Medicinal Chemistry Research

, Volume 23, Issue 5, pp 2445–2455 | Cite as

Virtual screening based on pharmacophoric features of known calpain inhibitors to identify potent inhibitors of calpain

  • Arumugam Ramachandran Muralidharan
  • Chandrabose Selvaraj
  • Sanjeev Kumar Singh
  • C. A. Nelson Jesudasan
  • Pitchairaj Geraldine
  • Philip A. Thomas
Original Research


Calpain, a member of the group of cysteine protease enzymes, has been recognized as a promising drug target for several diseases, including cataract. In the present study, an attempt was made to identify potential inhibitors of calpain by employing a pharmacophore-based virtual screening and docking approach. A knowledge-based 3D pharmacophore model was generated, based on the features of established calpain inhibitors SJA6017, MDL28170, E64D, SNJ 1715, calpastatin and CHEMBL 1921830, using the PHASE module of Schrödinger Suite. The best pharmacophore model (AAADH) derived consisted of five features, namely three hydrogen bond acceptors, one hydrogen bond donor and one hydrophobic region. This common pharmacophore hypothesis was then used to perform virtual screening against a binding database, with due consideration to the Lipinski ‘rule of five’ and absorption, distribution, metabolism, excretion properties were calculated using the Qikprop module, so as to obtain a pool of lead molecules. The short-listed lead molecules were then subjected to docking analysis with that of the mutated calpain 1 (1KXR) to reduce the false positive and false negative results against the target receptor. Interaction data and its corresponding interaction energy, along with binding energy calculated for the hit ligand (650709) and mutated receptor (1KXR) complex, suggest that compound 650709 has a more effective inhibitory potential than that of the other established calpain inhibitors.


Calpain inhibitor Cataract Pharmacophore model Virtual screening Molecular docking 



Financial assistance provided by Council of Scientific & Industrial Research (CSIR), New Delhi in the form of Senior Research Fellowship to the first author is gratefully acknowledged (Ref. No. 09/475 (0185)/2012-EMR-I). Financial assistance provided by University Grants Commission-Basic Scientific Research (UGC-BSR) in the form of one time grant to the corresponding author is gratefully acknowledged. The authors Sanjeev Kumar Singh and Chandrabose Selvaraj also gratefully acknowledge CSIR for the funding grant (Ref. No. 37(1491)/11/EMR-II).

Conflict of interest

The authors declare that there are no conflicts of interest.

Supplementary material

44_2013_842_MOESM1_ESM.pdf (108 kb)
Supplementary material 1 (PDF 108 kb)
44_2013_842_MOESM2_ESM.pdf (128 kb)
Supplementary Fig. 1 Comparative molecular dynamics analysis of native and mutant 1KXR protein. RMSD values of the native (black line) and mutant 1KXR (red line) systems versus simulation time (10,000 ns). X and Y axes represent Time (ns), and RMSD, respectively
44_2013_842_MOESM3_ESM.pdf (118 kb)
Supplementary Fig. 2 RMSF representations of native and mutant 1KXR in the dynamic state. Here black and red represent the native and the mutant. respectively


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Arumugam Ramachandran Muralidharan
    • 1
  • Chandrabose Selvaraj
    • 2
  • Sanjeev Kumar Singh
    • 2
  • C. A. Nelson Jesudasan
    • 3
  • Pitchairaj Geraldine
    • 1
  • Philip A. Thomas
    • 3
  1. 1.Department of Animal Science, School of Life SciencesBharathidasan UniversityTiruchirappalliIndia
  2. 2.Computer-Aided Drug Designing and Molecular Modeling Lab, Department of BioinformaticsAlagappa UniversityKaraikudiIndia
  3. 3.Institute of OphthalmologyJoseph Eye HospitalTiruchirappalliIndia

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