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

Area of Science:

• Structural Biology
• Computational Biology
• Cancer Research

Background:

• Protein-protein interaction (PPI) networks are vital for understanding cancer phenotypes and molecular mechanisms.
• Targeting protein pockets in cancer-related PPIs (oncoPPIs) offers a strategy to modulate protein function.

Purpose of the Study:

• To build a comprehensive pocketome of oncoPPIs to identify ligandable pockets.
• To analyze pocket properties across different cancer types and identify therapeutic targets.
• To develop a framework for evaluating and prioritizing novel disease targets.

Main Methods:

• Constructed a pocketome from 314 crystallographically solved oncoPPIs.
• Employed 3D geometric and energetic descriptors to identify and classify ligandable pockets.
• Analyzed ligand-bound pockets and built 3D oncoPPI networks to identify protein hubs.

Main Results:

• Identified key cancer-relevant proteins and interacting residues by integrating network and structural pocket data.
• Highlighted the therapeutic potential of targeting ligandable 3D oncoPPIs with clinical examples (S100A1, NRP1, CTNNB1, VCP).
• Created a publicly available reference dataset for future research.

Conclusions:

• The study provides a flexible framework for evaluating and prioritizing novel disease targets.
• Targeting ligandable pockets in oncoPPIs presents a promising therapeutic strategy for cancer treatment.
• The developed dataset and framework will aid future research in cancer drug discovery.