Design of novel amyloid β aggregation inhibitors using QSAR, pharmacophore modeling, molecular docking and ADME prediction
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.
KeywordsAlzheimer’s disease Curcuma longa 2D-QSAR 3D-QSAR Molecular docking
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.
- Dubey SK, Sharma AK, Narain U et al (2008) Design, synthesis and characterization of some bioactive conjugates of curcumin with glycine, glutamic acid, valine and demethylenated piperic acid and study of their antimicrobial and antiproliferative properties. Eur J Med Chem 43:1837–1846. https://doi.org/10.1016/j.ejmech.2007.11.027 CrossRefPubMedGoogle Scholar
- Garcia-Alloza M, Borrelli LA, Rozkalne A et al (2007) Curcumin labels amyloid pathology in vivo, disrupts existing plaques, and partially restores distorted neurites in an Alzheimer mouse model: curcumin reverses amyloid pathology in vivo. J Neurochem 102:1095–1104. https://doi.org/10.1111/j.1471-4159.2007.04613.x CrossRefPubMedGoogle Scholar
- Nguyen TKC, Dzung TTK, Cuong PV (2014) Assessment of antifungal activity of turmeric essential oil-loaded chitosan nanoparticles. J Chem Bio Phy Sci Sec B 4:2347–2356Google Scholar
- Selkoe DJ (1994) Cell biology of the amyloid beta-protein precursor and the mechanism of Alzheimer’s disease. Annu Rev Cell Biol 10:373–403. https://doi.org/10.1146/annurev.cb.10.110194.002105 CrossRefPubMedGoogle Scholar