Presentation description

Protein-metabolite interactions (PMIs) are key to understanding cellular metabolism, but fully mapping them remains a challenge. Omics data, like proteomics and metabolomics, offer a broad look at the molecular landscape, but on their own, they don't tell the full story. Integrating this data with tools like MIDAS (mass spectrometry integrated with equilibrium dialysis for the discovery of allostery systematically) can give a much clearer, more comprehensive picture. By combining MIDAS data with external databases like UniProt and HMDB, it's possible to build a more accurate map of these interactions.

In this project, a coding pipeline in R was developed to integrate MIDAS data with annotations from these external resources. The goal was to identify both known interactions as well as potentially novel, uncharacterized interactions, helping to generate new hypotheses. The project also looks at how PMI patterns relate to protein domain data, with the aim of uncovering new metabolite-binding motifs that could play a role in metabolic regulation.

While results are still in progress, this work lays the groundwork for a deeper understanding of metabolic networks and pathway enrichment. Ultimately, it could help clarify the roles of thousands of proteins and metabolites in cellular processes, paving the way for new drug development or therapeutic interventions, particularly for diseases like cancer.