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protein-protein interaction (PPI) network presented here was generated through the joint efforts of the QCRG (QBI Coronavirus Research Group). This work and corresponding methods can be found in the Nature publication: A SARS-CoV-2 Protein Interaction Map Reveals Targets for Drug-Repurposing; co-corresponding authors: Nevan J. Krogan, Marco Vignuzzi, Adolfo García-Sastre, Kevan M. Shokat, Brian K. Shoichet (Nature Accelerated Article Preview).\nIn late 2019 the emergence and rapid spread of the novel coronavirus SARS-CoV-2 (causative agent of COVID-19), lead to widespread global infections and the World Health Organization (WHO) declared a pandemic on March 11, 2020. With no antiviral drugs and no available vaccine to combat the virus, countries around the world enacted physical distancing programs, the only proven defense for stalling the spread of infection and mitigating the catastrophic death rate. In addition to the devastating toll on human health, the profound cultural and economic effect of the pandemic and social distancing efforts highlighted an acute and urgent need for efficacious vaccines and drug treatment strategies. Quickly, investigators around the world began clinical trials of various drugs known to combat other pathogens with the hopes that one or more might treat COVID-19, though our limited molecular knowledge of SARS-CoV-2 pathogenesis obstructed these efforts. To devise effective therapeutic strategies that counteract SARS-CoV-2 infection and COVID-19 pathology, it is crucial to understand the molecular details for how the virus hijacks host cell machinery, and to apply this knowledge towards developing new drugs and repurposing existing ones.\nDuring infection, viral proteins interact with host products to promote their own replication. In parallel, sentinel programs in the host cell activate innate defenses that the cell uses to protect itself and prevent the spread of disease to other host cells. Therefore, physical viral-host protein-protein interactions (PPIs) represent a combination of dependency factors (host products the virus needs to replicate) and restriction factors (products the host uses to defend itself and prevent viral spread). Identifying SARS-CoV-2 viral protein interactors is thus an important first step to clarifying the molecular pathology of infection. Towards this end, our group individually cloned, tagged, and expressed SARS-CoV-2 (2019-nCoV/USA-WA1/2020) proteins in human cells and identified the human proteins physically associated with each bait using a proteomics technique called affinity-purification mass spectrometry (AP-MS). In total, we captured and identified 332 high confidence host-pathogen PPIs for 27 (26 wild type and 1 mutant) SARS-CoV-2 bait proteins and map them in the interactive network provided here. Among these, we identify 66 druggable human proteins targeted by 69 compounds (29 FDA-approved drugs, 12 drugs in clinical trials, and 28 preclinical compounds). Note that SARS-CoV-2 encodes 29 protein products, three of which we were unable to obtain high quality PPI data for, therefore they are not present in this network.\nIn our interactive SARS-CoV-2 network all edges (blue and black lines) are clickable. Blue edges are based on our MS data and will provide interaction measurements from proteomics identifications and scoring. Black edges are human-human PPIs and are curated from CORUM, IntAct, and Reactome (as of 2020-04). All human prey nodes (grey and yellow circles) are clickable and will provide information mined from Uniprot, CHEMBL and IUPHAR databases (as of 2020-04). In cases where the human prey protein was identified as a drug target by the QCRG team, we have provided additional relevant drug targeting information and chemical structures. All SARS-CoV-2 bait nodes (red diamonds) are clickable and provide data mined from literature references and the PDB (as of 2020-04). Note that all SARS-CoV-2 protein and gene functions described for each of the 26 wild type and single mutant baits are based on the functions of homologous genes from other coronavirus species (mainly SARS-CoV and MERS-CoV). An in-depth literature review for each SARS-CoV-2 bait subnetwork and the corresponding references can be found in the Supplemental Discussion of the publication above. The SARS-CoV-2 baits in this study were designed from sequence alignments provided by Chan et. al. 2020 and Wu et. al. 2020, and though we are reasonably sure of the gene annotations, we want to be clear that not every protein has been verified to be expressed or functional during SARS-CoV-2 infections, either in vitro or in vivo.  We want to stress that this network represents the first step towards understanding the molecular interactions of SARS-CoV-2 and ongoing research is being done to fill in more structural, global, and mechanistic details.","funding":[{"funder":{"name":"National Institute of Allergy and Infectious Diseases (NIAID)"},"identifier":"U19AI135972","url":"https://reporter.nih.gov/project-details/9455006"}],"identifier":"fluomics_SC2_PPI","infectiousAgent":["https://www.ontobee.org/ontology/NCBITaxon?iri=http://purl.obolibrary.org/obo/NCBITaxon_2697049"],"name":"An interactive SARS-CoV-2 virus-human protein-protein interaction map","url":"https://ppi.zoiclabs.io/#/"},{"@context":{"bioschemas":"https://discovery.biothings.io/ns/bioschemas/","niaid":"https://discovery.biothings.io/ns/niaid/","rdf":"http://www.w3.org/1999/02/22-rdf-syntax-ns#","rdfs":"http://www.w3.org/2000/01/rdf-schema#","schema":"http://schema.org/"},"@type":"niaid:ComputationalTool","_id":"5c6605a4f9f5becd","_meta":{"class_id":"niaid::niaid:ComputationalTool","guide":"/guide/niaid/ComputationalTool","private":false,"username":"flaneuse"},"_score":2.9645581,"_ts":{"date_created":"2022-06-01T19:51:08.598074+00:00","last_updated":"2022-06-01T19:51:08.598074+00:00"},"creator":[{"name":"Systems biology of Clostridium difficile infection"}],"description":"The prewas R package allows users to create a binary SNP matrix from a whole genome alignment. The SNP matrix includes the following features: (1) multiple line representation of multiallelic sites, (2) multiple line representation for SNPs present in overlapping genes, and (3) choice over the reference allele. Additionally, users can collapse SNPs into genes so the output is a binary gene matrix. Output from the prewas package should be used as the input to bacterial GWAS tools such as hogwash.","funding":[{"funder":{"name":"National Institute of Allergy and Infectious Diseases (NIAID)"},"identifier":"U01-AI124255","url":"https://reporter.nih.gov/project-details/9108106"}],"identifier":"michigan_prewas","infectiousAgent":["Clostridium difficile"],"name":"prewas","sdPublisher":{"name":"GitHub"},"url":"https://github.com/Snitkin-Lab-Umich/prewas"}]}