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      Targeted in situ cross-linking mass spectrometry and integrative modeling reveal the architectures of three proteins from SARS-CoV-2

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          Significance

          We present a generic methodology that extracts structural data from living, intact cells for any protein of interest. Application of this methodology to different viral proteins resulted in significant cross-link sets that revealed the connectivity within their structures. Importantly, we show that these cross-link sets are detailed enough to enable the integrative modeling of the full-length protein sequence. Consequently, we report the global structural organization of Nsp2 and the dimer of the nucleocapsid protein. We foresee that similar applications will be highly useful to study other recalcitrant proteins on which the mainstream structural approaches currently fail.

          Abstract

          Atomic structures of several proteins from the coronavirus family are still partial or unavailable. A possible reason for this gap is the instability of these proteins outside of the cellular context, thereby prompting the use of in-cell approaches. In situ cross-linking and mass spectrometry (in situ CLMS) can provide information on the structures of such proteins as they occur in the intact cell. Here, we applied targeted in situ CLMS to structurally probe Nsp1, Nsp2, and nucleocapsid (N) proteins from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and obtained cross-link sets with an average density of one cross-link per 20 residues. We then employed integrative modeling that computationally combined the cross-linking data with domain structures to determine full-length atomic models. For the Nsp2, the cross-links report on a complex topology with long-range interactions. Integrative modeling with structural prediction of individual domains by the AlphaFold2 system allowed us to generate a single consistent all-atom model of the full-length Nsp2. The model reveals three putative metal binding sites and suggests a role for Nsp2 in zinc regulation within the replication–transcription complex. For the N protein, we identified multiple intra- and interdomain cross-links. Our integrative model of the N dimer demonstrates that it can accommodate three single RNA strands simultaneously, both stereochemically and electrostatically. For the Nsp1, cross-links with the 40S ribosome were highly consistent with recent cryogenic electron microscopy structures. These results highlight the importance of cellular context for the structural probing of recalcitrant proteins and demonstrate the effectiveness of targeted in situ CLMS and integrative modeling.

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          MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.

          Efficient analysis of very large amounts of raw data for peptide identification and protein quantification is a principal challenge in mass spectrometry (MS)-based proteomics. Here we describe MaxQuant, an integrated suite of algorithms specifically developed for high-resolution, quantitative MS data. Using correlation analysis and graph theory, MaxQuant detects peaks, isotope clusters and stable amino acid isotope-labeled (SILAC) peptide pairs as three-dimensional objects in m/z, elution time and signal intensity space. By integrating multiple mass measurements and correcting for linear and nonlinear mass offsets, we achieve mass accuracy in the p.p.b. range, a sixfold increase over standard techniques. We increase the proportion of identified fragmentation spectra to 73% for SILAC peptide pairs via unambiguous assignment of isotope and missed-cleavage state and individual mass precision. MaxQuant automatically quantifies several hundred thousand peptides per SILAC-proteome experiment and allows statistically robust identification and quantification of >4,000 proteins in mammalian cell lysates.
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            The PRIDE database and related tools and resources in 2019: improving support for quantification data

            Abstract The PRoteomics IDEntifications (PRIDE) database (https://www.ebi.ac.uk/pride/) is the world’s largest data repository of mass spectrometry-based proteomics data, and is one of the founding members of the global ProteomeXchange (PX) consortium. In this manuscript, we summarize the developments in PRIDE resources and related tools since the previous update manuscript was published in Nucleic Acids Research in 2016. In the last 3 years, public data sharing through PRIDE (as part of PX) has definitely become the norm in the field. In parallel, data re-use of public proteomics data has increased enormously, with multiple applications. We first describe the new architecture of PRIDE Archive, the archival component of PRIDE. PRIDE Archive and the related data submission framework have been further developed to support the increase in submitted data volumes and additional data types. A new scalable and fault tolerant storage backend, Application Programming Interface and web interface have been implemented, as a part of an ongoing process. Additionally, we emphasize the improved support for quantitative proteomics data through the mzTab format. At last, we outline key statistics on the current data contents and volume of downloads, and how PRIDE data are starting to be disseminated to added-value resources including Ensembl, UniProt and Expression Atlas.
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              A SARS-CoV-2 Protein Interaction Map Reveals Targets for Drug-Repurposing

              SUMMARY The novel coronavirus SARS-CoV-2, the causative agent of COVID-19 respiratory disease, has infected over 2.3 million people, killed over 160,000, and caused worldwide social and economic disruption 1,2 . There are currently no antiviral drugs with proven clinical efficacy, nor are there vaccines for its prevention, and these efforts are hampered by limited knowledge of the molecular details of SARS-CoV-2 infection. To address this, we cloned, tagged and expressed 26 of the 29 SARS-CoV-2 proteins in human cells and identified the human proteins physically associated with each using affinity-purification mass spectrometry (AP-MS), identifying 332 high-confidence SARS-CoV-2-human protein-protein interactions (PPIs). Among these, we identify 66 druggable human proteins or host factors targeted by 69 compounds (29 FDA-approved drugs, 12 drugs in clinical trials, and 28 preclinical compounds). Screening a subset of these in multiple viral assays identified two sets of pharmacological agents that displayed antiviral activity: inhibitors of mRNA translation and predicted regulators of the Sigma1 and Sigma2 receptors. Further studies of these host factor targeting agents, including their combination with drugs that directly target viral enzymes, could lead to a therapeutic regimen to treat COVID-19.
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                Author and article information

                Journal
                Proc Natl Acad Sci U S A
                Proc Natl Acad Sci U S A
                pnas
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                24 August 2021
                09 August 2021
                09 August 2021
                : 118
                : 34
                : e2103554118
                Affiliations
                [1] aDepartment of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem , Jerusalem 9190401, Israel;
                [2] bHadassah Academic College Jerusalem , Jerusalem 9101001, Israel;
                [3] cDepartment of Microbiology and Molecular Genetics, Institute for Medical Research Israel-Canada, The Kuvin Center for the Study of Infectious and Tropical Diseases, The Hebrew University-Hadassah Medical School, The Hebrew University of Jerusalem , Jerusalem 9190401, Israel;
                [4] dClinical Virology Unit, Hadassah Hebrew University Medical Center , 9190401 Jerusalem, Israel;
                [5] eThe Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem , Jerusalem 9190401, Israel
                Author notes

                Edited by Ivet Bahar, University of Pittsburgh School of Medicine, Pittsburgh, PA, and approved July 13, 2021 (received for review February 22, 2021)

                Author contributions: M.S., J.Z., K.Z., A.R., M.L., D.S.-D., and N.K. designed research; M.S., J.Z., K.Z., and T.E. performed research; M.B., E.B., L.B., M.S.-R., A.F., D.G.W., A.R., and D.S.-D. contributed new reagents/analytic tools; M.S., J.Z., K.Z., M.B., D.S.-D., and N.K. analyzed data; and M.S., J.Z., K.Z., M.L., D.S.-D., and N.K. wrote the paper.

                1M.S., J.Z., and K.Z. contributed equally to this work.

                Author information
                https://orcid.org/0000-0002-6114-0301
                https://orcid.org/0000-0003-4600-6611
                https://orcid.org/0000-0002-5440-441X
                https://orcid.org/0000-0002-9357-4526
                https://orcid.org/0000-0003-1615-7136
                Article
                202103554
                10.1073/pnas.2103554118
                8403911
                34373319
                52d2f8d8-cfd4-4cc2-907e-f024c8a43ab9
                Copyright   2021 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).

                History
                Page count
                Pages: 9
                Funding
                Funded by: Israel Science Foundation (ISF) 501100003977
                Award ID: 1768/15
                Award Recipient : Joanna Zamel Award Recipient : Keren Zohar Award Recipient : Siona Eliyahu Award Recipient : Merav Braitbard Award Recipient : Michal Linial Award Recipient : Dina Schneidman-Duhovny Award Recipient : Nir Kalisman
                Funded by: Israel Science Foundation (ISF) 501100003977
                Award ID: 3753/20
                Award Recipient : Joanna Zamel Award Recipient : Keren Zohar Award Recipient : Siona Eliyahu Award Recipient : Merav Braitbard Award Recipient : Michal Linial Award Recipient : Dina Schneidman-Duhovny Award Recipient : Nir Kalisman
                Funded by: Israel Science Foundation (ISF) 501100003977
                Award ID: 1466/18
                Award Recipient : Joanna Zamel Award Recipient : Keren Zohar Award Recipient : Siona Eliyahu Award Recipient : Merav Braitbard Award Recipient : Michal Linial Award Recipient : Dina Schneidman-Duhovny Award Recipient : Nir Kalisman
                Funded by: FSHD Global Research Foundation (FSHDGRF) 100009657
                Award ID: 41
                Award Recipient : Moriya Slavin
                Categories
                408
                530
                Biological Sciences
                Biophysics and Computational Biology
                Custom metadata
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                structural biology,mass spectrometry,in-cell techniques,integrative modeling

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