Edward Wallace, Edward.Wallace@ed.ac.uk
Christopher Wood, Chris.Wood@ed.ac.uk
Project Description
Fungal pathogens of humans are a major global threat to human health, as highlighted by the 2022 World Health Organisation fungal priority pathogens list. The project aims to computationally identify potential drug targets in fungal pathogens. One major challenge in antifungal drug development is that fungi are relatively closely related to humans, making it difficult to target essential fungal proteins without also targeting their human homologs. New data and AI approaches make it possible to address this systematically, by combining fungal genome data and functional genetic screens of essential proteins with protein structural predictions and structural similarity analysis. The project aims first, to assemble a list of the proteins that are present in WHO critical priority fungal pathogens and absent in humans, using both sequence and structural data. This list will be compared to published data on the functional importance of proteins for fungal virulence, that would inform which conserved protein families to prioritise for follow-up. Second, the project aims to look in detail at AI-predicted molecular surfaces to find features such as pockets, active sites, and binding sites, that are conserved within these fungal pathogens and not in humans. This molecular surface analysis is more fine-grained and could identify candidate drug target surfaces that are distinct in fungi from in humans. Overall, the project aims to leverage the power of protein structural prediction and big data to prioritise candidate antifungal drug targets at a large scale.
