors screened and tested 60 fragments from the Maybridge Ro3 library and solved a co-crystal structure of Nsp1 core with one such fragment 2E10 (N-(2,3dihydro-1H-inden-5-yl) acetamide) to 1.1Å resolution. The molecular dynamics simulation and other computational experiments were performed rigorously.

    Nsp1 blocks the path of mRNA in ribosomes to modulate protein synthesis in the host cell. Nsp1 also binds the first stem-loop (SL1) of SARS-CoV-2 mRNA. The authors used a molecular docking program (HADDOCK) to build models of the Nsp1/RNA complex and predicted two modes of Nsp1 binding to SL1 RNA. A comparative structural analysis of Nsp1/2E10 experimental structure with Nsp1/SL1 (model) reveals that small molecule compounds occupying this site may block RNA binding of Nsp1. Given the established role of this interface in modulating the host and viral gene expression programs, this finding provides an important framework for designing the small molecules capable of completely blocking this interface.

    A weakness of this study is the lack of experimental validation of the two modes of Nsp1 binding to SL1 RNA.

    The mechanism of binding, in particular whether Nsp1 binds to the ribosome first and then to the SL1 or the other way round, is still debated. Moreover, to the best of our knowledge, to this day there is no structure of the N-terminal region Nsp1 bound to the ribosome. Thus, we expect that obtaining a structure of the binary and possibly ternary complex to validate the predicted binding mode will necessitate considerable time and efforts and will hopefully be the focus of a follow up study.

    Reviewer #2 (Public Review):

    In this manuscript, the authors have identified cryptic pockets in the Nsp1 protein of the SARSCoV-2 virus. The authors used computational methods to identify these pockets and demonstrate drug binding via simulation studies. The authors also show that such cryptic pockets exist in other beta-coronaviruses as well.

    The authors carried out fragment-based screening using macromolecular crystallography and confirmed the presence of drug bound in one of the pockets identified. However, the binding assays showed a weak binding with high error.

    The weak binding is typical for fragments, however we agree that the error was high, therefore, we re-measured the data (both for Nsp1N and full-length Nsp1) to bring the error down. The new values can be found in Figure 6 – Figure supplement 2.

    Further, the authors perform Nsp1-mRNA simulation studies to identify how Nsp1 binds to the 5'UTR of SARS-CoV-2 mRNA and mention that targeting the identified pocket in Nsp1-N could disrupt the SARS-CoV-2 Nsp1-mRNA complex. However, there are conflicting reports on direct binding between the SARS-CoV-2 Nsp1-mRNA (See references 17 & 29).
    Nsp1 helps establish viral infection in the host, and hence identifying the druggable site in this protein is important. Therefore, this study is important and exciting.

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  2. eLife assessment

    SARS-CoV-2 nonstructural protein (Nsp1) has emerged as an attractive target as it plays an important role in modulating the host and viral gene expression. This study describes multiple druggable sites in Nsp1. A 1.1Å co-crystal structure of Nsp1 with a fragment, together with computational studies, provides a framework for the rational design of potential antiviral candidates. This important study is methodologically convincing and will be of interest to researchers in the fields of structural virology and rational drug design.

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  3. Reviewer #1 (Public Review):

    This manuscript by Borssato et al describes atomic-level structural details of the central core domain of nonstructural protein 1 (Nsp1) of SARS-CoV-2, the virus responsible for the ongoing COVID-19 pandemic. Authors combined X-ray crystallography, fragment screening, computational modeling, and molecular dynamics simulation approaches to characterize potentially druggable pockets in Nsp1 core (aa 10-126). This study presents several notable strengths. For example, authors screened and tested 60 fragments from the Maybridge Ro3 library and solved a co-crystal structure of Nsp1 core with one such fragment 2E10 (N-(2,3-dihydro-1H-inden-5-yl)acetamide) to 1.1Å resolution. The molecular dynamics simulation and other computational experiments were performed rigorously.

    Nsp1 blocks the path of mRNA in ribosomes to modulate protein synthesis in the host cell. Nsp1 also binds the first stem-loop (SL1) of SARS-CoV-2 mRNA. The authors used a molecular docking program (HADDOCK) to build models of the Nsp1/RNA complex and predicted two modes of Nsp1 binding to SL1 RNA. A comparative structural analysis of Nsp1/2E10 experimental structure with Nsp1/SL1 (model) reveals that small molecule compounds occupying this site may block RNA binding of Nsp1. Given the established role of this interface in modulating the host and viral gene expression programs, this finding provides an important framework for designing the small molecules capable of completely blocking this interface.

    A weakness of this study is the lack of experimental validation of the two modes of Nsp1 binding to SL1 RNA.

    Was this evaluation helpful?
  4. Reviewer #2 (Public Review):

    In this manuscript, the authors have identified cryptic pockets in the Nsp1 protein of the SARS-CoV-2 virus. The authors used computational methods to identify these pockets and demonstrate drug binding via simulation studies. The authors also show that such cryptic pockets exist in other beta-coronaviruses as well.

    The authors carried out fragment-based screening using macromolecular crystallography and confirmed the presence of drug bound in one of the pockets identified. However, the binding assays showed a weak binding with high error. Further, the authors perform Nsp1-mRNA simulation studies to identify how Nsp1 binds to the 5'UTR of SARS-CoV-2 mRNA and mention that targeting the identified pocket in Nsp1-N could disrupt the SARS-CoV-2 Nsp1-mRNA complex. However, there are conflicting reports on direct binding between the SARS-CoV-2 Nsp1-mRNA (See references 17 & 29).

    Nsp1 helps establish viral infection in the host, and hence identifying the druggable site in this protein is important. Therefore, this study is important and exciting.

    Was this evaluation helpful?
  5. Reviewer #3 (Public Review):

    In this manuscript, Borsatto et. al. have attempted to identify druggable cryptic pockets in the Non-structural protein 1 (Nsp1) of SARS-CoV-2. The authors analyzed analyzed molecular dynamics simulations of Non-structural protein 1 (Nsp1) of SARS-CoV-2 to search for potential drug binding pockets. The authors analyzed potential drug binding pocket volumes in unbiased simulations and utilized a Hamiltonian replica exchange scheme called SWISH to search for additional cryptic binding sites. The authors utilized conformations from their simulations to conduct a computational screen of potential drug fragments, and experimentally tested their predictions by soaking Nsp1 crystals with predicted fragment hits, and found that 1 of 60 predicted hits binds in a predicted pocket with mM binding affinity, and identified crystal packing contacts that may have prevented additional fragment hit binding. Finally, they ran simulations of Nsp1 in complex with RNA which suggest that ligand binding in pocket 1 may hinder RNA complex formation and run simulations of homologous Nsp1 in additional CoV genera to determine if the identified pockets are conserved.

    The authors utilized two approaches for identifying potential drug binding pockets: unbiased MD simulations and the SWISH hamiltonian replica exchange that scales water protein interactions to explore the opening of more hydrophobic binding cavities, which can be stabilized by cosolvent benzene molecules. The authors identify 2 potential pockets (pockets 1 and 2) from unbiased simulations, and identify an additional 2-pockets (pockets 3 and 4) from SWISH simulations. Pockets 2-4 are connected by a shallow groove identified on the x-ray structure, but are substantially deeper than this groove. The authors proceed to use the FTDyn and FTMap programs to search for potential fragment binding spots, and identified that pocket 1 contained the largest number binding hotspots (~50%), and that many predicted binding hotspots were found in the cryptic pockets discovered by SWISH.

    The authors proceeded to test their predictions by soaking 60 fragment hits obtained by FTMap and FTDyn, identified a single fragment that binds in Fragment 1, and solved the X-ray structure of this bound fragment. They also utilized microscale thermophoresis and thermal shift assays to measure a Kd value of 2.74 + 2.63mM. The authors then proceeded to analyze crystal packing contacts and identify packing contacts that may have prevented additional fragment hit binding. Finally, they ran simulations of Nsp1 in complex with RNA which suggest that ligand binding in pocket 1 may hinder RNA complex formation and run simulations of homologous Nsp1 in additional CoV genera to determine if the identified pockets are conserved.

    The authors were successful in identifying an experimentally verifying a druggable pocket in Nsp1. It is unclear to me however, to what extent the features of the this pocket are cryptic, and if the fragment that was found to bind could have been discovered using only the crystal structure, as this ligand appears to bind to a cavity identified by the Fpocket software from a crystal structure. In a sense the authors have computationally identified and experimentally verified a druggable pocket, and have proposed the presence of 3 additional potentially druggable cryptic pockets with strong computational evidence, but have not experimentally verified the druggablity of the proposed cryptic pockets.

    This manuscript represents an excellent demonstration of a state-of-the-art MD based computational methods for druggable pocket discovery on an important drug target. The experimental verification fragment binding to one of the identified sites, and the identification of putative additional sites, provide a foundation for future rational drug discovery campaigns of SARS-CoV-2 and other CoVs.

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  6. SciScore for 10.1101/2022.05.20.492819: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The outputs let us identify and visualize the pockets observed throughout the whole simulation time with the PyMol software.
    PyMol
    suggested: (PyMOL, RRID:SCR_000305)
    25 potential fragment hits were discovered by running PanDDA, and one of them was verified by manual inspection in COOT followed by refinement in Phenix.
    COOT
    suggested: (Coot, RRID:SCR_014222)
    The sequences were aligned with MEGA, version 11.0.11. 53 The resulting multi sequence alignment was used to construct the Maximum-Likelihood Tree with MEGA.
    MEGA
    suggested: (Mega BLAST, RRID:SCR_011920)

    Results from OddPub: Thank you for sharing your data.


    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.


    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.


    Results from JetFighter: We did not find any issues relating to colormaps.


    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.


    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

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