Chemical tools enable the study of complex “macro” systems with molecular precision. Coupled with advances in analytical methods, single cell techniques, and data informatics, our generation of scientists has more power than ever to determine subtle effects in biology. Protein post-translational modifications (PTMs)–which often add/change only a few atoms of a kilodalton sized-system–are the cell’s way of actively fine-tuning signaling and gene expression in response to environmental cues.
One of the most common pathways to go awry in disease is metabolic regulation. Cancer cells become more metabolically active than they should be (a survival advantage), neurons experiencing tauopathic stress are less active than they should be (neurodegeneration), and adipocytes with misregulated sugar levels become resistant to insulin (type 2 diabetes). Facets of these diseases can be traced back to altered signaling pathways controlled by sugar levels in cells, which in turn modify proteins via PTMs.
By focusing on the development of chemical tools to “tag” sugar PTMs in living cells, we hope to understand these processes in molecular detail. Proteomics allows a view of which proteins are important for signaling, transcriptomics reveals how metabolic genes are switched on/off, and machine learning/data informatics strategies allow us to correlate these two very different types of “big” datasets and mine them for therapeutic action. Our glycopeptidomimetic design platform allows us to subsequently target these pathways in disease model systems.