Zaka, M. | bioRxiv Science

bioRxiv2026

Ion Channel Nano-Diagnostics for ER+ Breast Cancer

Ion channels are pore-forming transmembrane proteins that allow ions to move down an electrochemical gradient and across the channel pore and regulate many cell functions. Among them, are the G-protein-gated inwardly-rectifying K+ channels 1 (GIRK1) that are ubiquitously expressed with major functions in the brain and heart. Interestingly, significantly higher GIRK1 expression has been found in estrogen receptor positive (ER+) breast cancer patients compared to patients with HER2+ tumors or normal patients, and that was statistically correlated with shorter survival times and metastatic potential. Herein, we report the preparation of [~]4 nm GAT1508-coated poly(ethylene glycol) gold nanoparticle (PEGylated AuNP) biomarker for ER+ breast cancer cell screening through an optical microscope. A urea-based small molecule, GAT1508, with an N-methylpyrazole benzyl group on one side and a bromo-thiophene tail on the other side, has been shown to predominantly bind GIRK1 subunits and specifically activate GIRK1/2 channels. Two derivatives of GAT1508were synthesized and characterized: an ethylamine derivative (GAT1508-EA) with a chain extension from the benzyl ring, and a propylamine derivative (GAT1508-PA) with a chain extension from the pyrazole ring. Electrophysiology (TEVC and whole-cell patch-clump) experiments as well as fluorescence studies (Thallium assay) showed that only GAT1508-PA inhibited GIRK1/2-mediated K+ currents in transfected HEK293GIRK1 cells. Docking studies showed strong binding for the propylamine GAT1508 derivative, both in the amine form (GAT1508-PA) as well as in the amide form (GAT1508-PA-EG2; coupled with PEG as in the AuNPs). GAT1508-PEG-AuNPs (GAT1508-NPs) were synthesized subsequently with [~]65 wt% metal loading. UV-Vis studies revealed the presence of the conjugated ligand at 260 nm. Flow cytometry studies showed binding of Alexa 594-labeled GAT1508-NPs in ER+ MCF-7 breast cancer cells with a strong interaction, while incubation of fixed MCF-7 cells with a GAT1508-NP solution led to optical detection of ER+ breast cancer cells, without the need of fluorescent dyes and additional amplification steps. Detection was not feasible in MDA-MB-231 cells, a triple (-) breast cell line that does not express GIRK1. This is the first study, to our knowledge, that couples nanotechnology with small molecule drug design and electrophysiology to develop ion channel-tracing molecular probes for the detection/screening of ER+ breast cancer.

bioengineering↗

bioRxiv2025

Orthosteric interactions with PIP2 activate TMEM16A channels

TMEM16A channels pass Ca2+-activated Cl- currents that drive a plethora of fundamental physiological processes. TMEM16A channels are activated by a rise in intracellular Ca2+ levels but also require interactions with the signaling phospholipid phosphatidylinositol 4,5-bisphosphate (PIP2) to gate open. Although PIP2 is essential for the activity of many types of ion channels, its precise binding site and role in channel gating remain poorly understood in most cases, limiting efforts to study channel dynamics and design targeted modulators. In this study, we identify the PIP2 binding interactions that govern TMEM16A gating and permeation. Using a combination of gating molecular dynamics (GMD) simulations and electrophysiological assays, we reveal how the 4 helix of TMEM16A interacts with both the phosphate headgroups and acyl chains of PIP2 to open an electrostatic ring within the channel and stabilize the extracellular opening of the Cl- conduction pathway. These findings provide key insights into the dynamic role of PIP2 in membrane protein function and shed light on the activation mechanism of TMEM16A. This work establishes a framework for rational targeting of TMEM16A in drug development, with potential therapeutic applications in a variety of diseases.

biophysics↗

bioRxiv2024

Development of Machine Learning-based QSAR Models for the Designing of Novel Anti-cancer Therapeutics Against Malignant Glioma

In the early drug design and discovery phase, virtual screening of diverse small molecule libraries is crucial. Machine learning (ML)-based algorithms have made this process easier and faster. In this study, we have applied ML-based algorithms to generate the QSAR models for virtual screening. The aim of study is to design the statistically significant models for the screening of small molecule libraries to identify the novel hits against IDH1 mutant receptor crucial for glioblastoma multiforme (GBM). To construct the models, we have used both cell lines data (U87 and U251 cells) and the inhibitors of IDH1 mutant reported in the literature and used the pIC50 activity data to train our models. Furthermore, ligand-based 3D QSAR models and structure-based pharmacophore models were also constructed and validated.

bioinformatics↗