Structure-guided identification of 2-arylbenzothiazoles as disruptors of the oncogenic MTDH-SND1 protein-protein interaction.

The protein-protein interaction (PPI) between metadherin (MTDH) and staphylococcal nuclease and tudor domain-containing 1 (SND1) drives oncogenic signaling and tumor progression in multiple cancer types, yet remains an underexploited therapeutic target. Here, we report an integrated computational-experimental strategy to identify small-molecule disruptors of the MTDH-SND1 interface. A focused PubChem library of 4149 2-arylbenzothiazoles was screened using a tiered workflow combining drug-likeness/PAINS filtering, hierarchical docking into the MTDH-binding groove on SND1 (PDB 4QMG), and MM/PBSA-based refinement. Top-ranked candidates were further evaluated by 1-μs molecular dynamics (MD) simulations and prioritized based on pose stability and interfacial engagement metrics. Four representative hits-L1 (2-(4-aminophenyl)benzothiazole), L2 (2-(4-amino-3-methylphenyl)-5-fluorobenzothiazole), L3 (YL-109), and L4 (2-(3,4,5-trimethoxyphenyl)benzothiazole)-were advanced for experimental validation. MD analyses indicated ligand-dependent modulation of PPI stability and conformational landscapes, while MM/PBSA decomposition suggested binding was driven predominantly by short-range hydrophobic packing within the interfacial groove. A quantitative split-luciferase complementation assay confirmed dose-dependent disruption of MTDH-SND1 in a cell-free format with low-micromolar potency (IC50: L1 10.72 ± 1.12 μM, L2 5.72 ± 0.92 μM, L3 11.77 ± 1.42 μM, L4 7.41 ± 1.01 μM) and in a cell-based reporter (IC50: L1 35.71 ± 2.80 μM, L2 16.44 ± 0.24 μM, L3 32.96 ± 2.58 μM, L4 21.93 ± 0.9 μM). Importantly, a linked-luciferase counterscreen (IC50 > 1000 μM) supported minimal luciferase interference. MST analysis further confirmed direct binding of L1-L4 to purified SND1, yielding low-micromolar KD values. Together, these results establish 2-arylbenzothiazoles as a promising chemotype for MTDH-SND1 PPI inhibition and provide a validated computational-to-experimental framework for discovering and optimizing MTDH-directed therapeutics.