Key result
Computational mapping of breast cancer BK channels identifies key partners enabling targeted peptide modulator design.
Why the study?
BK channel overexpression is implicated in cancer, but frameworks to identify, structurally characterize, and rationally target BK channel-associated protein-protein interactions in breast cancer were needed.
Establishes a computational framework for mapping BK channel protein-protein interactions in breast cancer and demonstrates the feasibility of targeting these interactions through rational peptide design.
May enable BK-targeted peptide therapies in breast cancer; extends research by establishing a computational framework for mapping oncogenic PPIs.
Large-conductance Ca²⁺-activated potassium (BK) channels are widely expressed across human tissues and play fundamental roles in the regulation of diverse cellular processes. Dysregulation of BK channel expression or activity has been implicated in multiple pathological conditions, including cancer, where BK channel overexpression is associated with enhanced tumor cell proliferation and altered cellular dynamics. In this study, we present an integrative computational framework to identify, structurally characterize, and rationally target BK channel-associated protein-protein interactions (PPI) in breast cancer. RNA-seq differential expression analysis revealed significant overexpression of KCNMA1 in estrogen-sensitive breast cancer cells, supporting a central role for BK channels in tumor-associated phenotypes. By integrating transcriptomic data with curated interaction databases and PPI prediction methods, we constructed a breast cancer-specific interaction network centered on BK and identified ACTG2, LINGO1, and RAB4A as high-confidence interaction partners. Structural modeling and coarse-grained molecular dynamics simulations revealed stable, partner-specific interaction interfaces between BK and each interactor, identifying key residues governing complex formation. Building on these results, we present the first computational structural model of the BK-LINGO1 complex, which reveals a predominantly hydrophobic transmembrane interface consistent with the established role of LINGO1 as a regulatory accessory subunit. Leveraging this PPI interface, we designed peptide-based modulators using a structure-guided approach and identified peptide variants with enhanced conformational stability and favorable binding energetics. Overall, our work establishes a robust computational framework for mapping BK channel protein-protein interactions in breast cancer and demonstrates the feasibility of targeting these interactions through rational peptide design, opening new opportunities for the selective modulation of BK channel function in cancer.
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González-Avendaño et al. (2026) studied this question. Computational mapping of BK channels in breast cancer identified ACTG2, LINGO1, and RAB4A as key interaction partners, enabling the structure-guided design of targeted peptide modulators.
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