title: DeNovo: virus-host sequence-based protein-protein interaction prediction.
abstract: Can we predict protein-protein interactions (PPIs) of a novel virus with its host? Three major problems arise: the lack of known PPIs for that virus to learn from, the cost of learning about its proteins and the sequence dissimilarity among viral families that makes most methods inapplicable or inefficient. We develop DeNovo, a sequence-based negative sampling and machine learning framework that learns from PPIs of different viruses to predict for a novel one, exploiting the shared host proteins. We tested DeNovo on PPIs from different domains to assess generalization.
  
  
  
  By solving the challenge of generating less noisy negative interactions, DeNovo achieved accuracy up to 81 and 86% when predicting PPIs of viral proteins that have no and distant sequence similarity to the ones used for training, receptively. This result is comparable to the best achieved in single virus-host and intra-species PPI prediction cases. Thus, we can now predict PPIs for virtually any virus infecting human. DeNovo generalizes well; it achieved near optimal accuracy when tested on bacteria-human interactions.
  
  
  
  Code, data and additional supplementary materials needed to reproduce this study are available at: https://bioinformatics.cs.vt.edu/~alzahraa/denovo
  
  
  
  alzahraa@vt.edu
  
  
  
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Computer Science, Virginia Tech, Blacksburg, VA, USA Department of Systems and Computer Engineering, Faculty of Engineering, Al-Azhar University, Cairo, Egypt.
authors.forename: Fatma-Elzahraa
authors.initials: FE
authors.lastname: Eid
authors.affiliation: Biomedical Informatics and Chemoinformatics Research Group, Department of Informatics and Systems, Center of Excellence for Advanced Sciences, National Research Center, Giza, Egypt.
authors.forename: Mahmoud
authors.initials: M
authors.lastname: ElHefnawi
authors.affiliation: Department of Computer Science, Virginia Tech, Blacksburg, VA, USA.
authors.forename: Lenwood S
authors.initials: LS
authors.lastname: Heath
mesh_terms: D005544:Forecasting; D006801:Humans; D025941:Protein Interaction Mapping; D017422:Sequence Analysis, DNA; D014764:Viral Proteins; D014780:Viruses
chemical_list: D014764:Viral Proteins