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Interactome of SARS-CoV-2 / nCoV19 modulated host proteins with computationally predicted PPIs.
Karunakaran, Kalyani B; Balakrishnan, N; Ganapathiraju, Madhavi K.
  • Karunakaran KB; Supercomputer Education and Research Centre, Indian Institute of Science, Bangalore, 560 012, India.
  • Balakrishnan N; Supercomputer Education and Research Centre, Indian Institute of Science, Bangalore, 560 012, India.
  • Ganapathiraju MK; Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, USA.
Res Sq ; 2020 May 13.
Article in English | MEDLINE | ID: covidwho-670716
ABSTRACT
World over, people are looking for solutions to tackle the pandemic coronavirus disease (COVID-19) caused by the virus SARS-CoV-2/nCoV-19. Notable contributions in biomedical field have been characterizing viral genomes, host transcriptomes and proteomes, repurposable drugs and vaccines. In one such study, 332 human proteins targeted by nCoV19 were identified. We expanded this set of host proteins by constructing their protein interactome, including in it not only the known protein-protein interactions (PPIs) but also novel, hitherto unknown PPIs predicted with our High-precision Protein-Protein Interaction Prediction (HiPPIP) model that was shown to be highly accurate. In fact, one of the earliest discoveries made possible by HiPPIP is related to activation of immunity upon viral infection. We found that several interactors of the host proteins are differentially expressed upon viral infection, are related to highly relevant pathways, and that the novel interaction of NUP98 with CHMP5 may activate an antiviral mechanism leading to disruption of viral budding. We are making the interactions available as downloadable files to facilitate future systems biology studies and also on a web-server at http//hagrid.dbmi.pitt.edu/corona that allows not only keyword search but also queries such as "PPIs where one protein is associated with 'virus' and the interactors with 'pulmonary'".

Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Topics: Vaccines Language: English Year: 2020 Document Type: Article Affiliation country: Rs.3.rs-28592

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Topics: Vaccines Language: English Year: 2020 Document Type: Article Affiliation country: Rs.3.rs-28592