Prediction of human-virus protein-protein interactions through a sequence embedding-based machine learning method

Xiaodi Yang, Shiping Yang, Qinmengge Li, Stefan Wuchty, Ziding Zhang

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

The identification of human-virus protein-protein interactions (PPIs) is an essential and challenging research topic, potentially providing a mechanistic understanding of viral infection. Given that the experimental determination of human-virus PPIs is time-consuming and labor-intensive, computational methods are playing an important role in providing testable hypotheses, complementing the determination of large-scale interactome between species. In this work, we applied an unsupervised sequence embedding technique (doc2vec) to represent protein sequences as rich feature vectors of low dimensionality. Training a Random Forest (RF) classifier through a training dataset that covers known PPIs between human and all viruses, we obtained excellent predictive accuracy outperforming various combinations of machine learning algorithms and commonly-used sequence encoding schemes. Rigorous comparison with three existing human-virus PPI prediction methods, our proposed computational framework further provided very competitive and promising performance, suggesting that the doc2vec encoding scheme effectively captures context information of protein sequences, pertaining to corresponding protein-protein interactions. Our approach is freely accessible through our web server as part of our host-pathogen PPI prediction platform (http://zzdlab.com/InterSPPI/). Taken together, we hope the current work not only contributes a useful predictor to accelerate the exploration of human-virus PPIs, but also provides some meaningful insights into human-virus relationships.

Original languageEnglish (US)
Pages (from-to)153-161
Number of pages9
JournalComputational and Structural Biotechnology Journal
Volume18
DOIs
StatePublished - 2020
Externally publishedYes

Keywords

  • Doc2vec
  • Embedding
  • Human-virus interaction
  • Machine learning
  • Prediction
  • Protein-protein interaction

ASJC Scopus subject areas

  • Biotechnology
  • Biophysics
  • Structural Biology
  • Biochemistry
  • Genetics
  • Computer Science Applications

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