Journal of Molecular Modeling

, Volume 15, Issue 11, pp 1337–1347 | Cite as

Developing novel approaches to improve binding energy estimation and virtual screening: a PARP case study

  • Fedor N. Novikov
  • Viktor S. Stroylov
  • Oleg V. Stroganov
  • Val Kulkov
  • Ghermes G. Chilov
Original Paper


Poly-(ADP-ribose)-polymerase (PARP) is a promising anti-cancer target as it plays a crucial role in the cellular reparation and survival mechanisms. However, the development of a robust and cost effective experimental technique to screen PARP inhibitors is still a scientific challenge owing to the difficulties in quantitative detection of the enzyme activity. In this work we demonstrate that the computational chemistry tools including molecular docking and scoring can perform on par with the experimental studies in assessing binding constants and in the recovery of active compounds in virtual screening. Using the recently introduced Lead Finder software we were able to dock a set of 142 well characterized PARP inhibitors and obtain a good correlation between the calculated and experimentally measured binding energies with the rmsd of 1.67 kcal mol−1. Additionally, fine-tuning of the energy scaling coefficients within the Lead Finder scoring function has further decreased rmsd to the value of 0.88 kcal mol−1. Moreover, we were able to reproduce the selectivity of ligand binding between the two isoforms of the enzyme-PARP1 and PARP2-suggesting that the Lead Finder software can be used to design isoform-selective inhibitors of PARP. An impressive enrichment was obtained in the virtual screening experiment, in which the mentioned set of PARP inhibitors was mixed with a commercial library of 300,000 compounds. We also demonstrate that the virtual screening performance can be significantly improved by an additional structural filtration of the docked ligand poses through detection of the crucial hydrogen bonding interactions with the enzyme.


Binding energy calculations PARP1 PARP2 Protein-ligand docking Selectivity modeling Virtual screening 



Our special thanks to Prof. Viktor Gergel and Alexander Grishagin, the Center of Supercomputer Technologies of the N.I. Lobacevsky State University of Nizhni Novgorod, for our access to the high-performance computing cluster (Nizhni Novgorod segment of the SKIF-grid program).

Supplementary material

894_2009_497_MOESM1_ESM.doc (548 kb)
ESM 1 (DOC 548 kb)


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Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • Fedor N. Novikov
    • 1
    • 2
  • Viktor S. Stroylov
    • 2
  • Oleg V. Stroganov
    • 1
    • 3
  • Val Kulkov
    • 4
  • Ghermes G. Chilov
    • 1
    • 3
  1. 1.MolTech LtdMoscowRussia
  2. 2.Department of Bioengineering and BioinformaticsLomonosov Moscow State UniversityMoscowRussia
  3. 3.Belozersky Institute of Physicochemical BiologyLomonosov Moscow State UniversityMoscowRussia
  4. 4.BioMolTech CorpTorontoCanada

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