4.6 Article

Speeding Up the Identification of Cystic Fibrosis Transmembrane Conductance Regulator-Targeted Drugs: An Approach Based on Bioinformatics Strategies and Surface Plasmon Resonance

Journal

MOLECULES
Volume 23, Issue 1, Pages -

Publisher

MDPI
DOI: 10.3390/molecules23010120

Keywords

cystic fibrosis; computational chemistry; molecular dynamics; molecular modeling; surface plasmon resonance

Funding

  1. Fondazione Italiana Fibrosi Cistica, FFC [6/2014]
  2. Fondazione Italiana Fibrosi Cistica, FCC [7/2015]
  3. Italian Ministry of Education, University and Research (MIUR) [PB05, 20157ATSLF]
  4. Regione Lombardia-CNR [FRRB LYRA_2015-0010]
  5. Omics4CF CNR project
  6. Fondazione Ricerca Fibrosi Cistica

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Cystic fibrosis (CF) is mainly caused by the deletion of Phe 508 (F508) in the cystic fibrosis transmembrane conductance regulator (CFTR) protein that is thus withheld in the endoplasmic reticulum and rapidly degraded by the ubiquitin/proteasome system. New drugs able to rescue F508-CFTR trafficking are eagerly awaited. An integrated bioinformatics and surface plasmon resonance (SPR) approach was here applied to investigate the rescue mechanism(s) of a series of CFTR-ligands including VX809, VX770 and some aminoarylthiazole derivatives (AAT). Computational studies tentatively identified a large binding pocket in the F508-CFTR nucleotide binding domain-1 (NBD1) and predicted all the tested compounds to bind to three sub-regions of this main pocket. Noticeably, the known CFTR chaperone keratin-8 (K8) seems to interact with some residues located in one of these sub-pockets, potentially interfering with the binding of some ligands. SPR results corroborated all these computational findings. Moreover, for all the considered ligands, a statistically significant correlation was determined between their binding capability to F508-NBD1 measured by SPR and the pockets availability measured by computational studies. Taken together, these results demonstrate a strong agreement between the in silico prediction and the SPR-generated binding data, suggesting a path to speed up the identification of new drugs for the treatment of cystic fibrosis.

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