Discovering the complete enhancer map of human herpesviruses using a natural language processing model
Nilabja Roy Chowdhury1, Deepanway Ghosal2, Vyacheslav Gurevich1
1Daniella Lee Casper Laboratory in Viral Oncology, Azrieli Faculty of Medicine, Bar-Ilan University, Safed, Israel.
Nature Communications
|December 2, 2025
Summary
Researchers developed ENHAvir, an AI tool using natural language processing (NLP), to identify enhancers in herpesviruses. This tool successfully predicts novel viral enhancers and confirms their activity, advancing our understanding of viral gene regulation.
Area of Science:
- Genomics
- Virology
- Bioinformatics
Background:
- Enhancers are crucial cis-regulatory elements controlling gene expression.
- Herpesviruses display distinct gene expression patterns during latent and lytic phases.
- Six Kaposi's sarcoma-associated herpesvirus (KSHV) enhancers were previously identified.
Purpose of the Study:
- To develop a computational tool for identifying enhancers in herpesvirus genomes.
- To predict novel enhancer elements in human herpesviruses.
- To compare viral and human enhancer characteristics.
Main Methods:
- A natural language processing (NLP) model, ENHAvir, was trained on KSHV enhancer and non-enhancer sequences.
- ENHAvir was used to predict enhancers in various human herpesviruses.
- Predicted enhancers were validated using enhancer reporter assays.
Main Results:
- ENHAvir successfully identified known and predicted novel enhancers in human herpesviruses.
- The activity of predicted enhancers was confirmed experimentally in HSV-2, HCMV, HHV-6, HHV-7, and EBV.
- Terminal repeats of herpesviruses function as potent enhancers.
- Conserved enhancer signatures and Alu elements were found when comparing viral and human enhancers.
Conclusions:
- ENHAvir is an effective AI tool for predicting enhancers in viral genomes.
- The study identified novel enhancers across multiple human herpesviruses.
- Comparative analysis reveals shared features between viral and human enhancers.
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