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MTDLs Design on AChE (Acetylcholinesterase) and β-Secretase (BACE-1): 3D-QSAR and Molecular Docking Studies

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) and a non-cross-validated coefficient (R), 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.


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][4][5][6][7][8].
An in vitro and in vivo characterization revealed its multifunctional mechanism of action and its of multitargeted AD inhibitors, molecular docking and 3D-QSAR (three-dimensional quantitative structure-activity relationship) studies should be carried out [10]. Molecular docking is an efficient tool for investigating receptor-ligand interactions, which plays a key role in clarifying the mechanism of molecular recognition in order to improve some biological function for the design of new compounds, especially when the crystal structure of a receptor or enzyme is available [11,12]. The aim of 3D-QSAR modeling is that the developed model should be strong enough to be capable of making accurate and reliable predictions of biological activities of new compounds [13].
In this context, we focused on finding new MTDLs with high potency and selective activity on AChE

Materials and Methods
In the present work, the 32 compounds in in vitro reported by Bolognesi et al. [1,2,8,17] were taken for the study, and their structures and bioactivities were listed in Table 1 (A1-A6 [17], B1-B8 [2], C1-C3 [8], D1-D7 [1], E1-E8 [8]). Four compounds in Table   1 were randomly selected as an external test set for further model validation, and the rest of the 28 compounds served as a training set to build the 3D-QSAR model.
The 3D structures of these compounds were sketched with molecular modeling software package SYBYL-X2.0 [18] and energetically minimized using the Tripos force field with Gasteiger-Hückel charges [10]. The 3D-QSAR model and molecular docking were carried out with the software package SYBYL-X2.0 [18]. The crystal structures of human AChE complexed with donepezil (E20) and human BACE-1 complexed with AZD3835 (32D) were retrieved from PDB with corresponding entry code 4EY7 [19] and 4B05 [20], respectively (http://www.pdb.org/). In order to compare the results from docking protocols and obtain good docking score, the donepezil and AZD3835 ligands were removed from donepezil-AChE complex and AZD3835-BACE-1 complex, respectively, and then the hydrogen atoms were added on the AChE and BACE-1 enzymes, respectively. Other parameters were established by default in software.

CoMFA and CoMSIA Model
For CoMFA model, steric and electrostatic fields were probed using a sp 3 carbon atom with a +1.0 net charge atom and a distance-dependent dielectric at each lattice point. The steric and electrostatic contributions were truncated at a default value of 30.0 kcal/mol and the latter were ignored at the lattice intersections with the maximal steric interactions [21,22].
For CoMSIA model, it was derived with the same lattice box as used for the CoMFA calculations. Five physicochemical properties (steric (S), electrostatic (E), hydrophobic (H), hydrogen bond donor (D), and hydrogen bond acceptor (A)) were evaluated using the probe atom. A probe atom sp3 carbon with a charge of +1, hydrophobicity of +1, and H-bond donor and acceptor property of +1 was placed at every grid point to measure the S, E, H, D and A fields [21]. In this paper, the statistical results showed that the CoMSIA model by SEHD combination gave the highest 491 Table 1 Structures and predicted activities of 32 inhibitors.  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].

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 q 2 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 (R 2 ) using the optimum NOC obtained from the leave-one-out cross-validation analysis [10,21,23].

CoMFA and CoMSIA Model Analysis
The results of PLS analysis were summarized in Table 2. Often, a high q 2 value (q 2 > 0.5) is considered as a proof of the high predictive ability of the model [24,25]. As shown in Table 2, the q 2 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 (R 2 ) 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   Fig. 2a: (i) The right vanillic group located in large green region, while the left one located in small green region; (ii) The right carbon chain located in large yellow region, while the left one does not. The result suggests that one side of inhibitor should be small group so that it would be conductive to enter the gorge to interact with the catalytic active sites, and the other side of inhibitor should be large group so that it would be favorable for interaction with the peripheral anionic site. From the contour plot of CoMFA electrostatic field in Fig. 2b, the blue is the electropositive favored color, and the red is the electronegative favored color. Fig.  2b reveals that the electronegative substituents should be distributed around the right and left imino-groups near the vanillic group, while the electropositive substituents should be distributed around two carbonyl of benzoquinone [24,27].
As shown in Table 2, the steric field descriptor explains 19.3% of the variance, while the proportion of electrostatic field, hydrophobic field and hydrogen bond donor field descriptors account for 29.5%, 29.6% and 21.6%, respectively [26]. It could be seen from the CoMSIA contour map in Fig. 3 that, the CoMSIA steric (Fig. 3a) and electrostatic (Fig. 3b) fields were in accordance with the distribution of the CoMFA steric (Fig. 2a) and electrostatic (Fig. 2b) fields, respectively. However, as comparative with Fig. 2b, the area of red (favor electronegative) was small in Fig. 3b. As for the difference, it might be highly probable the reason that the hydrophobic and other factors would affect the inhibitory activity [26].
The contour plot of CoMSIA hydrophobic field in Fig. 3c displays that the methoxyls and hydroxyls of two vanillic groups all located in white regions (favor hydrophilic), which means that inhibitory potency would be enhanced when the substituent groups were more hydrophilic. The right carbon chain of benzoquinone located in yellow regions, suggesting that the hydrophobic groups in this region might improve inhibitory potency.
According to the contour plot of CoMSIA bond donor field in Fig. 3d, left imino-group near the benzoquinone and carbonyl on benzoquinone both located in cyan regions, which reveals that hydrogen bond donor groups around there could improve inhibitory activity. The right imino-group near the vanillic group located in purple region. The result demonstrates that the hydrogen bond donor groups around there would not be conducive to inhibitory activity.

2. 1. Binding Mode with AChE
According to the Refs. [28][29][30], the active sites of AChE contain: (i) Catalytic triplets (SER203-GLU334-HIS447) and catalytic anionic sites (TRP86 and TYR337) which is located at the bottom of gorge; (ii) Peripheral anionic binding sites composed of residues TRP286 and TYR341 which is located at the entrance (mouth) of gorge. Our attention next turned toward the molecular docking with AChE, and three compounds (B1, B2 and B8) in Table 1 were selected as examples to illustrate the detailed interactions with AChE. Table 1, the sequence of inhibitory activity was B2 > B1 > B8, while the sequence of total score was B1 > B8 > B2. In order to further clarify the results, we investigate the binding modes with AChE as shown in Fig. 4. For B2 (deep blue stick model), the whole molecule get stuck at the entrance of gorge (the peripheral site); for B1 (light blue bats model), the vanillic region is located at the entrance of gorge, and the others regions enter the gorge to interact with the catalytic active sites; for B8 (light blue stick model), the phenyl benzothiazole region enters the gorge to interact with the catalytic active sites, and the others regions get stuck at the entrance of gorge. Meanwhile, it could be observed from Fig. 4   B1 enters the gorge to be more deeper than the B8. The result reveals that the total score might be dependent on the depth which inhibitor molecule enters the gorge, the deeper the molecule enters the gorge, the higher the total score would be. That explains why the sequence of total score was B1 > B8 > B2. However, the inhibitory activity had the order of B2 > B1 > B8, suggesting that the inhibitory activity should be determined by the interaction strength with AChE, the stronger the interaction would be, the higher the inhibitory activity would be. The result reflects that the peripheral site might be the important binding site for the inhibitory potency. Recent studies have also demonstrated that the peripheral site might accelerate the aggregation and deposition of beta-amyloid peptide, which is considered as another cause of AD [10,[31][32][33].

that the
Based on above results, we can come to a conclusion that one side of inhibitor should be small volume group which could be easy to enter the gorge to interact with the catalytic active sites at the bottom of grge, and the other side of inhibitor should be large volume group which could interact strongly with the peripheral anionic sites at the entrance of gorge. The result is in well accordance with the CoMFA (Fig. 2a) and CoMSIA (Fig. 3a) steric contour plots obtained in 3D-QSAR studies.

2. 2 Binding Mode with BACE-1
Up to now, crystal structure of the BACE-1 enzyme in complex with the different inhibitors have established its different binding subsites, i.e., S1 subsites include Leu30, Phe108, Typ115, and Ile118; S2 subsites include Arg235, Gln12, and Asn233; S3 subsites include Ala335, Ile110, and Ser113; S1' subsites include Lys224 and Thr329; and S2' subsites include Ile126 and Arg128 [34]. In this section, B8 in Table 1 was chosen as an example to characterize the binding mode with BACE-1. It could be observed from Fig. 5 that (i) the benzoquinone occupies the S1 pocket, (ii) six methylene occupy the S3' pocket, and the phenyl benzothiazole occupies the S2' pocket, (iii) the phenyl benzothiazole occupies the S1' pocket, (iv) the S3, S3sp, S2 pockets are empty. Huang et al. [35] stated that the S1, S2' and S3 active sites have the important role in improvement the inhibitory activity, and the S1, S2' and S3 pockets consist of largely hydrophobic environment. Therefore, if the hydrophobic substituents could occupy simultaneously S1, S2' and S3 pockets of BACE-1, a significant inhibitory potency would be increased.
Furthermore, as shown in Fig. 5, the active pockets of BACE-1 are all in the surface of protein, the S3 (including S3sp) pocket is wide, which is suitable for bukly substituents, while the S2' pocket is narrow, which is suitable for small volume substituents. The result is in strong agreement with the CoMFA (Fig. 2a) and CoMSIA (Fig. 3a) steric contour plots obtained in 3D-QSAR studies.

Molecular Design
Based on the above results of 3D-QSAR model and molecular docking, we have designed eight novel multitargeted (AChE and BACE-1) inhibitors as shown in Table 3. One can observe from it that the predicted activities and total scores of eight designed molecules were all much larger than B2. In particular, F1 had the strongest predicted activity. F5 (binding with AChE) and E2 (binding with BACE-1) had the highest total scores, respectively.
In order to further clarify the results, the binding modes of F1, F5 and E2 with AChE were shown in Fig. 6. It could be observed from Fig. 6a that, F1 could enter the bottom of gorge to interact with the catalytic active sites (anionic site and triplets) so that the ACh (acetylcholine) hydrolysis might be prevented, and strong inhibitory activity of F1 might be achieved. As shown in Fig. 6b, it might be the  Table 1). reason that F5 molecule is the planar conformation leading to the F5 would be advantageous to prevent ACh (acetylcholine) molecule into the gorge of AchE. The result is agreement with Chen and Ling's [36,37] result, which the plane conformation of territrem B is the ensure of high inhibitory activity. Fig. 7 shows the binding modes of F1, F5 and E2 with BACE-1, respectively. It could be seen from it that F1 occupies S1 and S2' pockets, F5 occupies S3 pocket, E2 occupies simultaneously S2', S1 and S3 pockets so that its total score is the largest among eight designed molecules [35]. As mentioned above binding modes in Figs. 6 and 7, we found that the designed molecules in the present work could simultaneously bind to the catalytic and peripheral sites of AChE, which could disrupt the interactions between the AChE and ACh (acetylcholine), hence slow down the progression of the AD disease [10,38,39]. Furthermore, the designed molecules could also interact with dual targets (AChE and BACE-1) simultaneously. In particular, we hope that F1, F5 and E2 could be used as novel lead compounds on the biological experiment and further study.

MTDLs Design on AChE (Acetylcholinesterase) and β-Secretase
The multifactorial nature of AD strongly supports the drug design strategy on MTDLs. Although this exciting new approach is still in its infancy [1,40], and the selection of a therapeutic target is one of the biggest challenges in designing new molecules for this multifactorial disease [1,41], it has opened a new avenue for the drug therapy of neurodegenerative diseases [1,42]. In this respect, the road to developing new drugs based on the MTDLs strategy is still long, but it is highly conceivable that MTDLs may represent the future treatment for AD [1].

Conclusions
The 3D-QSAR model was established for 32 AD inhibitors by using CoMFA and CoMSIA techniques.
Results showed that the CoMFA and CoMSIA models inhibitors. We hope that our results could provide hints for the design of new multitargeted AD derivatives with high potency and specific activity.