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DNA-encoded library versus RNA-encoded library selection enables design of an oncogenic noncoding RNA inhibitor

Nature evolves molecular interaction networks through persistent perturbation and selection, in stark contrast to drug discovery, which evaluates candidates one at a time by screening. Here, nature’s highly parallel ligand-target search paradigm is recapitulated in a screen of a DNA-encoded library...

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Detalles Bibliográficos
Autores principales: Benhamou, Raphael I., Suresh, Blessy M., Tong, Yuquan, Cochrane, Wesley G., Cavett, Valerie, Vezina-Dawod, Simon, Abegg, Daniel, Childs-Disney, Jessica L., Adibekian, Alexander, Paegel, Brian M., Disney, Matthew D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: National Academy of Sciences 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8833215/
https://www.ncbi.nlm.nih.gov/pubmed/35110406
http://dx.doi.org/10.1073/pnas.2114971119
Descripción
Sumario:Nature evolves molecular interaction networks through persistent perturbation and selection, in stark contrast to drug discovery, which evaluates candidates one at a time by screening. Here, nature’s highly parallel ligand-target search paradigm is recapitulated in a screen of a DNA-encoded library (DEL; 73,728 ligands) against a library of RNA structures (4,096 targets). In total, the screen evaluated ∼300 million interactions and identified numerous bona fide ligand–RNA three-dimensional fold target pairs. One of the discovered ligands bound a 5′GAG/3′CCC internal loop that is present in primary microRNA-27a (pri-miR-27a), the oncogenic precursor of microRNA-27a. The DEL-derived pri-miR-27a ligand was cell active, potently and selectively inhibiting pri-miR-27a processing to reprogram gene expression and halt an otherwise invasive phenotype in triple-negative breast cancer cells. By exploiting evolutionary principles at the earliest stages of drug discovery, it is possible to identify high-affinity and selective target–ligand interactions and predict engagements in cells that short circuit disease pathways in preclinical disease models.