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In Silico Pharmacology

, 6:12 | Cite as

Design of novel amyloid β aggregation inhibitors using QSAR, pharmacophore modeling, molecular docking and ADME prediction

  • Lilly Aswathy
  • Radhakrishnan S. Jisha
  • Vijay H. Masand
  • Jayant M. Gajbhiye
  • Indira G. Shibi
Original Research
  • 58 Downloads

Abstract

The inhibition of abnormal amyloid β (Aβ) aggregation has been regarded as a good target to control Alzheimer’s disease. The present study adopted 2D-QSAR, HQSAR and 3D QSAR (CoMFA & CoMSIA) modeling approaches to identify the structural and physicochemical requirements for the potential Aβ aggregation inhibition. A structure-based molecular docking technique is utilized to approve the features that are obtained from the ligand-based techniques on 30 curcumin derivatives. The combined outputs were then used to screen the modified 10 compounds. The 2D QSAR model on curcumin derivatives gave statistical values R2 = 0.9086 and SEE = 0.1837. The model was further confirmed by Y-randomization test and Applicability domain analysis by the standardization approach. The HQSAR study (Q2 = 0.615, R ncv 2  = 0.931, R pred 2  = 0.956) illustrated the important molecular fingerprints for inhibition. Contour maps of 3D QSAR models, CoMFA (Q2 = 0.687, R ncv 2  = 0.787, R pred 2  = 0.731) and CoMSIA (Q2 = 0.743, R ncv 2  = 0.972, R pred 2  = 0.713), depict that the models are robust and provide explanation of the important features, like steric, electrostatic and hydrogen bond acceptor, which play important role for interaction with the receptor site cavity. The molecular docking study of the curcumin derivatives elucidates the important interactions between the amino acid residues at the catalytic site of the receptor and the ligands, indicating the structural requirements of the inhibitors. The ligand–receptor interactions of top hits were analyzed to explore the pharmacophore features of Aβ aggregation inhibition. The Aβ aggregation inhibitory activities of novel chemical entities were then obtained through inverse QSAR. The newly designed molecules were further screened through machine learning, prediction of toxicity and nature of metabolism to get the proposed six lead compounds.

Keywords

Alzheimer’s disease Curcuma longa 2D-QSAR 3D-QSAR Molecular docking 

Notes

Acknowledgements

Aswathy L. is thankful to CSIR, New Delhi for the financial assistance in the form of Senior Research Fellowship. Jisha, R.S. is thankful to the University of Kerala, Thiruvananthapuram for providing financial assistance in the form of University Junior Research Fellowship for this work.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Lilly Aswathy
    • 1
  • Radhakrishnan S. Jisha
    • 1
  • Vijay H. Masand
    • 2
  • Jayant M. Gajbhiye
    • 3
  • Indira G. Shibi
    • 1
  1. 1.Department of ChemistrySree Narayana CollegeThiruvananthapuramIndia
  2. 2.Department of ChemistryVidya Bharati CollegeAmravatiIndia
  3. 3.Division of Organic ChemistryCSIR-National Chemical LaboratoryPuneIndia

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