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

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.

Trafficking of JC virus-like particles across the blood-brain barrier.

Hollow viral vectors, such as John Cunningham virus-like particles (JC VLPs), provide a unique opportunity to deliver drug cargo into targeted cells and tissue. Current understanding of the entry of JC virus in brain cells has remained insufficient. In particular, interaction of JC VLPs with the blood-brain barrier (BBB) has not been analyzed in detail.

T cell epitope mapping of JC polyoma virus-encoded proteome reveals reduced T cell responses in HLA-DRB1*04:01+ donors.

JC polyomavirus (JCV) infection is highly prevalent and usually kept in a persistent state without clinical signs and symptoms. It is only during immunocompromise and especially impaired CD4(+) T cell function in the brain, as seen in AIDS patients or natalizumab-treated multiple sclerosis patients, that JCV may cause progressive multifocal leukoencephalopathy (PML), an often life-threatening brain disease. Since CD4(+) T cells likely play an important role in controlling JCV infection, we here describe the T cell response to JCV in a group of predominantly HLA-DR-heterozygotic healthy donors (HD) by using a series of overlapping 15-mer peptides spanning all JCV-encoded open reading frames.