Development of drugs for Epstein–Barr virus using high-throughput in silico virtual screening

Abstract

Importance of the field: Epstein–Barr virus (EBV) is a ubiquitous human herpesvirus that is causally associated with endemic forms of Burkitt's lymphoma, nasopharyngeal carcinoma and lymphoproliferative disease in immunosuppressed individuals. On a global scale, EBV infects > 90% of the adult population and is responsible for ∼ 1% of all human cancers. To date, there is no efficacious drug or therapy for the treatment of EBV infection and EBV-related diseases.Areas covered in this review: In this review, we discuss the existing anti-EBV inhibitors and those under development. We discuss the value of different molecular targets, including EBV lytic DNA replication enzymes as well as proteins that are expressed exclusively during latent infection, such as EBV nuclear antigen 1 (EBNA-1) and latent membrane protein 1. As the atomic structure of the EBNA-1 DNA binding domain has been described, it is an attractive target for in silico methods of drug design and small molecule screening. We discuss the use of computational methods that can greatly facilitate the development of novel inhibitors and how in silico screening methods can be applied to target proteins with known structures, such as EBNA-1, to treat EBV infection and disease.What the reader will gain: The reader is familiarized with the problems in targeting of EBV for inhibition by small molecules and how computational methods can greatly facilitate this process.Take home message: Despite the impressive efficacy of nucleoside analogs for the treatment of herpesvirus lytic infection, there remain few effective treatments for latent infections. As EBV latent infection persists within and contributes to the formation of EBV-associated cancers, targeting EBV latent proteins is an unmet medical need. High-throughput in silico screening can accelerate the process of drug discovery for novel and selective agents that inhibit EBV latent infection and associated disease.