Startseite
English
Erweiterte Suche
Durchblättern nach ...
Publikationen im Überblick
Ansprechpartner
Hilfe
DOI
PMC
 |
|
|
Open Access Gold möglich sobald Verlagsversion bei der ZB eingereicht worden ist.
A computational map of the human-SARS-CoV-2 protein-RNA interactome predicted at single-nucleotide resolution.
NAR Gen. Bioinfo. 5:lqad010 (2023)
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.
Altmetric
Weitere Metriken?
Zusatzinfos bearbeiten
[➜Einloggen]
Publikationstyp
Artikel: Journalartikel
Dokumenttyp
Wissenschaftlicher Artikel
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,
Artikelnummer: lqad010
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
Joachim Herz Foundation
Deutsche Forschungsgemeinschaft
Helmholtz Association under the joint research school 'Munich School for Data Science