Discovery Of Novel Casein Kinase-1Delta Inhibitors By ... ... Discovery of Novel Casein...
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Discovery of Novel Casein Kinase-1δ Inhibitors by
Structure Based Virtual Screening, ADMET Analysis and
Molecular Dynamics Simulation to Target Alzheimer's
Bhanwar Singh Choudhary* 1, Ruchi Malik1, Shubham Srivastava1
1Department of Pharmacy, Central University of Rajasthan, Ajmer, India
Bhanwar Singh Choudhary (CSIR-SRF),
Dr Ruchi Malik’s Research Group
Department of Pharmacy,
Central University of Rajasthan, Ajmer,
Rajasthan, India - 305817
• Alzheimer’s disease (AD) is a growing public health problem among the elderly.
According to WHO, the number of affected people aged 65 or older were projected
to grow from an estimated 524 million in 2010 to nearly 1.5 billion by 2050, with
developing countries affected the most.
• CK1δ is a potential target for AD, over activity of CK1δ may be regulated by an
inhibitor without affecting its basic activity in cells.
• CK1δ directly phosphorylated tubulin binding site of tau protein, which indicate its
importance in tau aggregation. CK1δ and GSK-3 phosphorylate at least 15 sites in
the paired helicoidal filaments (PHF) of tau in Alzheimer brain.
Figure 1: Mechanism of neurofibrillary tangles formation in Alzheimer’s disease and
promising drug target site
CK-1 δ Inhibitors
• Virtual screening: The protocol encompasses standard protocols provided by
Schrodinger for virtual screening of large databases using HTVS, SP, XP and MM-
• ADMET: QikProp was used for ADME prediction was performed and reported for
screened compounds. ADMET predictor version 7.1 was used to calculate toxicity
for best scoring inhibitors obtained from MM-GBSA study and PF-4800567.
• Molecular Dynamics: Full-atom MD simulations were performed for each system
by using Desmond version 2014.2 (Maestro, version 10.4).
6 Figure 2: Graphical representation of methodology
Validation of Protein
Enrichment calculation: Enrichment based ROC graph of the docking results was
plotted between true positive rate (sensitivity) and false positive rate (1-specificity).
ROC graph reflects the probability of a higher rank of randomly selected active
Table 1: Values of ROC and BEDROC calculated for SP docking results
S No. PDB ID ROC BEDROC (α 160.9)
1 4HNF 0.74 0.201
2 3UYT 0.65 0.205
Result and Discussion
Validation of docking protocol
Docking protocol was validated by structure guided binding pattern overlap of the
co-crystallized and docked molecule i.e. PF4800567. The RMSD was found to be
1.7358 Å (figure 3).
Figure 3: Superimposed co-crystallized (green) and docked (grey) PF4800567.
Table 1: MM-GBSA dG score, dock score and interacting residues of best hits.
S. No. Zinc ID MM-GBSA Score Dock Score Interacting Residues
1 PF4800567 -95.639 -10.444 Glu83, Leu85
2 ZINC09036109 -115.578 -11.954 Leu85, Asp149
3 ZINC72371723 -109.652 -12.415 Lys38, Tyr56, Leu85
4 ZINC72371725 -109.16 -12.321 Lys38, Tyr56, Leu85
5 ZINC19698731 -108.93 -11.611 Glu83, Leu85
6 ZINC01373165 -107.224 -12.258 Leu85
Po/w logS Pcaco
Kp Metab HOA
of 5 MDCK
Range 13/70 -2.5
/1.2 -8/-1 1/8
4 25 />500
1 PF4800567 36.14 3.3 -4.8 1351.8 -0.4 -2.0 4 3 0 1689.7
2 ZINC09036109 52.9 6.7 -8.1 1693.5 -0.7 -0.9 6 1 1 1244.8
3 ZINC72371723 48.4 5.6 -6.5 583.9 0.1 -2.8 6 1 1 746.4
4 ZINC72371725 48.3 5.6 -6.5 581.7 0.1 -2.8 6 1 1 751.8
5 ZINC19698731 44.4 1.3 -2.6 40.3 -1.4 -3.4 6 2 0 223.2
6 ZINC01373165 48.0 3.7 -4.0 110.7 -0.9 -4.1 8 3 0 50.7
Table 2: ADME properties prediction of CK1δ inhibitors using QikProp
In-silico ADMET Studies
S. No Molecule Name hERG PHOS AlkPos GGT LDH SGOT SGPT
1 PF4800567 T NT T NT NT T NT
2 ZINC09036109 NT NT NT NT T T T
3 ZINC72371723 T NT T NT NT NT NT
4 ZINC72371725 T NT T NT NT NT NT
5 ZINC19698731 NT NT T NT T T NT
6 ZINC01373165 NT NT NT T NT T T
Table 3: Toxicity prediction of CK1δ inhibitors using ADMET predictor
Molecular Dynamics Studies
Figure 4a: RMSD and RMSF of protein ligand complex CK1δ-ZINC09036109
13 Figure 4b: RMSD and RMSF of protein ligand complex CK1δ-ZINC01373165
14 Figure 4c: RMSD and RMSF of protein ligand complex CK1δ-PF4800567
Figure 5a: 2D Molecular interaction diagram and Protein-Ligand contacts of
ZINC09036109 with amino acid residues of CK1δ
16 Figure 5b: 2D Molecular interaction diagram and Protein-Ligand contacts of
ZINC01373165 with amino acid residues of CK1δ
Figure 5c: 2D Molecular interaction diagram and Protein-Ligand contacts of PF4800567
with amino acid residues of CK1δ
• These studies reveal that ZINC09036109, ZINC19698731 and ZINC01373165 will
be approaching optimal ADMET properties along with good dG bind score. Co-
crystallized PF4800567 was toxic to hERG whereas, three screened hits are
expected to show less toxicity potential.
• Molecular dynamics simulation explains that ZINC09036109 and ZINC01373165
have good selectivity for CK1δ on the basis of key residues interactions.
• Department of Science & Technology (DST) -Rajasthan for Research
• DST-Rajasthan and Council of Scientific & Industrial Research (CSIR) –
India for fellowship.
• Indian Council for Medical Research (ICMR) for Travel Support.
• Central University of Rajasthan for Infrastructural facilities.