Archives of Biochemistry and BiophysicsBiophysicsPeer-reviewed journal article
First-in-Class Small Molecule ROBO2 Binders Identified through Integrated Virtual Screening and Biophysical Validation
Abstract
Roundabout homolog 2 (ROBO2) is a transmembrane receptor implicated in glioblastoma progression through its interaction with Slit2-mediated signaling pathways. Dysregulated Slit2-ROBO2 signaling enhances tumor cell migration, invasion, and tissue infiltration, while elevated ROBO2 levels contribute to an immunosuppressive tumor microenvironment supporting GBM aggressiveness and highlighting ROBO2 as a therapeutic target. Despite its therapeutic relevance, no ROBO2-targeted small molecules have been reported. To address this gap, we performed a structure-based virtual screening campaign targeting ROBO2, followed by experimental validation with Dianthus TRIC platform and microscale thermophoresis (MST). Fifteen compounds were screened for ROBO2 binding, from which four candidates exhibited robust TRIC signals. Subsequent affinity measurements revealed that two small molecules, Z1334432986 and Z1692774161, bind ROBO2 in a reproducible concentration-dependent manner, with dissociation constants (Kd) of 40.8{+/-}4.8 M and 25.8{+/-}16.95 M, respectively. Molecular docking with validated hits revealed a shared ROBO2 binding pocket defined by conserved anchor residues (ASN354, SER366, ASP385) and accommodation of distinct ligand conformations within the ROBO2 binding pocket. This work establishes a screening pipeline for identifying ROBO2-targeted small molecules and lays the foundation for developing therapeutics aimed at disrupting Slit-ROBO2 signaling in GBM. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/697640v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@2fa804org.highwire.dtl.DTLVardef@bfa3d5org.highwire.dtl.DTLVardef@19d1522org.highwire.dtl.DTLVardef@1d6774a_HPS_FORMAT_FIGEXP M_FIG C_FIG
- organism: human
- assay: proteomics
- research focus: drug response / drug discovery
- approach: deep learning
- approach: experimental assay
- Citations
- 1
- FWCI
- 3.67
- Field percentile
- top 7%
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