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Horlacher, M. ; Oleshko, S. ; Hu, Y. ; Ghanbari, M.* ; Cantini, G. ; Schinke, P. ; Vergara, E.E. ; Bittner, F.* ; Mueller, N.S.* ; Ohler, U.* ; Moyon, L. ; Marsico, A.

A computational map of the human-SARS-CoV-2 protein-RNA interactome predicted at single-nucleotide resolution.

NAR Gen. Bioinfo. 5:lqad010 (2023)
Postprint DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
RNA-binding proteins (RBPs) are critical host factors for viral infection, however, large scale experimental investigation of the binding landscape of human RBPs to viral RNAs is costly and further complicated due to sequence variation between viral strains. To fill this gap, we investigated the role of RBPs in the context of SARS-CoV-2 by constructing the first in silico map of human RBP-viral RNA interactions at nucleotide-resolution using two deep learning methods (pysster and DeepRiPe) trained on data from CLIP-seq experiments on more than 100 human RBPs. We evaluated conservation of RBP binding between six other human pathogenic coronaviruses and identified sites of conserved and differential binding in the UTRs of SARS-CoV-1, SARS-CoV-2 and MERS. We scored the impact of mutations from 11 variants of concern on protein-RNA interaction, identifying a set of gain- and loss-of-binding events, as well as predicted the regulatory impact of putative future mutations. Lastly, we linked RBPs to functional, OMICs and COVID-19 patient data from other studies, and identified MBNL1, FTO and FXR2 RBPs as potential clinical biomarkers. Our results contribute towards a deeper understanding of how viruses hijack host cellular pathways and open new avenues for therapeutic intervention.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Neural-networks; In-vivo; Binding; Sars-cov-2; Reveals; Coronavirus; Identification; Specificity; Sequence; Variant
ISSN (print) / ISBN 2631-9268
e-ISSN 2631-9268
Zeitschrift NAR: Genomics and Bioinformatics
Quellenangaben Band: 5, Heft: 1, Seiten: , Artikelnummer: lqad010 Supplement: ,
Verlag Oxford University Press
Verlagsort Great Clarendon St, Oxford Ox2 6dp, England
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
Förderungen Berlin Center of Machine Learning - German Ministry for Education and Research
Joachim Herz Foundation
Deutsche Forschungsgemeinschaft
Helmholtz Association under the joint research school 'Munich School for Data Science