Author: Zheng Zhang; Sifan Ye; Aiping Wu; Taijiao Jiang; Yousong Peng
Title: Prediction of receptorome for human-infecting virome Document date: 2020_2_28
ID: 9ruhvpbv_6
Snippet: Previous studies have developed computational models for predicting PPIs between viruses and hosts which could help identification of virus receptors (Lasso et al., 2019; Yan et al., 2019) . For example, Lasso et al. developed an in silico computational framework (P-HIPSTer) that employs structural information to predict more than 280,000 PPIs between 1,001 human-infecting viruses and humans, and made a series of new findings about human-virus in.....
Document: Previous studies have developed computational models for predicting PPIs between viruses and hosts which could help identification of virus receptors (Lasso et al., 2019; Yan et al., 2019) . For example, Lasso et al. developed an in silico computational framework (P-HIPSTer) that employs structural information to predict more than 280,000 PPIs between 1,001 human-infecting viruses and humans, and made a series of new findings about human-virus interactions (Lasso et al., 2019) . Numerous PPIs between viral RBPs and human cell membrane proteins could be used to identify virus receptors. Here, we developed a computational model to predict the receptorome of the human-infecting virome based on the features of human virus receptors and protein sequences. Combination of the model with Lasso's work further predicted receptors for 693 human-infecting viruses. The study would greatly facilitate identification of human virus receptors.
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