Integrative computational analysis of HCMV-encoded miRNAs: Mapping host protein interactions and immune evasion mechanisms

Abstract

Human Cytomegalovirus (HCMV) is a widespread virus that poses serious risks to newborns and individuals with weakened immune systems, often causing severe disease. One of the virus's most cunning strategies for survival is its ability to produce its microRNAs (miRNAs), which allow it to subtly hijack the host's cellular machinery, evade immune detection, and persist in the body. The interaction between miRNAs and their target proteins in host cells provides significant insights into the mechanisms of HCMV infection. In recent years, computational tools have become essential in uncovering the secrets of these viral miRNAs. Using a combination of machine learning models, sequence alignment tools, and secondary structure prediction algorithms, researchers have been able to identify HCMV-encoded miRNAs and predict their interactions with host and viral genes. These in silico approaches help trace how viral miRNAs interfere with key immune processes, such as antigen presentation and interferon signaling, offering insight into how HCMV weakens antiviral responses. Computational analyses also reveal how these miRNAs affect protein-protein interactions and cellular pathways crucial for immune defense. Despite these advances, challenges remain, including difficulties in validating predictions and understanding the context-dependent roles of miRNAs. This review highlights how computational biology has transformed our understanding of HCMV-host interactions, while also acknowledging the need for more accurate, integrative models to bridge the gap between prediction and biological reality.