@article{González-Avendaño2026,

title = {Computational Mapping and Targeting of BK Channel Protein–Protein Interactions in Breast Cancer},

author = {Mariela González-Avendaño and Roberto Rosales-Rojas and Ariela Vergara-Jaque },

doi = {10.1021/acs.jcim.6c00191},

year  = {2026},

date = {2026-03-17},

abstract = {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.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{rosales-rojas_self-organization_2022,

title = {Self-Organization Dynamics of Collagen-like Peptides Crosslinking Is Driven by Rose-Bengal-Mediated Electrostatic Bridges},

author = {Roberto Rosales-Rojas and Matías Zuñiga-Bustos and Francisca Salas-Sepúlveda and Constanza Galaz-Araya and Ricardo A. Zamora and Horacio Poblete},

url = {https://www.mdpi.com/1999-4923/14/6/1148},

doi = {10.3390/pharmaceutics14061148},

issn = {1999-4923},

year  = {2022},

date = {2022-05-01},

urldate = {2025-01-02},

journal = {Pharmaceutics},

volume = {14},

number = {6},

pages = {1148},

abstract = {The present work focuses on the computational study of the structural micro-organization of hydrogels based on collagen-like peptides (CLPs) in complex with Rose Bengal (RB). In previous studies, these hydrogels computationally and experimentally demonstrated that when RB was activated by green light, it could generate forms of stable crosslinked structures capable of regenerating biological tissues such as the skin and cornea. Here, we focus on the structural and atomic interactions of two collagen-like peptides (collagen-like peptide I (CLPI), and collagen-like peptide II, (CLPII)) in the presence and absence of RB, highlighting the acquired three-dimensional organization and going deep into the stabilization effect caused by the dye. Our results suggest that the dye could generate a ternary ground-state complex between collagen-like peptide fibers, specifically with positively charged amino acids (Lys in CLPI and Arg in CLPII), thus stabilizing ordered three-dimensional structures. The discoveries generated in this study provide the structural and atomic bases for the subsequent rational development of new synthetic peptides with improved characteristics for applications in the regeneration of biological tissues during photochemical tissue bonding therapies.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{González-Avendaño2021,

title = {PPI-MASS: An Interactive Web Server to Identify Protein-Protein Interactions From Mass Spectrometry-Based Proteomics Data},

author = {Mariela González-Avendaño and Simon Zuñiga-Almonacid and Ian Silva and Boris Lavanderos and Felipe Robinson and Roberto Rosales-Rojas and Fabio Durán-Verdugo and Wendy González and Mónica Cáceres and Oscar Cerda and Ariela Vergara-Jaque},

doi = {10.3389/fmolb.2021.701477},

year  = {2021},

date = {2021-06-30},

urldate = {2021-06-30},

abstract = {Mass spectrometry-based proteomics methods are widely used to identify and quantify protein complexes involved in diverse biological processes. Specifically, tandem mass spectrometry methods represent an accurate and sensitive strategy for identifying protein-protein interactions. However, most of these approaches provide only lists of peptide fragments associated with a target protein, without performing further analyses to discriminate physical or functional protein-protein interactions. Here, we present the PPI-MASS web server, which provides an interactive analytics platform to identify protein-protein interactions with pharmacological potential by filtering a large protein set according to different biological features. Starting from a list of proteins detected by MS-based methods, PPI-MASS integrates an automatized pipeline to obtain information of each protein from freely accessible databases. The collected data include protein sequence, functional and structural properties, associated pathologies and drugs, as well as location and expression in human tissues. Based on this information, users can manipulate different filters in the web platform to identify candidate proteins to establish physical contacts with a target protein. Thus, our server offers a simple but powerful tool to detect novel protein-protein interactions, avoiding tedious and time-consuming data postprocessing. To test the web server, we employed the interactome of the TRPM4 and TMPRSS11a proteins as a use case. From these data, protein-protein interactions were identified, which have been validated through biochemical and bioinformatic studies. Accordingly, our web platform provides a comprehensive and complementary tool for identifying protein-protein complexes assisting the future design of associated therapies.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}