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Combinatorial Virtual Screening Revealed a Novel Scaffold for TNKS Inhibition to Combat Colorectal Cancer.

ABSTRACT: The abnormal Wnt signaling pathway leads to a high expression of β-catenin, which causes several types of cancer, particularly colorectal cancer (CRC). The inhibition of tankyrase (TNKS) activity can reduce cancer cell growth, invasion, and resistance to treatment by blocking the Wnt signaling pathway. A pharmacophore search and pharmacophore docking were performed to identify potential TNKS inhibitors in the training databases. The weighted MM/PBSA binding free energy of the docking model was calculated to rank the databases. The reranked results indicated that 26.98% of TNKS inhibitors that were present in the top 5% of compounds in the database and near an ideal value ranked 28.57%. The National Cancer Institute database was selected for formal virtual screening, and 11 potential TNKS inhibitors were identified. An enzyme-based experiment was performed to demonstrate that of the 11 potential TNKS inhibitors, NSC295092 and NSC319963 had the most potential. Finally, Wnt pathway analysis was performed through a cell-based assay, which indicated that NSC319963 is the most likely TNKS inhibitor (pIC₅₀ = 5.59). The antiproliferation assay demonstrated that NSC319963 can decrease colorectal cancer cell growth; therefore, the proposed method successfully identified a novel TNKS inhibitor that can alleviate CRC.

SUBMITTER: Chang CC

PROVIDER: S-EPMC8773749 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Transforming growth factor-beta (TGF-β), a member of the TGF-β cytokine superfamily, is known to bind to sulfated glycosaminoglycans (GAGs), but the nature of this interaction remains unclear. In a recent study, we found that preterm human milk TGF-β2 is sequestered by chondroitin sulfate (CS) in its proteoglycan form. To understand the molecular basis of the TGF-β2-CS interaction, we utilized the computational combinatorial virtual library screening (CVLS) approach in tandem with molecular dynamics (MD) simulations. All possible CS oligosaccharides were generated in a combinatorial manner to give 24 di- (CS02), 192 tetra- (CS04), and 1536 hexa- (CS06) saccharides. This library of 1752 CS oligosaccharides was first screened against TGF-β2 using the dual filter CVLS algorithm in which the GOLDScore and root-mean-square-difference (RMSD) between the best bound poses were used as surrogate markers for in silico affinity and in silico specificity. CVLS predicted that both the chain length and level of sulfation are critical for the high affinity and high specificity recognition of TGF-β2. Interestingly, CVLS led to identification of two distinct sites of GAG binding on TGF-β2. CVLS also deduced the preferred composition of the high specificity hexasaccharides, which were further assessed in all-atom explicit solvent MD simulations. The MD results confirmed that both sites of binding form stable GAG-protein complexes. More specifically, the highly selective CS chains were found to engage the TGF-β2 monomer with high affinity. Overall, this work present key principles of recognition with regard to the TGF-β2-CS system. In the process, it led to the generation of the in silico library of all possible CS oligosaccharides, which can be used for advanced studies on other protein-CS systems. Finally, the study led to the identification of unique CS sequences that are predicted to selectively recognize TGF-β2 and may out-compete common natural CS biopolymers.

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Combinatorial Virtual Screening Revealed a Novel Scaffold for TNKS Inhibition to Combat Colorectal Cancer.

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