Accurate Prediction of Virus-Host Protein-Protein Interactions via a Siamese Neural Network Using Deep Protein Sequence Embeddings

Abstract

Prediction and understanding of tissue-specific virus-host interactions have relevance for the development of novel therapeutic interventions strategies. In addition, virus-like particles (VLPs) open novel opportunities to deliver therapeutic compounds to targeted cell types and tissues. Given our incomplete knowledge of virus-host interactions on one hand and the cost and time associated with experimental procedures on the other, we here propose a novel deep learning approach to predict virus-host protein-protein interactions (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 architecture. After evaluating the high prediction performance of STEP in comparison to an existing method, we apply it to two use cases, SARS-CoV-2 and John Cunningham polyomavirus (JCV), to predict virus protein to human host interactions. For the SARS-CoV-2 spike protein our method predicts an interaction with the sigma 2 receptor, which has been suggested as a drug target. As a second use case, we apply STEP to predict interactions of the JCV VP1 protein showing an enrichment of PPIs with neurotransmitters, which are known to function as an entry point of the virus into glial brain cells. In both cases we demonstrate how recent techniques from the field of Explainable AI (XAI) can be employed to identify those parts of a pair of sequences, which most likely contribute to the protein-protein interaction. Altogether our work highlights the potential of deep sequence embedding techniques originating from the field of natural language processing as well as XAI methods for the analysis of biological sequences. We have made our method publicly available via GitHub. The bigger pictureDevelopment of novel cell and tissue specific therapies requires a profound knowledge about protein-protein interactions (PPIs). Identifying these PPIs with experimental approaches such as biochemical assays or yeast two-hybrid screens is cumbersome, costly, and at the same time difficult to scale. Computational approaches can help to prioritize huge amounts of possible PPIs by learning from biological sequences plus already-known PPIs. In this work, we developed a novel approach (Siamese Tailored deep sequence Embedding of Proteins - STEP) that is based on recent deep protein sequence embedding techniques, which we integrate into a Siamese neural network architecture. We use this approach to train models by utilizing protein sequence information and known PPIs. After evaluating the high prediction performance of STEP in comparison to an existing method, we apply it to two use cases, SARS-CoV-2 and John Cunningham polyomavirus (JCV), to predict virus protein to human host interactions. Altogether our work highlights the potential of deep sequence embedding techniques originating from the field of natural language processing as well as Explainable AI methods for the analysis of biological sequence data. HighlightsO_LIA novel deep learning approach (STEP) predicts virus protein to human host protein interactions based on recent deep protein sequence embedding and a Siamese neural network architecture C_LIO_LIPrediction of protein-protein interactions of the JCV VP1 protein and of the SARS-CoV-2 spike protein C_LIO_LIIdentification of parts of sequences that most likely contribute to the protein-protein interaction using Explainable AI (XAI) techniques C_LI Data Science MaturityDSML 3: Development/Pre-production: Data science output has been rolled out/validated across multiple domains/problems