DeepViral: prediction of novel virus-host interactions from protein sequences and infectious disease phenotypes.
KAUST DepartmentBio-Ontology Research Group (BORG)
Biological and Environmental Science and Engineering (BESE) Division
Computational Bioscience Research Center (CBRC)
Computer Science Program
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Online Publication Date2021-03-03
Print Publication Date2021-09-09
Permanent link to this recordhttp://hdl.handle.net/10754/668043
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AbstractMotivationInfectious diseases caused by novel viruses have become a major public health concern. Rapid identification of virus-host interactions can reveal mechanistic insights into infectious diseases and shed light on potential treatments. Current computational prediction methods for novel viruses are based mainly on protein sequences. However, it is not clear to what extent other important features, such as the symptoms caused by the viruses, could contribute to a predictor. Disease phenotypes (i.e., signs and symptoms) are readily accessible from clinical diagnosis and we hypothesize that they may act as a potential proxy and an additional source of information for the underlying molecular interactions between the pathogens and hosts.ResultsWe developed DeepViral, a deep learning based method that predicts protein-protein interactions (PPI) between humans and viruses. Motivated by the potential utility of infectious disease phenotypes, we first embedded human proteins and viruses in a shared space using their associated phenotypes and functions, supported by formalized background knowledge from biomedical ontologies. By jointly learning from protein sequences and phenotype features, DeepViral significantly improves over existing sequence-based methods for intra- and inter-species PPI prediction.AvailabilityCode and datasets for reproduction and customization are available at https://github.com/bio-ontology-research-group/DeepViral. Prediction results for 14 virus families are available at https://doi.org/10.5281/zenodo.4429824.
CitationLiu-Wei, W., Kafkas, Ş., Chen, J., Dimonaco, N. J., Tegnér, J., & Hoehndorf, R. (2021). DeepViral: prediction of novel virus–host interactions from protein sequences and infectious disease phenotypes. Bioinformatics. doi:10.1093/bioinformatics/btab147
SponsorsWe would like to thank Maxat Kulmanov and Mona Alshahrani for their advice on earlier versions of this work. We also thank Jeffery Law for making public the mappings of the SARS-CoV-2 proteins. We acknowledge the use of computational resources from the KAUST Supercomputing Core Laboratory.
PublisherOxford University Press (OUP)
CollectionsArticles; Bio-Ontology Research Group (BORG); Biological and Environmental Science and Engineering (BESE) Division; Bioscience Program; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Except where otherwise noted, this item's license is described as This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
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