# Target Mapping in Cancer: Ligandable Protein Pockets on 3D OncoPPI Networks

## TL;DR

This study identifies ligandable protein pockets in cancer-related PPI networks to guide drug discovery and target prioritization.

## Contribution

A new framework combining structural and network data to identify and prioritize ligandable oncoPPI targets for drug development.

## Key findings

- Combining 3D oncoPPI networks with structural data identifies key cancer-relevant proteins and residues.
- Examples like S100A1 and NRP1 demonstrate the therapeutic potential of targeting ligandable oncoPPIs.
- A publicly available dataset is provided to support future cancer drug discovery efforts.

## Abstract

Background/Objectives: Studying protein–protein interaction (PPI) networks is crucial in understanding cancer phenotypes and molecular mechanisms. Here, we focus on PPIs involved in 12 different types of cancer (oncoPPIs), highlighting those protein pockets serving as outposts to modulate protein functioning. Methods: To explore these cavities linked to the cancer phenotype changes, we built a comprehensive pocketome of 314 crystallographically solved oncoPPIs. Based on this experimental data, we identified and investigated all ligandable protein pockets by employing 3D geometric and energetic descriptors. These pockets were classified as suitable for designing new oncoPPI modulators or PROTACs. The ligand-bound crystallographic pockets were analyzed to compare their properties across cancer types. Finally, 3D oncoPPI networks were built for each cancer type to identify highly connected proteins acting as hubs. Results: Combining interaction networks with structural pocket data helps identify cancer-relevant proteins and key interacting residues. Using this approach, we present clinical examples (e.g., S100A1, NRP1, CTNNB1, VCP) to show the therapeutic value of targeting ligandable 3D oncoPPIs. We also provide a publicly available reference dataset supporting future research. Conclusions: Notably, this study offers a flexible framework for evaluating and prioritizing novel disease targets.