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?:abstract
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Viral zoonoses are a serious threat to public health and global security, as reflected by the current scenario of the growing number of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cases. However, as pathogenic viruses are highly diverse, identification of their host ranges remains a major challenge. Here, we present a combined computational and experimental framework, called REceptor ortholog-based POtential virus hoST prediction (REPOST), for the prediction of potential virus hosts. REPOST first selects orthologs from a diverse species by identity and phylogenetic analyses. Secondly, these orthologs is classified preliminarily as permissive or non-permissive type by infection experiments. Then, key residues are identified by comparing permissive and non-permissive orthologs. Finally, potential virus hosts are predicted by a key residue–specific weighted module. We performed REPOST on SARS-CoV-2 by studying angiotensin-converting enzyme 2 orthologs from 287 vertebrate animals. REPOST efficiently narrowed the range of potential virus host species (with 95.74% accuracy).
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