Utilizing advanced machine learning techniques, the research team integrated phylogenetics with AI to predict protein structures, mapping glycoprotein structures across the Flaviviridae family.

AI technologies, including AlphaFold and ESMFold, were employed to classify viral entry proteins, a task that traditional methods could not achieve.

The study notably reveals that Hepatitis C utilizes a novel entry mechanism, which is distinct from other viruses, highlighting the need for further investigation.

Dr. Joe Grove expressed hope that this new understanding of Hepatitis C's entry mechanism will play a crucial role in vaccine development.

The findings are significant for pandemic preparedness and understanding current viral threats, such as Mpox, which has been classified as a public health emergency by the WHO.

Glycoproteins are essential for virus entry and host interactions, yet they remain poorly characterized across many Flaviviridae species, which poses challenges for vaccine development.

Currently, there is no vaccine for Hepatitis C, and the findings from this research could significantly contribute to the development of one.

A recent study led by the MRC-University of Glasgow Center for Virus Research and the University of Sydney has focused on the Flaviviridae family of viruses, which includes well-known pathogens such as Dengue, Zika, and Hepatitis C.

The research team aims to expand their studies using AI technology to thousands of viruses, enhancing knowledge for current and emerging viral diseases.

The results of this study have been published in the prestigious medical journal Nature, providing critical insights into viral evolution and mechanisms.

This research represents one of the first systematic applications of protein structure prediction in virology, establishing a valuable resource for future investigations.

This study is particularly notable for identifying a completely new entry mechanism for Hepatitis C, which currently affects approximately 39,000 people in Scotland.