Comprehensive mapping of cystic fibrosis mutations to CFTR protein identifies mutation clusters and molecular docking

Steven V Molinski1, Vijay M Shahani1, Adithya S Subramanian1

  • 1Cyclica Inc., Toronto, Ontario, M5C 1C4, Canada.

Proteins
|March 24, 2018
PubMed

Insights

Cystic Fibrosis (CF) mutations cluster at protein interfaces, revealing new drug targets. This study maps CFTR gene mutations onto protein structures to understand disease mechanisms and guide personalized medicine for CF patients.

Area of Science:

  • Genetics and Molecular Biology
  • Structural Biology
  • Pharmacogenomics

Background:

  • Cystic Fibrosis (CF) is caused by over 2000 mutations in the CFTR gene.
  • The distribution of these mutations across the CFTR protein's tertiary structure and their impact on function remain incompletely understood.
  • The precise binding site for Class I CFTR correctors is not well-defined.

Purpose of the Study:

  • To analyze the aggregate distribution of exonic CFTR mutations across CFTR protein structural models.
  • To identify mutation hotspots and understand their relationship to protein structure and function.
  • To elucidate the binding site of Class I correctors for CFTR.

Main Methods:

  • CFTR mutation data from three curated databases (>130,000 data points) were mapped onto homology and cryo-EM structural models of CFTR.
  • High-frequency mutant CFTR alleles were analyzed for spatial co-localization within the protein structure.
  • Immunoblotting and in silico molecular docking were used to investigate the binding site of Class I correctors.

Main Results:

  • Six high-frequency mutant CFTR alleles spatially co-localized, with a significant cluster identified at the NBD1:ICL4 interdomain interface.
  • Class I correctors (VX-809, VX-661, C18) demonstrated a similar in vitro binding mechanism.
  • Molecular docking suggested a putative corrector binding site within a multi-domain pocket near residues F374-L375.

Conclusions:

  • Interdomain interfaces of the CFTR protein are significant mutation hotspots.
  • A putative binding site for Class I correctors has been identified.
  • Structural pharmacogenomics, integrating mutation data with protein models, shows potential for CF drug discovery and personalized medicine.

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