RSC Medicinal ChemistryBiochemistryPeer-reviewed journal article
Discovery of a First-in-Class SLIT2 Binder Disrupting the SLIT2/ROBO1 Axis via DNA-Encoded Library (DEL) Screening
Shaoren Yuan, Somaya A. Abdel‐Rahman, Nelson García-Vázquez, Hossam NadaiD, Laura Calvo‐BarreiroiD, Katarzyna KuncewicziD, Moustafa Tarek GabriD
Abstract
The SLIT2/ROBO1 signaling axis plays a critical role in neural development, immune regulation, and tumor progression, including glioblastoma. However, small molecule inhibitors targeting this protein-protein interaction remain unexplored. Herein, we report the discovery and validation of DEL-S1, a first-in-class small molecule that binds to SLIT2 and disrupts its interaction with ROBO1. Using a DNA-encoded library (DEL) screen of 4.2 billion compounds, DEL-S1 was identified and confirmed to bind SLIT2 via temperature-related intensity change (TRIC) assay. Functional inhibition of the SLIT2/ROBO1 complex by DEL-S1 was demonstrated using a Time-Resolved Fluorescence Resonance Energy Transfer (TR-FRET) assay, yielding an IC50 of 68.8 {+/-} 12.5 {micro}M. Molecular docking and molecular dynamics (MD) simulations revealed key interaction hotspots at the SLIT2 binding interface and confirmed that DEL-S1 impairs SLIT2/ROBO1 complex formation by inducing conformational rearrangements. DEL-S1 exhibited favorable ADME properties, including satisfactory plasma and microsomal stability, low cytotoxicity, and minimal hERG liability. To facilitate structure-activity relationship (SAR) exploration, we designed and implemented a modular, one-pot synthetic route leveraging cyanuric chloride reactivity, enabling rapid derivatization of the triazine scaffold of DEL-S1. This strategy yielded structurally diverse analogs, including water-soluble carboxylate derivatives with preserved SLIT2/ROBO1 inhibitory activity. Together, this work establishes a novel chemical scaffold targeting SLIT2 and introduces a flexible synthetic platform to support further optimization toward therapeutic development.
- organism: human
- assay: proteomics
- research focus: drug response / drug discovery
- approach: deep learning
- approach: statistical model
- Citations
- 3
- FWCI
- 0.90
- Field percentile
- top 28%
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