LSTM-PHV: prediction of human-virus protein-protein interactions by LSTM with word2vec.

ABSTRACT: 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. However, few interspecies PPIs have been identified, because experimental methods including mass spectrometry are time-consuming and expensive, and molecular dynamic simulation is limited only to the proteins whose 3D structures are solved. Sequence-based machine learning methods are expected to overcome these problems. We have first developed the LSTM model with word2vec to predict PPIs between human and virus, named LSTM-PHV, by using amino acid sequences alone. The LSTM-PHV effectively learnt the training data with a highly imbalanced ratio of pos

SUBMITTER: Tsukiyama S 

PROVIDER: S-EPMC8574953 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature