Comparative QSAR studies on peptide deformylase inhibitors

Ji Young Lee, Munikumar Reddy Doddareddy, Yong Seo Cho, Hyunah Choo, Hun Yeong Koh, Jae Hoon Kang, Kyoung Tai No, Ae Nim Pae

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19 Citations (Scopus)


Comparative quantitative structure-activity relationship (QSAR) analyses of peptide deformylase (PDF) inhibitors were performed with a series of previously published (British Biotech Pharmaceuticals, Oxford, UK) reverse hydroxamate derivatives having antibacterial activity against Escherichia coli PDF, using 2D and 3D QSAR methods, comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), and hologram QSAR (HQSAR). Statistically reliable models with good predictive power were generated from all three methods (CoMFA r2=0.957, q2=0.569; CoMSIA r2=0.924, q2=0.520; HQSAR r2=0.860, q2=0.578). The predictive capability of these models was validated by a set of compounds that were not included in the training set. The models based on CoMFA and CoMSIA gave satisfactory predictive r2 values of 0.687 and 0.505, respectively. The model derived from the HQSAR method showed a low predictability of 0.178 for the test set. In this study, 3D prediction models showed better predictive power than 2D models for the test set. This might be because 3D information is more important in the case of datasets containing compounds with similar skeletons. Superimposition of CoMFA contour maps in the active site of the PDF crystal structure showed a meaningful correlation between receptor-ligand binding and biological activity. The final QSAR models, along with information gathered from 3D contour and 2D contribution maps, could be useful for the design of novel active inhibitors of PDF.

Original languageEnglish
Pages (from-to)543-558
Number of pages16
JournalJournal of Molecular Modeling
Issue number5
Publication statusPublished - 2007 May

All Science Journal Classification (ASJC) codes

  • Catalysis
  • Computer Science Applications
  • Physical and Theoretical Chemistry
  • Organic Chemistry
  • Computational Theory and Mathematics
  • Inorganic Chemistry


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