Prediction of receptorome for human-infecting virome
Zheng Zhang, Sifan Ye, Aiping Wu, Taijiao Jiang, Yousong Peng
doi: https://doi.org/10.1101/2020.02.27.967885
Abstract
The virus receptor is key for viral infection of host cells. Identification of the virus receptor is still challenging at present. Our previous study has shown that human virus receptor proteins have some unique features including high level of N-glycosylation, high number of interaction partners and high expressions. Here, we built a random-forest model to identify human virus receptorome from human cell membrane proteins with accepted accuracy based on combination of unique features of human virus receptors and protein sequences. A total of 729 human cell membrane proteins were predicted to constitute the receptorome for the human-infecting virome. Combination of the random-forest model with protein-protein interactions between human and viruses predicted in previous studies further predicted receptors for 693 human-infecting viruses, such as the Enterovirus, Norovirus and West Nile virus. Finally, we predicted the candidate alternative receptors for the 2019-nCoV. The study would greatly facilitate identification of receptors for human-infecting virome.
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