bioRxivBiophysicsPreprint
Combining MicroED and native mass spectrometry for structural discovery of enzyme-biosynthetic inhibitor complexes
Niko W. VlahakisiD, Cameron W. FlowersiD, Mengting LiuiD, Matthew P. AgdanowskiiD, Samuel E. Johnson, Jacob A. SummersiD, Catherine Keyser, Phoebe Russell, Samuel L. RoseiD, Julien OrlansiD, Nima Adhami, Yu CheniD, Michael R. SawayaiD, Shibom BasuiD, Daniele de SanctisiD, Soichi Wakatsuki AiD, Hosea M. NelsoniD, Joseph A. LooiD, Yi TangiD, José A. RodrígueziD
Abstract
With the goal of accelerating the discovery of small molecule-protein complexes, we leverage fast, low-dose, event based electron counting microcrystal electron diffraction (MicroED) data collection and native mass spectrometry. This approach resolves structures of the epoxide-based cysteine protease inhibitor, and natural product, E-64, and its biosynthetic analogs bound to the model cysteine protease, papain. The combined structural power of MicroED and the analytical capabilities of native mass spectrometry (ED-MS) allows assignment of papain structures bound to E-64-like ligands with data obtained from crystal slurries soaked with mixtures of known inhibitors, and crude biosynthetic reactions. ED-MS further discriminates the highest-affinity ligand soaked into microcrystals from a broad inhibitor cocktail, and identifies multiple similarly high-affinity ligands soaked into microcrystals simultaneously. This extends to libraries of printed ligands dispensed directly onto TEM grids and later soaked with papain microcrystal slurries. ED-MS identifies papain binding to its preferred natural products, by showing that two analogues of E-64 outcompete others in binding to papain crystals, and by detecting papain bound to E-64 and an analogue from crude biosynthetic reactions, without purification. This illustrates the utility of ED-MS for natural product ligand discovery and for structure-based screening of small molecule binders to macromolecular targets.
- assay: structural biology
- research focus: protein structure prediction
- research focus: drug response / drug discovery
- approach: experimental assay
- Citations
- 0
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