Large-scale chemoproteomics expedites ligand discovery and predicts ligand behavior in cells
Fabian Offensperger, Gary Tin, Miquel Duran‐Frigola, Elisa Hahn, Sarah Dobner, Christopher W. am Ende, Joseph W. Strohbach, Andrea Rukavina, Vincenth Brennsteiner, Kevin Ogilvie, Nara Marella, Katharina Kladnik, Rodolfo Ciuffa, Jaimeen D. Majmudar, S. Denise Field, Ariel Bensimon, Luca Ferrari, Evandro Ferrada, Amanda Hui Qi Ng, Zhechun Zhang, Gianluca Degliesposti, Andras Boeszoermenyi, Sascha Martens, Robert V. Stanton, André C. Müller, J. Thomas Hannich, David Hepworth, Giulio Superti‐Furga, Stefan Kubicek, Monica Schenone, Georg E. Winter
Abstract
Chemical modulation of proteins enables a mechanistic understanding of biology and represents the foundation of most therapeutics. However, despite decades of research, 80% of the human proteome lacks functional ligands. Chemical proteomics has advanced fragment-based ligand discovery toward cellular systems, but throughput limitations have stymied the scalable identification of fragment-protein interactions. We report proteome-wide maps of protein-binding propensity for 407 structurally diverse small-molecule fragments. We verified that identified interactions can be advanced to active chemical probes of E3 ubiquitin ligases, transporters, and kinases. Integrating machine learning binary classifiers further enabled interpretable predictions of fragment behavior in cells. The resulting resource of fragment-protein interactions and predictive models will help to elucidate principles of molecular recognition and expedite ligand discovery efforts for hitherto undrugged proteins.