Abstract
Aquaporin-7 (AQP7), a key glycerol channel involved in the metabolic reprogramming of breast cancer, is a promising but challenging therapeutic target. The high structural conservation among aquaporins and a lack of data on its splice variants have hindered the development of isoform-specific inhibitors. To address this, we deployed an integrative computational strategy combining homology modelling, AI-based structure predictions, high-throughput virtual screening, and all-atom molecular dynamics (MD) simulations to decode the structural basis for AQP7 isoform inhibition. We characterized five AQP7 isoforms, revealing >90 % sequence similarity but critical structural variations that impact draggability. While some isoforms retained a complete, stable pore architecture, others were truncated and non-functional. Virtual screening of specialized aquaporin-focused libraries from Enamine and Life Chemicals identified two potent inhibitors i.e., Z225008686 demonstrates pan-isoform efficacy, and Z1594872812 displays isoform-specific binding. MD simulations revealed that Z225008686 induces exceptional pore constriction (<1.7 Å) through stable interactions with key residues viz., Gln183, Asn94, Phe74, and Val97, reducing water flux by ∼40 %. Free energy decomposition identified Arg106 and Phe74 as critical binding anchors. Notably, we uncovered a novel, ligand-induced gating mechanism in functional isoforms, where dynamic interactions with Phe74, Arg106, and Gln183 narrow the pore—presenting a new pharmacologically targetable checkpoint. This gating efficiency is governed by a precise balance between conformational flexibility and structural stability. Collectively, our study identifies specific lead compounds and reveals a new gating mechanism in isoforms of AQP7, offering a transformative strategy for treating breast cancers reliant on glycerol metabolism. Our current findings are based on computational predictions and need further experimental validation to confirm their biological relevance.
| Original language | English |
|---|---|
| Article number | 111336 |
| Journal | Computers in Biology and Medicine |
| Volume | 199 |
| DOIs | |
| Publication status | Published - 12-2025 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
All Science Journal Classification (ASJC) codes
- Health Informatics
- Computer Science Applications
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