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bioRxiv Preprint

Viruses hijack the host cell's machinery for the purpose of viral replication and interfere with the activity of master regulatory proteins - including RNA binding proteins (RBPs). These RBPs are major actors in several steps of RNA processing, able to recognize and bind to their target RNAs by means of sequence or structure motifs. While host RBPs are known to represent critical factors for RNA viral replication, stability, and escape of host immune responses, their role in the context of SARS-CoV-2 infection remains poorly understood. Few experimental studies have mapped the SARS-CoV-2 RNA-protein interactome in infected human cells, but they are limited in the resolution and exhaustivity of their output. In contrast, computational approaches enable rapid screening of a large number of human RBPs for putative interactions with the viral RNA and are thus crucial to prioritize candidates for further experimental investigation. Here, we investigated the role of RBPs in the context of SARS-CoV-2 by constructing a first single-nucleotide in silico map of human RBP / viral RNA interactions. To this end, we trained pysster and DeepRiPe, two deep learning methods based on convolutional neural networks, to learn the sequence preferences of >100 RBPs from eCLIP and PAR-CLIP data generated on human cell lines. We then applied our models cross-species to predict the propensity of each host RBP to bind to the SARS-CoV-2 RNA genome at single-base resolution. We further evaluated conservation of RBP binding between 6 other human pathogenic coronaviruses and identified sites of conserved and differential binding in the untranslated regions of SARS-CoV-1, SARS-CoV-2 and MERS. We scored the impact of sequence variants from 11 viral strains on protein-RNA interaction, including alpha, delta and omicron strains, and identified a set of gain-and loss of binding events. Further, we performed a systematic in silico mutagenesis to screen the SARS-CoV-2 genome for hypothetical high impact variants, which provides a resource to anticipate the regulatory impact of variants on novel strains. Lastly, we explore the clinical impact of the identified RBPs by linking them to other functional data and OMICs on COVID-19 patients from other studies. Our results contribute towards a deeper understanding of how viruses hijack host cellular pathways by providing insights into new players of host-virus interactions and provide a rich resource that enables the discovery of new antiviral targets and therapeutics. To facilitate the use of our results in future studies, we integrated the protein-RNA interaction map and variant impact predictions into an online resource ( By providing the community with pre-trained RBP models we enable host-viral RNA interaction prediction for any (RNA) virus beyond SARS-CoV-2 and provide a tool to efficiently monitor new viral strains.

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