Design of Curcumin and Flavonoid Derivatives with Acetylcholinesterase and Beta-Secretase Inhibitory Activities Using in Silico Approaches
Abstract
:1. Introduction
2. Results
2.1. Combinatorial Library of Curcumin and Flavonoid Derivatives
2.2. 3D-Pharmacophore Models
2.3. Virtual Screening
2.4. 2D-QSAR Models
2.5. Molecular Docking
2.6. AChE and β-Secretase Inhibitory Activities
3. Discussion
4. Materials and Methods
4.1. Design a Combinatorial Library of Curcumin and Flavonoid Compounds
4.2. Building and Validating Pharmacophore Models
4.3. Building 2D-QSAR Models
4.3.1. Data Collection
4.3.2. Molecular Descriptors Calculation and Processing
4.3.3. Building and Validating of 2D-QSAR Models
4.4. Molecular Docking Procedure
4.4.1. Ligand Preparation
4.4.2. Docking and Results Evaluation
4.5. Chemistry
4.6. AChE Inhibition Assay
4.7. β-Secretase Inhibition Assay
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
References
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Sample Availability: Not available. |
-OH | -OCH3 | -OC2H5 | -OCOCH3 |
---|---|---|---|
-F | -Cl | -Br | -I |
-NO2 | -NH2 | -N(CH3)2 | -NHCOCH3 |
-CH2CHH=CH2 | -COOH | -COOCH3 | -COOC2H5 |
-CN | -CONH2 | -SO2NH2 | -SH |
-SCH3 | -SC2H5 | C6H5CH2O- |
AChE | BACE-1 | ||||
---|---|---|---|---|---|
Training Set | Validation Sets | Training Set | Validation Sets | ||
Active | Decoy | Active | Decoy | ||
04 | 655 | 26369 | 04 | 436 | 18217 |
No. | Parameter | Pharmacophore Model | |
---|---|---|---|
A1 | B1 | ||
1 | Total molecules in database (D) | 27024 | 18653 |
2 | Total number of actives in database (A) | 655 | 436 |
3 | Total hits (Ht) | 914 | 438 |
4 | Active Hits (Ha) | 524 | 305 |
5 | %Yield of actives [(Ha/Ht) × 100] | 57.33 | 69.63 |
6 | %Ratio of actives [(Ha/A) × 100] | 80 | 69.95 |
7 | Enrichment factor (E), [(Ha × D)/(Ht × A)] | 23.65 | 29.79 |
8 | False negatives [A – Ha] | 131 | 131 |
9 | False positives [Ht − Ha] | 390 | 133 |
10 | Goodness-of-hit score (GH*) | 0.62 | 0.69 |
AChE | ||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Model AT | Model AF: Full data set (n = 72) | |||||||||||||
pIC50 = −0.92791 | pIC50 = −1.00890 | |||||||||||||
+2.34847×BCUT_SLOGP_3 | +2.38027×BCUT_SLOGP_3 | |||||||||||||
−0.14990×reactive | −0.11002×reactive | |||||||||||||
−0.00355×PEOE_VSA+1 | −0.00391×PEOE_VSA+1 | |||||||||||||
−0.00514×PEOE_VSA-3 | −0.00480×PEOE_VSA-3 | |||||||||||||
−0.00219×SlogP_VSA2 | −0.00202×SlogP_VSA2 | |||||||||||||
−0.00447×SMR_VSA2 | −0.00387×SMR_VSA2 | |||||||||||||
Internal Validation | External Validation | |||||||||||||
N | RMSE | R2 | RMSELOO | R2LOO | n | RMSE | R2 | R2(PRED) | CCC | |||||
50 | 0.18 | 0.70 | 0.22 | 0.57 | 22 | 0.16 | 0.78 | 0.78 | 0.64 | 0.69 | 0.11 | 0.88 | 0.72 | |
BACE-1 | ||||||||||||||
Model BT | Model BF: Full Data Set (N = 215) | |||||||||||||
pIC50 = 1.26826 | pIC50 = 1.01351 | |||||||||||||
+0.87076×petitjean | +0.59775×petitjean | |||||||||||||
+6.37086×BCUT_PEOE_1 | +4.85517×BCUT_PEOE_1 | |||||||||||||
+3.30481×a_ICM | +3.13351×a_ICM | |||||||||||||
−0.47753×chiral_u | −0.50839×chiral_u | |||||||||||||
+0.08513×rings | +0.02540×rings | |||||||||||||
+0.15746×a_nN | +0.16067×a_nN | |||||||||||||
+0.00608×PEOE_VSA-0 | +0.00577×PEOE_VSA-0 | |||||||||||||
+0.02183×PEOE_VSA-6 | +0.01771×PEOE_VSA-6 | |||||||||||||
−0.25952×logS | −0.26227×logS | |||||||||||||
+0.00893×SlogP_VSA3 | +0.00920×SlogP_VSA3 | |||||||||||||
+0.00944×SlogP_VSA5 | +0.01101×SlogP_VSA5 | |||||||||||||
Internal Validation | External Validation | |||||||||||||
N | RMSE | R2 | RMSELOO | R2LOO | n | RMSE | R2 | R2(PRED) | CCC | |||||
150 | 0.37 | 0.80 | 0.40 | 0.77 | 65 | 0.41 | 0.83 | 0.81 | 0.79 | 0.76 | 0.05 | 0.91 | 0.76 |
Code | Category | Description |
---|---|---|
BCUT_SlogP_3 | Adjacency and distance matrix | A Burden’s parameter using atomic contribution to logP (using the Wildman and Crippen SlogP method [34]) instead of partial charge. |
BCUT_PEOE_1 | Adjacency and distance matrix | A descriptor relating topological shape and partial charges. |
petitjean | Adjacency and distance matrix | Value of (diameter-radius)/diameter. |
reactive | Physical property | An indicator of the presence of reactive groups. A non-zero value indicates that the molecule contains a reactive group. The table of reactive groups is based on the Oprea set [35] and includes metals, phospho-, N/O/S-N/O/S single bonds, thiols, acyl halides, Michael Acceptors, azides, esters, etc. |
logS | Physical property | The log of the aqueous solubility (mol/L). |
PEOE_VSA-0, PEOE_VSA+1, PEOE_VSA-3, PEOE_VSA-6 | Partial charge | Sum of the proximate accessible van der Waals surface area (Å2), vi, calculation for each atom over all the atoms i, such that partial charge of atom i is in a specified range. |
SlogP_VSA2, SlogP_VSA3, SlogP_VSA5 | Subdivided surface areas | Sum of the proximate accessible van der Waals surface area (Å2), vi, calculated for each atom over all the atoms, such that partition coefficient for atom i is in a specified range. |
SMR_VSA2 | Subdivided surface areas | Sum of the proximate accessible van der Waals surface area (Å2), vi, calculation for each atom over all the atoms i, such that molar refractivity for atom i is in a specified range. |
a_ICM | Atom counts and bond counts | The entropy of the element distribution in the molecule (including implicit hydrogens but not lone pair pseudo-atoms). |
chiral_u | Atom counts and bond counts | The number of unconstrained chiral centers. |
rings | Atom counts and bond counts | The number of rings. |
a_Nn | Atom counts and bond counts | The number of nitrogen atoms. |
AChE | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Ligand | Predicted pIC50 | In Vitro IC50 (µM) | In Vitro pIC50 | Docking Score (kJ.mol−1) | |||||||
1ACJ | 1DX6 | 1EVE | 1W6R | 4EY6 (chain A) | 4EY6 (chain B) | 4EY7 (chain A) | 4EY7 (chain B) | ||||
C1 | 4.37 | - | - | Not docked | −24.13 | −25.62 | −25.47 | Not docked | Not docked | −34.38 | −36.23 |
C2 | 4.24 | - | - | Not docked | −30.97 | −23.97 | −25.19 | −23.21 | −12.72 | −23.56 | −31.90 |
F2 | 4.72 | - | - | −21.38 | −21.18 | −23.30 | -27.09 | −21.95 | −21.56 | −28.70 | −30.99 |
F9 | 4.86 | 30.05 ± 1.24 | 4.52 ± 0.02 | −20.27 | −28.46 | −25.53 | −25.80 | −25.63 | −27.26 | −25.17 | −27.11 |
F24 | 4.87 | 80.52 ± 3.07 | 4.09 ± 0.02 | −20.19 | −21.08 | −20.87 | −21.53 | −24.10 | −23.66 | −25.27 | −26.59 |
F37 | 4.84 | - | - | −20.58 | −22.46 | −21.81 | −22.98 | −21.02 | −22.75 | −23.34 | −22.45 |
BACE-1 | |||||||||||
3VEU | 4B78 | 5HU0 (chain A) | 5HU0 (chain B) | 5HU1 (chain A) | 5HU1 (chain B) | ||||||
C1 | 10.27 | - | - | −24.28 | −10.23 | −17.28 | −22.09 | −17.39 | −14.74 | ||
C2 | 9.13 | - | - | −24.04 | −24.64 | −27.00 | −16.51 | −25.78 | −17.79 | ||
F2 | 6.30 | - | - | −19.51 | −13.11 | −14.79 | −14.92 | −16.47 | −17.20 | ||
F9 | 6.77 | 1.85 ± 0.33 | 5.73 ± 0.08 | −21.34 | −15.98 | −18.53 | −17.83 | −20.32 | −19.49 | ||
F24 | 6.44 | 3.52 ± 0.77 | 5.45 ± 0.10 | −22.39 | −14.36 | −18.58 | −17.07 | −15.09 | −16.19 | ||
F37 | 6.11 | - | - | −21.87 | −13.66 | −17.39 | −15.80 | −16.61 | −15.09 |
Source | Model | Training Set | Validation Set | |||
---|---|---|---|---|---|---|
N | R2 | Q2 | n | R2PRED | ||
This study | PLS | 55 | 0.70 | 0.57 | 22 | 0.78 |
Roy et al. 2018 [37] | MLR | 284 | 0.52–0.74 | 0.50–0.71 | 142 | 0.50–0.63 |
Niraj et al. 2015 [38] | PLS | 24 | 0.78 | 0.70 | 11 | 0.66 |
Abuhamdah et al. 2013 [24] | GFA−MLR | 68 | 0.94 | 0.92 | 17 | 0.84 |
Solomon et al. [39] | GFA | 53 | 0.86 | 0.80 | 26 | 0.86 |
Source | Model | Training Set | Validation Set | |||
---|---|---|---|---|---|---|
N | R2 | Q2 | n | R2PRED | ||
This study | PLS | 150 | 0.80 | 0.77 | 65 | 0.81 |
Kumar et al. 2019 [21] | PLS | 76 | 0.83 | 0.80 | 22 | 0.85 |
Ambure et al. 2016 [40] | PLS | 52 | 0.83 | 0.76 | 22 | 0.81 |
Ambure et al. 2016 [40] | MLR | 51 | 0.83 | 0.76 | 22 | 0.80 |
Jain et al. 2013 [41] | MLR | 20 | 0.90 | 0.90 | 7 | 0.90 |
Hossain et al. 2013 [42] | CoMFA | 71 | 1.00 | 0.77 | 35 | 0.77 |
Hossain et al. 2013 [42] | CoMSIA | 71 | 1.00 | 0.73 | 35 | 0.71 |
Hossain et al. 2013 [42] | PLS | 71 | 0.94 | 0.79 | 35 | 0.71 |
Chakraborty et al. 2017 [43] | LHM | 20 | 0.94 | 0.91 | 10 | 0.86 |
Roy et al. 2018 [37] | MLR | 51 | 0.76–0.83 | 0.71–0.76 | 23 | 0.75–0.91 |
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Tran, T.-S.; Le, M.-T.; Tran, T.-D.; Tran, T.-H.; Thai, K.-M. Design of Curcumin and Flavonoid Derivatives with Acetylcholinesterase and Beta-Secretase Inhibitory Activities Using in Silico Approaches. Molecules 2020, 25, 3644. https://doi.org/10.3390/molecules25163644
Tran T-S, Le M-T, Tran T-D, Tran T-H, Thai K-M. Design of Curcumin and Flavonoid Derivatives with Acetylcholinesterase and Beta-Secretase Inhibitory Activities Using in Silico Approaches. Molecules. 2020; 25(16):3644. https://doi.org/10.3390/molecules25163644
Chicago/Turabian StyleTran, Thai-Son, Minh-Tri Le, Thanh-Dao Tran, The-Huan Tran, and Khac-Minh Thai. 2020. "Design of Curcumin and Flavonoid Derivatives with Acetylcholinesterase and Beta-Secretase Inhibitory Activities Using in Silico Approaches" Molecules 25, no. 16: 3644. https://doi.org/10.3390/molecules25163644