From Deep Oceans to Egyptian Mummies – Artificial Intelligence Assisted Metabolomics for Natural Product Drug Discovery

The laborious and expensive process of identifying a single bioactive molecule for a target protein from numerous complex crude extracts hinders natural product discovery. To speed up and simplify this iterative process of fractionation and bioactivity testing, we introduce a scalable native metabolomics approach. This method combines non-targeted liquid chromatography-tandem mass spectrometry with the detection of protein binding through native mass spectrometry.[1] Heterotrimeric G proteins represent an interesting, but so far “undruggable” pharmacological target despite being involved in the regulation of a wide range of physiological and pathophysiological processes in living organisms.[2] At present, selective small molecule modulators specifically target Gαq and more recently Gαs leaving the remaining Gα subfamilies without such modulators. [3,4]

Here we apply native metabolomics approach for the discovery and isolation of the first small molecule binders of Gαi proteins. Subsequently, we apply MS- and NMR-based methods combined with AI-algorithms to accelerate the structure elucidation of the new Gαi binders through semi-automated substructure annotation and dereplication against NP spectra and structure databases. [5-8] Further biochemical characterizations of two binders reveal that one selectively binds to Gαi and functionally inhibits its intrinsic GTPase function, paving the way for fundamental studies and new therapeutic strategies towards Gαi-mediated diseases.

Additionally, we apply LC-MS/MS metabolomics tools for the rationalization and chemotaxonomic assignments of traditional medicinal preparations from ancient culturesm, e.g. archaeological artefacts of embalming residues from Egyptian mummies.