Viral infection involves a large number of protein-protein interactions (PPIs) between human and virus. The PPIs range from the initial binding of viral coat proteins to host membrane receptors to the hijacking of host transcription machinery.
We have developed the LSTM model with word2vec to predict PPIs between human and virus, named LSTM-PHV, by using amino acid sequences alone. In predicting PPIs between human and unknown or new virus, the LSTM-PHV presented higher performance than the existing predictors. Interestingly, learning of only sequence contexts as words presented remarkably high performances.
Use of the LSTM-PHV enhances the screening of drug targets that inhibit human-virus PPIs and definitely contributes to advances in remedies of infectious diseases including COVID-19.