Journal of Pharmacy and Pharmacology 3 (2015) 489-501 doi: 10.17265/2328-2150/2015.10.006 MTDLs Design on AChE (Acetylcholinesterase) and β-Secretase (BACE-1): 3D-QSAR and Molecular Docking Studies Jiancheng Shi, Wentong Tu, Jiarong Sheng and Chusheng Huang College of Chemistry and Material Sciences, Guangxi Teachers Education University, Nanning 530001, China Abstract: To find promising new multitargeted AD (Alzheimer’s disease) inhibitors, the 3D-QSAR (three-dimensional quantitative structure-activity relationship) model for 32 AD inhibitors was established by using the CoMFA (comparative molecular field analysis) and CoMSIA (comparative molecular similarity index analysis) methods. Results showed that the CoMFA and CoMSIA models were constructed successfully with a good cross-validated coefficient (q 2 ) and a non-cross-validated coefficient (R 2 ), and the binding modes obtained by molecular docking were in agreement with the 3D-QSAR results, which suggests that the present 3D-QSAR model has good predictive capability to guide the design and structural modification of novel multitargeted AD inhibitors. Meanwhile, we found that one side of inhibitory molecule should be small group so that it would be conductive to enter the gorge to interact with the catalytic active sites of AChE (acetylcholinesterase), and the other side of inhibitory molecule should be large group so that it would be favorable for interaction with the peripheral anionic site of AChE. Furthermore, based on the 3D-QSAR model and the binding modes of AChE and β-secretase (BACE-1), the designed molecules could both act on dual binding sites of AChE (catalytic and peripheral sites) and dual targets (AChE and BACE-1). We hope that our results could provide hints for the design of new multitargeted AD derivatives with more potency and selective activity. Key words: 3D-QSAR, molecular docking, AChE, BACE-1, MTDLs. 1. Introduction In the fight against AD (Alzheimer’s disease), the etiology of AD has yet to be fully elucidated, and there is compelling evidence that this neurodegenerative disease is a multifactorial syndrome [1, 2]. Therefore, pharmaceutical researchers have proposed a move from the “one protein, one target, one drug” strategy to the “one drug, multiple targets” paradigm, which suggests the use of compounds with multiple activities at different target sites. Accordingly, the MTDLs (multitarget-directed ligands) design strategy has been the subject of increasing attention by many research groups [3-8]. An in vitro and in vivo characterization revealed its multifunctional mechanism of action and its Corresponding author: Chusheng Huang, Ph.D, professor, research fields: organic synthesis and natural products. E-mail: [email protected]. interaction with three molecular targets involved in AD pathology, namely, AChE (acetylcholinesterase), Aβ (β-amyloid), and β-secretase (BACE-1) [8, 9]. Up to now, the MTDLs design strategy has proven particularly fruitful, some of which have emerged as interesting pharmacological tools for the investigation of neurodegenerative disorders, or as innovative drug candidates for combating AD [3-7]. For example, Hui et al. [4] reported that design and synthesis of tacrine-phenothiazine hybrids as multitarget drugs for Alzheimer’s disease. Rosini et al. [5] studied that multi-target design strategies in the context of Alzheimer’s disease: acetylcholinesterase inhibition and NMDA receptor antagonism as the driving forces. Bolea et al. [6] stated propargylamine-derived multitarget-directed ligands: fighting Alzheimer’s disease with monoamine oxidase inhibitors. In order to further elucidate the binding mechanism D DAVID PUBLISHING
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Journal of Pharmacy and Pharmacology 3 (2015) 489-501 doi: 10.17265/2328-2150/2015.10.006
MTDLs Design on AChE (Acetylcholinesterase) and
β-Secretase (BACE-1): 3D-QSAR and Molecular Docking
Studies
Jiancheng Shi, Wentong Tu, Jiarong Sheng and Chusheng Huang
College of Chemistry and Material Sciences, Guangxi Teachers Education University, Nanning 530001, China
Abstract: To find promising new multitargeted AD (Alzheimer’s disease) inhibitors, the 3D-QSAR (three-dimensional quantitative structure-activity relationship) model for 32 AD inhibitors was established by using the CoMFA (comparative molecular field analysis) and CoMSIA (comparative molecular similarity index analysis) methods. Results showed that the CoMFA and CoMSIA models were constructed successfully with a good cross-validated coefficient (q2) and a non-cross-validated coefficient (R2), and the binding modes obtained by molecular docking were in agreement with the 3D-QSAR results, which suggests that the present 3D-QSAR model has good predictive capability to guide the design and structural modification of novel multitargeted AD inhibitors. Meanwhile, we found that one side of inhibitory molecule should be small group so that it would be conductive to enter the gorge to interact with the catalytic active sites of AChE (acetylcholinesterase), and the other side of inhibitory molecule should be large group so that it would be favorable for interaction with the peripheral anionic site of AChE. Furthermore, based on the 3D-QSAR model and the binding modes of AChE and β-secretase (BACE-1), the designed molecules could both act on dual binding sites of AChE (catalytic and peripheral sites) and dual targets (AChE and BACE-1). We hope that our results could provide hints for the design of new multitargeted AD derivatives with more potency and selective activity. Key words: 3D-QSAR, molecular docking, AChE, BACE-1, MTDLs.
1. Introduction
In the fight against AD (Alzheimer’s disease), the
etiology of AD has yet to be fully elucidated, and
there is compelling evidence that this
neurodegenerative disease is a multifactorial
syndrome [1, 2]. Therefore, pharmaceutical
researchers have proposed a move from the “one
protein, one target, one drug” strategy to the “one drug,
multiple targets” paradigm, which suggests the use of
compounds with multiple activities at different target
sites. Accordingly, the MTDLs (multitarget-directed
ligands) design strategy has been the subject of
increasing attention by many research groups [3-8].
An in vitro and in vivo characterization revealed its
multifunctional mechanism of action and its
Corresponding author: Chusheng Huang, Ph.D, professor,
research fields: organic synthesis and natural products. E-mail: [email protected].
interaction with three molecular targets involved in
AD pathology, namely, AChE (acetylcholinesterase),
Aβ (β-amyloid), and β-secretase (BACE-1) [8, 9].
Up to now, the MTDLs design strategy has proven
particularly fruitful, some of which have emerged as
interesting pharmacological tools for the investigation
of neurodegenerative disorders, or as innovative drug
candidates for combating AD [3-7]. For example, Hui
et al. [4] reported that design and synthesis of
tacrine-phenothiazine hybrids as multitarget drugs for
Alzheimer’s disease. Rosini et al. [5] studied that
*Samples in the test set; a: Experimental activity (PIC50); b: Predicted activity (PIC50); c: The residual difference between experimental and predicted activities; d: Docking total_score on AChE; e: Docking total_score on BACE-1.
cross-validated value (q2), correlation coefficient (R2)
and fischer test value (F), which means that the SEHD
combination has the best prediction ability and
stability. Therefore, we chose SEHD combination to
establish the best CoMSIA model. Meanwhile, the
statistical evaluation for the CoMSIA analyses was
executed in the same way as described for CoMFA
[10].
3.1.2 PLS (Partial Least-Square) Calculations and
Validations
The relationship between the CoMFA and CoMSIA
interaction energies and the AChE inhibitory activity
(pIC50) has been quantified by the PLS (partial
least-square) method (leave-one-out) [21, 23]. The
cross-validated q2 that resulted in the NOC (optimum
number of components) and lowest standard error of
MTDLs Design on AChE (Acetylcholinesterase) and β-Secretase (BACE-1): 3D-QSAR and Molecular Docking Studies
493
prediction was selected. The minimum column
filtering value was set to 2.0 kcal/mol to speed up the
analysis with improvement signal-to-noise ratio. Final
analysis was performed to calculate
non-cross-validated (R2) using the optimum NOC
obtained from the leave-one-out cross-validation
analysis [10, 21, 23].
3.1.3 CoMFA and CoMSIA Model Analysis
The results of PLS analysis were summarized in
Table 2. Often, a high q2 value (q2 > 0.5) is
considered as a proof of the high predictive ability of
the model [24, 25]. As shown in Table 2, the q2 values
of CoMFA and CoMSIA models are 0.535 and 0.537,
respectively, which suggests that the CoMFA and
CoMSIA models have strong predictive ability [26].
Meanwhile, we could observe from Fig. 1 that the
predicted values using the newly constructed CoMFA
and CoMSIA models were in well agreement with
experimental data, which reveals that the CoMFA and
CoMSIA models are reliable [26]. Furthermore, it
could be obtained from Fig. 1 that the correlation
coefficient (R2) is 0.999 for CoMFA model (Fig. 1a),
while 0.992 for CoMSIA model (Fig. 1b). The result
means that the CoMFA model is more reliable than
CoMSIA model. Therefore, the CoMFA model was
employed to design new inhibitors in the present
work.
Based on above 3D-QSAR model, the CoMFA and
CoMSIA coefficient isocontour maps were made onto
the active sites of enzyme (AChE) in Figs. 2 and 3,
respectively [10]. One notes that the B series
inhibitors in Table 1 were chosen as examples to
validate the predictive capability of 3D-QSAR model,
and the compound B2 (the most potent inhibitor of B
series) was used as a reference molecule in Figs. 2 and
3 [10]. Table 2 shows that the CoMFA steric field
descriptor explains 51.9% of the variance, while the
electrostatic descriptor explains the rest 48.1%. These
steric and electrostatic fields were presented as
contour plots in Figs. 2a and 2b, respectively [26].
As seen from the contour plot of CoMFA steric
field in Fig. 2a, the bulky substituent in green regions
(favor steric) would be favorable for inhibitory
potency, while bulky substituent in yellow regions
(disfavor steric) would not be beneficial to inhibitory
activity. In particular, there are two interesting features
Table 2 Statistical indexes of CoMFA (comparative molecular field analysis) and CoMSIA (comparative molecular similarity index analysis) models based on 32 compounds.
Model q2 Optimal number of components R2 F QSAR field distribution (%) CoMFA CoMSIA
Ploeger, B., Cebers, G., Kolmodin, K., Swahn, B. M.,
Von Berg, S. Bueters, T., and Falting, J. J. Biol.
Chem., 2012, 287, 41245-41257), and has been
deposited in the Protein Data Bank with code 4B05
(http://www.pdb.org/).
Acknowledgments
The authors acknowledge the financial support of
the Natural Science Foundation of Guangxi Province
(No. 2013GXNSFAA019019) and the Natural Science
Foundation of Guangxi Province (No.
2013GXNSFAA019041).
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