Large-scale chemoproteomics expedites ligand discovery and predicts ligand behavior in cells

ABSTRACT: Chemical modulation of protein function enables a mechanistic understanding of biological circuits and represents the foundation of most medicines. However, despite major efforts over decades of research, around 80% of the human proteome still lack functional ligands. Chemical proteomics allowed the advancement of fragment-based ligand discovery (FBLD) towards cellular systems and emerged as a promising strategy to detect reversible fragment-protein interactions that can be furnished into bioactive chemical probes. Limitations in scale and throughput have, however, stymied the interpretation of the resulting ligandability maps, thus complicating the identification of specific and actionable fragment-protein interactions. Here, we report reversible, global ligandability maps for 435 structurally different fragments, collectively representing 6739 discrete interactions. We benchmark the dataset by showing that identified fragments can be advanced to active chemical probes. Integrating multi-layered ligandability portraits with artificial intelligence (AI) and machine learning (ML) enabled quantitative and qualitative predictions of fragment interactomes via chemically interpretable models. The resulting, interactive catalogue of fragment-protein interactions and predictive models is expected to expedite ligand discovery efforts in a community-wide fashion and should facilitate the pursuit of hitherto undrugged target proteins.

INSTRUMENT(S): Orbitrap Fusion Lumos

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Kidney

SUBMITTER: Nara Marella  

LAB HEAD: Georg Winter

PROVIDER: PXD041587 | Pride | 2024-05-07

REPOSITORIES: Pride