Computational Mapping and Targeting of BK Channel Protein-Protein Interactions in Breast Cancer

Area of Science:

• Biophysics
• Computational Biology
• Molecular Oncology

Background:

• Large-conductance calcium-activated potassium (BK) channels are crucial for cellular functions and implicated in cancer pathogenesis.
• BK channel dysregulation, particularly overexpression, correlates with enhanced tumor cell proliferation in breast cancer.

Purpose of the Study:

• To establish an integrative computational framework for identifying and characterizing BK channel protein-protein interactions (PPI) in breast cancer.
• To rationally design peptide-based modulators targeting these identified BK channel PPIs.

Main Methods:

• RNA-seq differential expression analysis to identify KCNMA1 (BK channel gene) overexpression in breast cancer.
• Integration of transcriptomic data with interaction databases and PPI prediction for network construction.
• Structural modeling and molecular dynamics simulations to characterize BK channel interaction interfaces.
• Structure-guided design of peptide modulators targeting the BK-LINGO1 complex.

Main Results:

• Significant KCNMA1 overexpression was observed in estrogen-sensitive breast cancer cells.
• A breast cancer-specific BK channel interaction network was constructed, identifying ACTG2, LINGO1, and RAB4A as key partners.
• Stable, partner-specific interaction interfaces were revealed through structural modeling and simulations.
• The first computational model of the BK-LINGO1 complex identified a transmembrane interface.
• Novel peptide variants with enhanced stability and binding energetics were designed.

Conclusions:

• The study presents a robust computational framework for mapping BK channel PPIs in breast cancer.
• Targeting BK channel-associated PPIs via rational peptide design is feasible.
• This work opens avenues for selective BK channel modulation in cancer therapeutics.