Ligand-based computational modelling of platelet-derived growth factor beta receptor leading to new angiogenesis inhibitory leads

Rua'a A. Al-Aqtash, Malek A. Zihlif, Hana Hammad, Zeyad D. Nassar, Jehad Al Meliti, Mutasem O. Taha

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

Platelet derived growth factor beta receptor (PDGFR- β) plays an important role in angiogenesis. PDGFR-β expression is correlated with increased vascularity and maturation of blood vessels in cancer. Pharmacophore modeling and quantitative structure-activity relationship (QSAR) analysis were combined to explore the structural requirements for ligand-PDGFR-β recognition using 107 known PDGFR-β inhibitors. Genetic function algorithm (GFA) coupled to k nearest neighbor (kNN) and multiple linear regression (MLR) analysis were employed to generate predictive QSAR models based on optimal combinations of pharmacophores and physicochemical descriptors. The successful pharmacophores were complemented with exclusion spheres to optimize their receiver operating characteristic curve (ROC) profiles. The QSAR models and their associated pharmacophore hypotheses were validated by identification and experimental evaluation of new angiogenesis inhibitory leads retrieved from the National Cancer Institute (NCI) structural database. Two hits illustrated low micromolar IC50 values in two distinct anti-angiogenesis bioassays.

Original languageEnglish
Pages (from-to)170-179
Number of pages10
JournalComputational Biology and Chemistry
Volume71
DOIs
Publication statusPublished or Issued - Dec 2017
Externally publishedYes

Keywords

  • Angiogenesis
  • PANC-1
  • PDGFR-β
  • Pharmacophore
  • QSAR
  • Virtual screening

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Organic Chemistry
  • Computational Mathematics

Cite this