Accurate prediction of virus-host protein-protein interactions via a Siamese neural network using deep protein sequence embeddings.

ABSTRACT: Prediction and understanding of virus-host protein-protein interactions (PPIs) have relevance for the development of novel therapeutic interventions. In addition, virus-like particles open novel opportunities to deliver therapeutics to targeted cell types and tissues. Given our incomplete knowledge of PPIs on the one hand and the cost and time associated with experimental procedures on the other, we here propose a deep learning approach to predict virus-host PPIs. Our method (Siamese Tailored deep sequence Embedding of Proteins [STEP]) is based on recent deep protein sequence embedding techniques, which we integrate into a Siamese neural network. After showing the state-of-the-art performance of STEP on external datasets, we apply it to two use cases, severe acute respiratory syndrome coro

SUBMITTER: Madan S 

PROVIDER: S-EPMC9481957 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature