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A combined fragment-based virtual screening and STD-NMR approach for the identification of E-cadherin ligands.

ABSTRACT: Cadherins promote cell-cell adhesion by forming homophilic interactions via their N-terminal extracellular domains. Hence, they have broad-ranging physiological effects on tissue organization and homeostasis. When dysregulated, cadherins contribute to different aspects of cancer progression and metastasis; therefore, targeting the cadherin adhesive interface with small-molecule antagonists is expected to have potential therapeutic and diagnostic value. Here, we used molecular docking simulations to evaluate the propensity of three different libraries of commercially available drug-like fragments (nearly 18,000 compounds) to accommodate into the Trp2 binding pocket of E-cadherin, a crucial site for the orchestration of the protein's dimerization mechanism. Top-ranked fragments featuring five different aromatic chemotypes were expanded by means of a similarity search on the PubChem database (Tanimoto index >90%). Of this set, seven fragments containing an aromatic scaffold linked to an aliphatic chain bearing at least one amine group were finally selected for further analysis. Ligand-based NMR data (Saturation Transfer Difference, STD) and molecular dynamics simulations suggest that these fragments can bind E-cadherin mostly through their aromatic moiety, while their aliphatic portions may also diversely engage with the mobile regions of the binding site. A tetrahydro-β-carboline scaffold functionalized with an ethylamine emerged as the most promising fragment.

SUBMITTER: Vasile F

PROVIDER: S-EPMC9437437 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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A combined fragment-based virtual screening and STD-NMR approach for the identification of E-cadherin ligands.

Vasile Francesca F Lavore Francesca F Gazzola Silvia S Vettraino Chiara C Parisini Emilio E Piarulli Umberto U Belvisi Laura L Civera Monica M

Frontiers in chemistry 20220819

Cadherins promote cell-cell adhesion by forming homophilic interactions <i>via</i> their N-terminal extracellular domains. Hence, they have broad-ranging physiological effects on tissue organization and homeostasis. When dysregulated, cadherins contribute to different aspects of cancer progression and metastasis; therefore, targeting the cadherin adhesive interface with small-molecule antagonists is expected to have potential therapeutic and diagnostic value. Here, we used molecular docking simu ...[more]

PMID: 36059878

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Project description:To reduce time and cost, virtual ligand screening (VLS) often precedes experimental ligand screening in modern drug discovery. Traditionally, high-resolution structure-based docking approaches rely on experimental structures, while ligand-based approaches need known binders to the target protein and only explore their nearby chemical space. In contrast, our structure-based FINDSITEcomb2.0 approach takes advantage of predicted, low-resolution structures and information from ligands that bind distantly related proteins whose binding sites are similar to the target protein. Using a boosted tree regression machine learning framework, we significantly improved FINDSITEcomb2.0 by integrating ligand fragment scores as encoded by molecular fingerprints with the global ligand similarity scores of FINDSITEcomb2.0. The new approach, FRAGSITE, exploits our observation that ligand fragments, e.g., rings, tend to interact with stereochemically conserved protein subpockets that also occur in evolutionarily unrelated proteins. FRAGSITE was benchmarked on the 102 protein DUD-E set, where any template protein whose sequence identify >30% to the target was excluded. Within the top 100 ranked molecules, FRAGSITE improves VLS precision and recall by 14.3 and 18.5%, respectively, relative to FINDSITEcomb2.0. Moreover, the mean top 1% enrichment factor increases from 25.2 to 30.2. On average, both outperform state-of-the-art deep learning-based methods such as AtomNet. On the more challenging unbiased set LIT-PCBA, FRAGSITE also shows better performance than ligand similarity-based and docking approaches such as two-dimensional ECFP4 and Surflex-Dock v.3066. On a subset of 23 targets from DEKOIS 2.0, FRAGSITE shows much better performance than the boosted tree regression-based, vScreenML scoring function. Experimental testing of FRAGSITE's predictions shows that it has more hits and covers a more diverse region of chemical space than FINDSITEcomb2.0. For the two proteins that were experimentally tested, DHFR, a well-studied protein that catalyzes the conversion of dihydrofolate to tetrahydrofolate, and the kinase ACVR1, FRAGSITE identified new small-molecule nanomolar binders. Interestingly, one new binder of DHFR is a kinase inhibitor predicted to bind in a new subpocket. For ACVR1, FRAGSITE identified new molecules that have diverse scaffolds and estimated nanomolar to micromolar affinities. Thus, FRAGSITE shows significant improvement over prior state-of-the-art ligand virtual screening approaches. A web server is freely available for academic users at http:/sites.gatech.edu/cssb/FRAGSITE.

Dataset Information

A combined fragment-based virtual screening and STD-NMR approach for the identification of E-cadherin ligands.

Publications

A combined fragment-based virtual screening and STD-NMR approach for the identification of E-cadherin ligands.

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