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. 2020 Jan 28;5(5):2428–2439. doi: 10.1021/acsomega.9b03960

Insight into Ginkgo biloba L. Extract on the Improved Spatial Learning and Memory by Chemogenomics Knowledgebase, Molecular Docking, Molecular Dynamics Simulation, and Bioassay Validations

Yan Chen †,‡,§,∥,⊥, Zhiwei Feng ‡,§,∥,⊥, Mingzhe Shen ‡,§,∥,⊥, Weiwei Lin ‡,§,∥,⊥, Yuanqiang Wang #, Siyi Wang ‡,§,∥,⊥, Caifeng Li †, Shengfeng Wang †, Maozi Chen ‡,§,∥,⊥, Weiguang Shan †,*, Xiang-Qun Xie ‡,§,∥,⊥,*
PMCID: PMC7017398  PMID: 32064403

Abstract

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Epilepsy is a common cause of serious cognitive disorders and is known to have impact on patients’ memory and executive functions. Therefore, the development of antiepileptic drugs for the improvement of spatial learning and memory in patients with epileptic cognitive dysfunction is important. In the present work, we systematically predicted and analyzed the potential effects of Ginkgo terpene trilactones (GTTL) on cognition and pathologic changes utilizing in silico and in vivo approaches. Based on our established chemogenomics knowledgebase, we first conducted the network systems pharmacology analysis to predict that ginkgolide A/B/C may target 5-HT 1A, 5-HT 1B, and 5-HT 2B. The detailed interactions were then further validated by molecular docking and molecular dynamics (MD) simulations. In addition, status epilepticus (SE) was induced by lithium–pilocarpine injection in adult Wistar male rats, and the results of enzyme-linked immunosorbent assay (ELISA) demonstrated that administration with GTTL can increase the expression of brain-derived neurotrophic factor (BDNF) when compared to the model group. Interestingly, recent studies suggest that the occurrence of a reciprocal involvement of 5-HT receptor activation along with the hippocampal BDNF-increased expression can significantly ameliorate neurologic changes and reverse behavioral deficits in status epilepticus rats while improving cognitive function and alleviating neuronal injury. Therefore, we evaluated the effects of GTTL (bilobalide, ginkgolide A, ginkgolide B, and ginkgolide C) on synergistic antiepileptic effect. Our experimental data showed that the spatial learning and memory abilities (e.g., electroencephalography analysis and Morris water maze test for behavioral assessment) of rats administrated with GTTL were significantly improved under the middle dose (80 mg/kg, GTTL) and high dose (160 mg/kg, GTTL). Moreover, the number of neurons in the hippocampus of the GTTL group increased when compared to the model group. Our studies showed that GTTL not only protected rat cerebral hippocampal neurons against epilepsy but also improved the learning and memory ability. Therefore, GTTL may be a potential drug candidate for the prevention and/or treatment of epilepsy.

1. Introduction

Epilepsy, one of the most common central nervous system (CNS) disorders, can cause disturbances in the brain and often lead to seizures. It is a complex disorder with an unclear defined pathogenesis process. These disturbance episodes occur frequently with complex symptoms. Learning and memory dysfunction is the most common neuropsychological effects of epilepsy.

Brain-derived neurotrophic factor (BDNF), an important molecular mediator of the neuroplasticity in the brain, can influence many different brain functions (e.g., learning and memory).1,2 It is known that the BDNF concentration decreases during an epilepsy episode, which can be a factor underlying the epilepsy process. Currently, there are no effective treatments available to improve the prognosis of patients with epilepsy, despite having adequate trials for potentially effective antiepileptic agents. Several clinical observations3,4 suggest that epilepsy can lead to progressive cognitive impairment. Main physiopathological symptoms observed in epileptic patients include neuronal losses, reactive gliosis, mossy fiber sprouting, dendritic injury, and neurogenesis.5 A considerable amount of evidence from both rodent and human studies indicate that the status of epileptics epileptiform abnormalities can contribute to cognitive impairment.

Patients with epilepsy usually require long-term treatment with antiepileptic drugs (AEDs) and those with refractory epilepsy often require polytherapy. However, most AEDs are far from ideal and they often possess undesirable side effects while lacking the ability to manage these conditions in the long term. For example, carbamazepine hypersensitivity and valproate teratogenicity are some well-known limitations of AEDs. There are no available Food and Drug Administration (FDA)-approved drugs that have disease-modifying or preventive properties. Therefore, there is a clear need for the development of a more effective agent for the treatment or modification of the progression of epilepsy and epilepsy-related cognitive impairment. A solution to such a problem is to seek out a novel antiepileptic from the plant kingdom that could potentially offer a solution to mild cognitive impairment, without adverse effects associated with AEDs, such as hypersensitivity reactions or aplastic anemia.6

Ginkgo biloba L., also known as the “living fossil”, belongs to the family of Ginkgoaceae and has been introduced to other tepid climates in both hemispheres.7 Leaf extract of G. biloba L. (GBE) is a traditional Chinese medicine (TCM) used to treat both cardiovascular and cerebrovascular diseases and Alzheimer’s disease.8−10 Similarly, in European medicine, GBE has been employed for the improvement of memory, neuronal disorders (e.g., tinnitus or intermittent claudication), amelioration of brain metabolism, and peripheral blood flow.11 In addition, several studies using experimental animal models have been performed to further explore its additional therapeutic potential, especially in neurodegenerative and behavioral dysfunctions.12

Wide neuroprotective effects of GBE are determined by the following active substances: “terpene trilactones, flavonol glycosides, biflavones, proanthocyanidins, alkylphenols, simple phenolic acids, 6-hydroxykynurenic acid, 4-O-methylpyridoxine, polyprenols, and so on.”13 Currently, there is a large gap between the understanding of the GBE ingredients and their therapeutic effect in the damage of nerves. Recent research has demonstrated the repair of the function in learning and memory formation in subjects treated with Ginkgo flavonoids. However, studies on Ginkgo terpene trilactones (GTTLs) are limited. Ginkgolides are the only natural products that possess a tert-butyl group with many potential therapeutics.13,14 For example, a recent study15 shows that YY-1224, a GTTL strengthened G. biloba extraction, can ameliorate the Alzheimer’s disease pathogenesis by its inhibitory effect on oxidative stress and neuroinflammation. Lu et al.16 also demonstrated that GTTL has antithrombotic effects in vivo. In addition, Choi and co-workers17 evaluated the effects of the gut microbiota on the pharmacokinetics of the active form of GTTL. For safety/toxicity, Chan18 reported that ginkgolides A and B induced apoptosis and decreased cell numbers in mouse blastocysts, leading to embryonic death. In addition, Shiao et al.19 reported that ginkgolide B may cause negative impacts on the mouse oocytes fertilization, maturation, and fetal development both in vitro and in vivo.

In this study, we first carried out computational systems pharmacology (CSP)-target mapping for the active ingredients of GTTL. Next, docking studies and molecular dynamics (MD) simulations were used to explore the interaction pattern between the active ingredients of GTTL and the predicted targets (5-HT 1A, 5-HT 1B, and 5-HT 2B). Combining with the result of enzyme-linked immunosorbent assay (ELISA) that GTTL can increase the expression level of BDNF, we hypothesized that GTTL may have a positive effect on epilepsy prevention and treatment. Finally, we experimentally evaluated the active constituents of GTTL, including bilobalide and ginkgolide A/B/C on their ability to improve learning and memory, using lithium–pilocarpine-induced status epilepticus rats as a validation of our prediction.

2. Results and Discussion

2.1. Systems Pharmacology Analysis Using Our Knowledgebases and Tools

We extracted trilactone mixture from ginkgo leaves, including ginkgolide A, ginkgolide B, ginkgolide C, and bilobalide (Figure 1). The ratio of these four mentioned ingredients in the mixture is 4:1:2:3 based on the result of high performance liquid chromatography (HPLC). Taking the low dose 40 mg/kg as an example, GTTL contains 16 mg/kg ginkgolide A, 4 mg/kg ginkgolide B, 8 mg/kg ginkgolide C, and 12 mg/kg bilobalide.

Figure 1.

Figure 1

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Systems pharmacology analyses for GTTL. A map of GTTLs and their suspected targets with docking scores higher than 5.0 were constructed based on the binding simulations using our established knowledgebases and tools.

First, we performed target/off-target prediction for these four major ingredients of GTTL, using our Alzheimer Database (AlzPlatform), Hallucinogen-Specific Chemogenomics Database, and drug abuse-related GPCRs knowledgebase. The reason we choose these databases is that these databases contain CNS-related target proteins. As shown in Figure 1, a map of GTTLs and their suspected targets were constructed based on the binding simulations with docking scores higher than 5.0.

As illustrated in Figure 1, the potential targets of GTTL include monoamine oxidase A (MAO A), monoamine oxidase B (MAO B), 5-hydroxytryptamine receptor 1A (5-HT 1A), 5-hydroxytryptamine receptor 1B (5-HT 1B), 5-hydroxytryptamine receptor 2B (5-HT 2B), cytochrome P450 2D6 (CYP450 2D6), sucrase–isomaltase (SI), and Ras-related C3 botulinum toxin substrate 1 (RAC1). Interestingly, MAO A and MAO B belong to the monoamine oxidase family that oxidizes various amine substrates, such as small-molecule monoamines, polyamines, and modified amino acids within proteins.20 MAO A is known for its ability to regulate the levels of monoamine neurotransmitters including serotonin, noradrenaline, and dopamine,21 while MAO B catalyzes the oxidation of arylalkylamine neurotransmitters, mostly dopamine.22 5-HT 1A, 5-HT 1B, and 5-HT 2B all belong to the serotonin receptor family, which are involved with many accentual neurological processes, including mood regulation, sleep, learning, memory, and aggression.23 Cytochrome P450 2D6 is an isoform of cytochrome P450 present in the human brain, which is involved with the metabolism of various neurotransmitters and neurosteroids.24 SI (sucrase–isomaltase) is an intestinal glycoprotein utilized for the breakdown of glycogen and starch.25 RAC1 is a Rho GTPase, which is an important modulator in the cytoskeleton, and it is critical for numerous cellular functions such as phagocytosis, neuronal polarization, and adhesion.26

First, our results showed that ginkgolide C may be able to bind to MAO A (docking score: 5.29) or MAO B (docking score: 7.35), in turn indicating that ginkgolide C may potentially regulate the levels of monoamine neurotransmitters including serotonin, dopamine, and noradrenaline. Moreover, four active gradients of GTTL were predicted to bind to CYP2D6, meaning they may be metabolized by CYP2D6. Additionally, ginkgolides A, B, and C were predicted to bind to both 5-HT 2B and 5-HT 1A, but not bilobalide. The docking scores of 5-HT 1A were observed to be especially high for these three chemicals, including 6.14 for ginkgolide A, 6.09 for ginkgolide B, and 6.64 for ginkgolide C. The role of serotonin (5-HT) neurotransmission in the epileptogenesis has already been confirmed by several previous studies. Thus, our predicted results suggested the possible role of GTTL in the potential treatment of epilepsy. To further explore the docking interactions between 5-HT 1A/2B and ginkgolide A/B/C, we carried out molecular docking studies. The results are shown below.

2.2. Detailed Interactions between GTTL and 5-HT

Figure 2 shows that the binding pockets of 5-HT 1A and 5-HT 2B shared seven identical residues, such as Asp116/Asp1353.32, Val117/Val1363.33, Ala203/Ala2255.461, Trp358/Trp3376.48, Phe361/Phe3406.51, Phe362/Phe3416.52, and Tyr390/Tyr3707.42, while some different residues located in the binding site, such as Cys120/Ser1393.36, Tyr195/Phe2175.39, Thr196/Met2185.40, Ser199/Gly2215.43, Ala365/Asn3446.55, Ala383/Glu3637.35, and Asn386/Val3667.38, might influence the binding mode and affinity.

Figure 2.

Figure 2

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Detailed interaction between GTTL and 5-HT 1A and 5-HT 2B. (a–c) Docking interactions between 5-HT 1A and ginkgolide A/B/C. (d–f) Docking interactions between 5-HT 2B and ginkgolide A/B/C.

For 5-HT 1A, methyl-substituted lactone of the agonist inserts into the binding pocket and forms three strong hydrogen bonds between the carbonyl of lactone and hydroxyl of Tyr390 (∼3.4–3.6 Å) and carboxyl of Asp116 (∼2.7–2.9 Å), which stabilize their binding (Figure 2a–c). Meanwhile, the hydrogen bond (∼2.1–2.3 Å) between the amino of Asn386 and the oxygen on lactone further stabilizes their binding. In addition, the hydroxyl on the five-membered ring can form a hydrogen bond (3.4–3.6 Å) with the amide of Asn386 (Figure 2a–c).

Ginkgolide A/ B/C were docked back into the binding site of 5-HT 2B with a flipped pose owing to the different/selective residues between 5-HT 1A and 5-HT 2B (Ala383/Glu3637.35 and Asn386/Val3667.38). Two potential hydrogen bonds are observed between the carbonyl of lactone and carboxyl of Glu363, with the distance of ∼3.5–3.9 and ∼3.4–3.8 Å, respectively; however, the hydrogen bonds observed in ginkgolide B are weaker (3.9 and 3.8 Å) than ginkgolide A/C (Figure 2d–f). Four hydrogen bonds were recorded between the two hydroxyls on the five-membered ring of ginkgolide A/C and the hydroxyl of Tyr370 as well as the carboxyl of Asp135. The distance of these hydrogen bonds ranged from 2.9 to 3.8 Å (Figure 2d,f); the short length of these bonds has the potential to promote the binding of the ginkgolides and 5-HT 2B. The carbonyl on the lactone of ginkgolide B can only form one hydrogen bond with the hydroxyl of Tyr370 and one with the carboxyl of Asp135; the distance is 2.6 Å (Figure 2e). Our results indicated that ginkgolide A/B/C may bind to 5-HT 1A and 5-HT 2B and form a stable complex with strong hydrogen bonds.

2.3. Molecular Dynamics (MD) Simulation between 5-HT 1A and Ginkgolide A/C

To further explore the dynamic interactions, we performed a 100 ns MD for the complexes of 5-HT 1A and ginkgolide A/C. As shown in Figure 3a, the root-mean-square deviation (RMSD) of 5-HT 1A and ginkgolide A fluctuated around 1.8 and 0.6 Å, respectively, indicating that the system was very stable during the simulation. Moreover, RMSDs of 5-HT 1A and ginkgolide C were about 2.1 and 0.9 Å, respectively, which means this system stays at equilibrium, as shown in Figure 3b. Sequentially, we selected 500 snapshots from 50 to 100 ns to calculate the mean binding energy, as shown in Figure 3c,d. Importantly, we found that most of the residues involved in the binding site of 5-HT 1A shared a similar energy contribution to the recognition of ginkgolide A/C. For example, Asp116 in TM3 of 5-HT 1A contributed greatly to the binding of ginkgolide A/C with a strong hydrogen bond. Moreover, Thr118/Gly382/Asn386 interacted with ginkgolide A/C with additional hydrogen bonds. Finally, several hydrophobic residues involving Phe112, Ile113, Ile189, Phe361, and Leu368 also contributed to the recognition of ginkgolide A/C (Tables 1 and 2). All of the above results were consistent with our previous docking studies.

Figure 3.

Figure 3

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Convergence parameters of MD for 5-HT 1A complexed with ginkgolide A/C. (a) RMSD of the complex of 5-HT 1A and ginkgolide A. (b) RMSD of the complex of 5-HT 1A and ginkgolide C. (c) Energy decomposition of the complex of 5-HT 1A and ginkgolide A. (d) Energy decomposition of the complex of 5-HT 1A and ginkgolide C.

Table 1. Energy Decomposition of the Complex of 5-HT 1A and Ginkgolide A.

residues van der Waals electrostatic polar solvation nonpolar solvation total
Tyr96 –1.608 –0.094 0.044 –0.931 –2.589
Phe112 –1.835 –1.063 0.456 –1.240 –3.683
Ile113 –1.510 –0.443 0.423 –1.170 –2.699
Asp116 0.197 –14.880 5.836 –0.854 –9.702
Thr188 –1.955 –2.161 1.017 –1.191 –4.291
Ile189 –0.744 –0.517 0.333 –0.388 –1.316
Phe361 –1.215 0.149 –0.308 –1.042 –2.417
Leu368 –0.737 0.012 0.108 –0.596 –1.214
Gly382 0.064 –4.685 0.894 –0.692 –4.420
Asn386 –1.308 –0.862 0.187 –1.005 –2.988
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Table 2. Energy Decomposition of the Complex of 5-HT 1A and Ginkgolide C.

residues van der Waals electrostatic polar solvation non-polar solvation total
Tyr96 –1.619 –0.508 0.252 –1.091 –2.965
Phe112 –0.740 –0.644 0.294 –0.620 –1.710
Ile113 –0.752 –0.329 0.295 –0.582 –1.368
Asp116 –0.238 –13.043 4.656 –0.876 –9.501
Thr188 –0.849 –1.545 0.817 –0.486 –2.063
Ile189 –0.920 0.390 –0.570 –0.467 –1.566
Phe361 –0.427 –0.188 0.115 –0.364 –0.864
Leu368 –1.122 0.404 –0.544 –1.015 –2.276
Gly382 –0.450 –1.148 0.562 –0.359 –1.394
Asn386 –1.081 –4.776 1.672 –1.037 –5.222
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2.4. Measurement of BDNF Concentration

Status epilepticus (SE) was induced by lithium–pilocarpine injection in adult male Wistar rats. After successfully developing epilepsy (with behavioral seizure scores of 4 and 5), we looked into the level of BDNF with/without GTTL, in which the expression of BDNF in the brain increases as a mechanism to promote the repair of damaged neurons.

As shown in Figure 4, the BDNF data had been collected for all six subject groups from their hippocampus tissues. Even though the veh/pilo model group was not being treated with any pharmacological agents, it still expressed a significantly higher amount of BDNF compared to the control group (veh/veh) (p < 0.01), which is consistent with the literature regarding the elevation of BDNF upon neurological damage. Animals in the positive control group (carbamazepine/pilo), which is treated with 108 mg/kg carbamazepine, showed no significant difference when compared to the model group. This finding could indicate that carbamazepine exerts its neurological repair effect through a mechanism that does not increase the expression of BDNF. The group treated with low-dose GTTL (GTTL 40 mg/kg/pilo) also showed no significant increase in BDNF when compared to the model group, which indicated that there was no significant therapeutic benefit compared to the model group. However, groups treated with middle-dose GTTL (GTTL 80 mg/kg/pilo) and high-dose GTTL (GTTL 160 mg/kg/pilo) showed a significant increase in the level of BDNF when compared to the model group (veh/pilo) (p < 0.01), which could indicate the effective treatment through the introduced agent.

Figure 4.

Figure 4

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Expression of BDNF in the hippocampus after treatments. As shown, the expression of BDNF is significantly higher in the model group (veh/pilo) compared to that of the control group (veh/veh). The expression of BDNF in the positive control group (carbamazepine/pilo) and low-dose GTTL treatment group (GTTL 40 mg/kg/pilo) showed no significant difference compared to that of the model group. The expression of BDNF in the middle-dose (GTTL 80 mg/kg/pilo) and high-dose (GTTL 160 mg/kg/pilo) GTTL treatment group is significantly higher than that of the model group. Symbols * or # indicates p < 0.05, ** or ## indicates p < 0.01, and *** or ### indicates p < 0.001.

Recently, some studies suggest that the occurrence of a reciprocal involvement of 5-HT receptor activation along with the hippocampal BDNF-increased expression can significantly ameliorate neurologic changes and reverse behavioral deficits in status epilepticus rats while improving cognitive function and alleviate neuronal injury. Therefore, we hypothesized that GTTL may have a positive effect on epilepsy prevention and treatment.

2.5. Electroencephalography Analysis

We then carried out animal studies to experimentally investigate our hypothesis. First, the electroencephalogram activity of rats was monitored. As shown in Figure 5, the electroencephalogram of rats in the control group (veh/veh, Figure 5) mainly consists of α or β waves with a small number of θ waves, without obvious rhythm. However, the model group (veh/pilo, Figure 5) showed diffuse epileptiform waves, mostly polyspike waves, synchronized sharp waves, and slow spine composite waves, with a large number of epileptiform discharges. The carbamazepine treatment group significantly decreased the number of spike waves and sharp waves. High dose (160 mg/kg, GTTL), middle dose (80 mg/kg, GTTL), and low dose (40 mg/kg, GTTL) treatment groups showed scattered spike waves and sharp waves. The number of spike waves and sharp waves decrease as the dose of GTTL increases, which can indicate a collation between GTTL and the recovery of neurological damage.

Figure 5.

Figure 5

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EEG recordings in GTTL-treated rats. EEG data collected after 30 days of treatment.

2.6. Morris Water Maze (MWM)

The spatial learning and memory ability of each rat was tested using MWM (5 consecutive days). Hallmarks include escape latency (Figure 6a), and swim pattern (Figure 7) were recorded and evaluated by a video tracking system for the performance analysis. After completion of the escape latency test, the platform was removed from the pool and replaced with a visible platform. The probe trial was then applied for all of the rats. Sequentially, the rats were placed in the pool at the same pole and allowed to swim for 2 min. The number of rats crossing the target quadrant was recorded, as shown in Figure 6b.

Figure 6.

Figure 6

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Escape latency and the total number of subjects crossing the targeted quadrant during the training trial in the Morris water maze (MWM). (a) Average escape latency of test subjects during the trails from day 1 to day 5. (b) Total number of subjects crossing the targeted quadrant with the platform on day 6 of the Morris water maze experiment. On day 6, rats were tested for a probe trial in which the platform was removed from the pool and replaced by a visible platform. Rats were placed in the pool at the same pole and allowed to swim for 2 min. Symbol * or # indicates p < 0.05, ** or ## indicates p < 0.01, *** or ### indicates p < 0.001.

Figure 7.

Figure 7

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Travel pattern of search strategy during the probe trial in MWM. An illustration of the movement of the test subjects during the Morris water maze before the subject reached the platform of the experiment.

First, the time to locate the escape latency (hidden platform) is shown in Figure 6a. The data demonstrated that on average, the control group animals rapidly learned to find the hidden platform, whereas the animals treated with Li-pilocarpine alone took the longest to reach the platform. Furthermore, the escape time (Figure 6a) decreased in all groups daily, which showed that the animals had learned to find the platform position. On the 3rd, 4th, and 5th day, the average latency to find the platform of rats administrated with GTTL was significantly reduced in a time-dependent and dose-dependent manner compared to the Li-pilocarpine group. On the other hand, the escape latency of the Li-pilocarpine group on the 3rd, 4th, and 5th day was significantly longer than that of the controls (p < 0.01).

During the probe trial session, as shown in Figure 7, rats from the control group (veh/veh), middle-dose group (80 mg/kg, GTTL), high-dose group (160 mg/kg, GTTL), and carbamazepine group found the platform mainly in a linear manner, while rats in the Li-pilocarpine group (veh/pilo) and low-dose group (40 mg/kg, GTTL) searched for the platform primarily in the margin with a random pattern. All in all, the experimental result showed that the treatment of GTTL can improve the escape latency in lithium–pilocarpine-induced status epilepticus rats, which could potentially indicate the improvement of learning and memory abilities.

2.7. Nissl Staining

The Nissl stain has proven to be a reliable marker for neuronal degeneration in the hippocampal CA3. Representative images of Nissl-stained hippocampal slices are presented in Figure 8. Intact cells can be found in the veh/veh group, and we observed a significant decrease of neuronal cells in the hippocampal slices in Li-pilocarpine group rats compared to that of the control. Based on the results, status epilepticus significantly increased necrotic death in the hippocampal CA3 region of the rats in the veh/pilo group, as compared with the rats in the control group (Figure 8a,b). However, with the GTTL treatment, neuronal damage was significantly reduced in the high-dose group, which suggested that GTTL could significantly decrease the neuronal loss in lithium–pilocarpine-induced status epilepticus rats (Figure 8e–g).

Figure 8.

Figure 8

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Nissl stain result of the hippocampal area of rats after status epilepticus. The stained area indicates the presence of neuronal cells. An increase in the density of neuronal cells can indicate the recovery of the brain from neurological damage. Red arrows indicate the histologic changes (×400).

3. Conclusions

In the present work, we focus on the studies of Ginkgo terpene trilactones (GTTL) and evaluate the effect of its extract (or crude drug) containing different constituents (or compounds) on spatial learning and memory. For the first time, our systems pharmacology analysis and molecular docking studies indicated that the GTTL may bind to 5-HT 1A/5-HT 1B/5-HT 2B, which may ameliorate neurologic changes. Moreover, our results also showed that GTTL increased the level of BDNF in the hippocampus of status epilepticus rats. Our experimental observations showed that GTTL not only protected rat cerebral hippocampal neurons against epilepsy but also improved the learning and memory ability and alleviated neuronal injury. Therefore, GTTL may be a potential lead candidate for epilepsy prevention or treatment. In addition, we are investigating the effects and functions of each constituent of GTTL, and the results will be published in the future.

4. Materials and Methods

4.1. Chemogenomics Knowledgebase and Computational Systems Pharmacology (CSP) Analyses

We have already constructed many different knowledgebases, including AlzPlatform (https://www.cbligand.org/AD/),27 hallucinogen-related knowledgebase (https://www.cbligand.org/hallucinogen/),28 and a drug abuse-associated GPCRs’ knowledgebase (DAKB-GPCRs) (https://www.cbligand.org/dakb-gpcrs/),29 that can be used for target prediction, off-target prediction, and CSP analyses between query compound(s) and the potential target proteins.

Moreover, several in-house chemoinformatics tools including TargetHunter, HTDocking, and Blood–Brain Barrier (BBB) Predictor30,31 were also integrated into the knowledgebases. In this work, we applied our Alzheimer Database (AlzPlatform), Hallucinogen-related knowledgebase, DAKB-GPCRs (https://www.cbligand.org/dakb-gpcrs/),29 and our reported computational tools to carry out the CSP-target mapping for GTTL, and the detailed protocol can be found in our recent publications.32,33 Especially, Cytoscape 3.4.0,34 a tool for analyzation and visualization, was used in the present work as described previously.32,33

4.2. Docking Study of Ligand Receptor

In this study, the homology model of 5-HT 1A was built by using the crystal structures 5-HT 1B (PDB:4IAQ/4IAR)35 and 5-HT 2B (PDB:4IB4).36

SYBYL-X 1.3 was used to predict the binding pocket(s) of 5-HT 1A. Then, the docking program Surflex-Dock GeomX was used to build the complex of 5-HT 1A-small molecules, in which the calculated score is expressed as −log (Kd).37,38 The detailed protocol and the parameters of docking can be found in our recent publications.37,38

4.3. Molecular Dynamics (MD) Simulation

The complexes of 5-HT 1A receptor-bound ginkgolide A/C were set up for MD simulation, in which the system included 0.15 M NaCl, ∼21 000 water molecules, lipids, and ions with a box size of 93 Å × 93 Å × 93 Å. In brief, protein, water molecules, and ligands were described by AMBER ff14SB force field,39 TIP3P water model,40 and (AM1-BCC) method41,42/GAFF in AMBER16.42

The simulations were conducted using the AMBER1643−45 package. The MD systems were first relaxed by a set of minimizations by removing the possible steric clashes. Two femtoseconds were set to the time step that was used for three different phases, including the heating phase, equilibrium phase, and the entire production phase. Especially, the temperature was constrained by Langevin dynamics with 2 ps–1 for collision frequency.46,47 Moreover, the long-range electrostatics was regulated by the reported Particle Mesh Ewald48,49 method, and 10 Å was set to the cutoff value of the real-space interactions. In addition, SHAKE algorithm50 was applied to constrain each hydrogen atom that is involved in covalent bonding. Finally, a 100 ns MD simulation was carried out for each system.

4.4. MM-GBSA Calculation

Snapshots (500) were evenly selected from the sampling phase for MM-GBSA51−57 binding energy decomposition analysis, using the following formula

4.4.

where EMM is the total energies of molecular mechanics in vacuo, including internal energies, electrostatic, and van der Waals. ΔESOL is the sum of ΔEGB (polar solvation energy) and ΔESA (nonpolar energy), in which the former energy term was calculated by the GB approximation model56,58 and the latter was computed by fitting SASA59 with the LCPO model.60,61 The MM-GBSA analysis can be used to investigate the contribution of each binding residue.62

4.5. Animals

Adult male Wistar rats, weighing 240–260 g, were purchased from the Zhejiang Academy of Medical Sciences Laboratory Animal Center (Hangzhou, China), license number: SCXK-20140003. All animals were housed and kept on a 12 h/12 h light/dark cycle in standard conditions with temperature and relative humidity set at 23 ± 2 °C and 55 ± 10%, respectively. All animals had free access to diet and water. Animal sacrifice at the end of the study was performed under deep anesthesia with 5% isoflurane. All experimental procedures were conducted in accordance with the Guide for the Care and Use of Laboratory Animals in the Zhejiang University of Technology, Hangzhou, China, and conformed to the National Institutes of Health Guide for Care and Use of Laboratory Animals (Publication No. 85-23, revised 1996).

4.6. Materials

G. biloba L. terpene trilactones (GTTLs) used in this study were obtained from the Zhejiang Conba Pharmaceutical Co., Ltd., China. The mixture included bilobalide, ginkgolide A, ginkgolide B, and ginkgolide C (Figure 9a). The representative chromatograms of the standard mixture and sample of GTTL are shown in Figure 9b,c. The chromatographic peaks were identified by comparing their retention times with reference compounds and spiking of samples with the reference compounds. The ratio of four ingredients in the mixture is 4:1:2:3 for ginkgolide A, ginkgolide B, ginkgolide C, and bilobalide, respectively, from the result of HPLC.

Figure 9.

Figure 9

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Structures of bilobalide, ginkgolide A, ginkgolide B, and ginkgolide C and the representative chromatograms of the standard mixture and sample of GTTL. (a) The molecular structure of GTTL: (A) ginkgolide A, (B) ginkgolide B, (C) ginkgolide C, and (D) bilobalide. (b) Chromatogram of the standard mixture. (c) Chromatogram of the sample GTTL mixture with ginkgolide C at 4.411, bilobalide at 5.283, ginkgolide A at 6.082, and ginkgolide B at 7.916.

4.7. Induction of Status Epilepticus Rats and GTTL Treatment

Status epilepticus (SE) was induced by lithium–pilocarpine injection as reported previously but with some modifications.63 Male Wistar rats were intraperitoneally injected with 127 mg/kg freshly prepared lithium chloride (Sigma). After 18–20 h, atropine (1 mg/kg) was intraperitoneally administered 30 min prior to the intraperitoneal injection of pilocarpine hydrochloride (20 mg/kg) (Sigma). If no seizure was determined after 30 min, the animals were continuously injected with 10 mg/kg pilocarpine every 30 min until stage 4 or 5 seizure was presented. Behavioral seizures were scored according to the Racine scale64 with minor modifications as follows: without any detectable response (stage 0); mouth and face twitching (stage 1); mouth and face twitching and head nodding (stage 2); mouth and face twitching, head nodding, and forelimb twitching (stage 3); mouth and face twitching, head nodding, forelimb twitching, and rearing (stage 4); mouth and face twitching, head nodding, forelimb twitching, rearing and loss of balance (stage 5). The status epilepticus (SE) could last for up to 60 min before being interrupted via administration of 10% chloral hydrate (Sigma) (300 mg/kg), which terminates the attack. The behavior changes of animals in both control and model groups were carefully examined each day after injection. Then, all animals were monitored by video to assure the development of spontaneous epileptic activities, and they got into the chronic epileptic phase after 10 days. The control rats received an injection of the same amount of normal saline as a replacement for pilocarpine (veh/veh 0.1% dimethyl sulfoxide in saline, n = 10). The rats that successfully developed spontaneous epileptic were divided into the following experimental groups for a period of 4 weeks: group I (veh/pilo, n = 10) was treated with vehicle; group II (GTTL 40/pilo, n = 10), group III (GTTL 80/pilo, n = 10), and group IV (GTTL 160/pilo, n = 10) were treated with GTTL at 40, 80, and 160 mg/kg, p.o., respectively; group V (carbamazepine/pilo, n = 10) was treated with 108 mg/kg carbamazepine. (Li-pilocarpine treatment with administrated orally and carbamazepine daily.)

4.8. Enzyme-Linked Immunosorbent Assay (ELISA) for the Determination of BDNF Level

The BDNF levels in hippocampal homogenates were determined by ELISA assay. Briefly, 96-well plates were coated for 24 h with the sample diluents. The standard curve ranged from 7.8 to 500 pg/mL of BDNF. Then, the plates were washed four times with the sample diluents. After washing, a monoclonal anti-BNDF rabbit antibody was sequentially added to each well, followed by an incubation of 2 h at room temperature. After being completely washed, another antibody of peroxidase-conjugated antirabbit (1:200) was added to the plates for 1 h incubation. Finally, after adding the substrate, stop solution, and streptavidin enzyme, the levels of BDNF were determined by measuring the absorbance at 450 nm using a plate reader. Data were expressed as pg BDNF/mg total protein calculated against the standard curve.

4.9. Electroencephalography Analysis

At the end of the experimental period, the electroencephalogram activity of rats from the above groups was monitored through a POWERLAB System (AD Instruments, Australia) for at least 2 h. Briefly, under 5% isoflurane anesthesia, the rat was placed on a stereotaxic frame, and recording electrodes were implanted over the hippocampus. From these electrode placements, electroencephalography signals were taken.

4.10. Morris Water Maze

After 4 weeks, the cognitive impairments in spatial learning and memory formation of all of the rats were investigated by Morris water maze (MWM) test.65 The MWM was a circular pool with 180 cm in diameter and 60 cm in height that filled to a depth of 35 cm with water (23–26.8 °C) and was divided into four quadrants. Animals were trained to seek an escape platform (25 cm2 Plexiglas square, 2 cm below the surface of the water in one of four quadrants) placed in the center of a fixed quadrant (goal quadrant) onto which rats could escape from the water. Rats were submitted to a training of 5 consecutive days in the MWM, and then they were put in randomly chosen quadrants of the pool to escape onto the goal platform. On locating the platform, the rats could remain there for 30 s before returning to its cage. If the rat failed to find the platform in 2 min, the time was assigned as 2 min. The rat was then manually guided to the platform where it stayed on for 30 s. Hallmarks that included escape latency and swim patterns were recorded and evaluated utilizing a video tracking system for the performance analysis. After completion of the escape latency test, the platform was removed from the pool and replaced with a visible platform. Then, the probe trial was applied for all of the rats. Sequentially, rats were placed in the pool at the same pole and allowed to swim for 2 min. The number of rats crossing the target quadrant was recorded.

4.11. Nissl Staining

Rats from the above groups were anesthetized with 5% isoflurane. Sequentially, they were perfused through the heart with 4% paraformaldehyde (PFA). The brains were isolated immediately and fixed in 4% PFA for 24 h, followed by submerging in 20 and 30% sucrose for 48 h.66 Then, the hippocampus of the brain was cut into 25-μm-thick sections by a freezing microtome (ThermoFisher), and the sections were then stained using Nissl staining solution (Beyotime Institute of Biotechnology, Nantong, China) according to the manufacturer’s instructions. At the end of staining, the neuronal survival and loss of Nissl bodies in the hippocampus were observed using a light microscope (Nikon, Tokyo, Japan).

4.12. Statistical Analysis

All data are demonstrated as mean ± SD and analyzed using SPSS 13.0 software. The significance of differences analyses between different groups was analyzed by one-way analysis of variance (ANOVA). Group differences were calculated using t-test, and * or # indicates p < 0.05, ** or ## indicates p < 0.01, and *** or ### indicates p < 0.001.

Acknowledgments

This work was done while Dr. Yan Chen was a visiting scholar at the University of Pittsburgh (9/30/2017–9/28/2018). The experimental work was completed in Chen and Shan’s Laboratory, while the computational work was finished in Xie’s Center/Laboratory. The authors would like to acknowledge the funding support to the Shan Laboratory from the National Natural Science Foundation of China (81872777) and the funding support to the Xie laboratory from the NIH NIDA (P30 DA035778A1). The authors would also like to acknowledge the support from the China Scholarship Council (CSC, No. 201608330382).

Author Contributions

∇ Y.C. and Z.F. contributed equally.

The authors declare no competing financial interest.

References

  1. Belviranlı M.; Okudan N. The effects of Ginkgo biloba extract on cognitive functions in aged female rats: the role of oxidative stress and brain-derived neurotrophic factor. Behav. Brain Res. 2015, 278, 453–461. 10.1016/j.bbr.2014.10.032. [DOI] [PubMed] [Google Scholar]
  2. Tyler W. J.; Alonso M.; Bramham C. R.; Pozzo-Miller L. D. From acquisition to consolidation: on the role of brain-derived neurotrophic factor signaling in hippocampal-dependent learning. Learn. Mem. 2002, 9, 224–237. 10.1101/lm.51202. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Babb T. L.; Kupfer W.; Pretorius J.; Crandall P.; Levesque M. Synaptic reorganization by mossy fibers in human epileptic fascia dentata. Neuroscience 1991, 42, 351–363. 10.1016/0306-4522(91)90380-7. [DOI] [PubMed] [Google Scholar]
  4. Blümcke I.; Beck H.; Lie A. A.; Wiestler O. D. Molecular neuropathology of human mesial temporal lobe epilepsy. Epilepsy Res. 1999, 36, 205–223. 10.1016/S0920-1211(99)00052-2. [DOI] [PubMed] [Google Scholar]
  5. Venturin G. T.; Greggio S.; Marinowic D. R.; Zanirati G.; Cammarota M.; Machado D. C.; DaCosta J. C. Bone marrow mononuclear cells reduce seizure frequency and improve cognitive outcome in chronic epileptic rats. Life Sci. 2011, 89, 229–234. 10.1016/j.lfs.2011.06.006. [DOI] [PubMed] [Google Scholar]
  6. Laxer K. D.; Trinka E.; Hirsch L. J.; Cendes F.; Langfitt J.; Delanty N.; Resnick T.; Benbadis S. R. The consequences of refractory epilepsy and its treatment. Epilepsy Behav. 2014, 37, 59–70. 10.1016/j.yebeh.2014.05.031. [DOI] [PubMed] [Google Scholar]
  7. Lim T. K.Edible Medicinal and Non-Medicinal Plants; Springer, 2012; Vol. 1. [Google Scholar]
  8. Yin Y.; Ren Y.; Wu W.; Wang Y.; Cao M.; Zhu Z.; Wang M.; Li W. Protective effects of bilobalide on Aβ25–35 induced learning and memory impairments in male rats. Pharmacol., Biochem. Behav. 2013, 106, 77–84. 10.1016/j.pbb.2013.03.005. [DOI] [PubMed] [Google Scholar]
  9. Augustin S.; Rimbach G.; Augustin K.; Schliebs R.; Wolffram S.; Cermak R. Effect of a short-and long-term treatment with Ginkgo biloba extract on amyloid precursor protein levels in a transgenic mouse model relevant to Alzheimer’s disease. Arch. Biochem. Biophys. 2009, 481, 177–182. 10.1016/j.abb.2008.10.032. [DOI] [PubMed] [Google Scholar]
  10. Luo Y.; Smith J. V.; Paramasivam V.; Burdick A.; Curry K. J.; Buford J. P.; Khan I.; Netzer W. J.; Xu H.; Butko P. Inhibition of amyloid-β aggregation and caspase-3 activation by the Ginkgo biloba extract EGb761. Proc. Natl. Acad. Sci. U.S.A. 2002, 99, 12197–12202. 10.1073/pnas.182425199. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Leistner E.; Drewke C. Ginkgo biloba and ginkgotoxin. J. Nat. Prod. 2010, 73, 86–92. 10.1021/np9005019. [DOI] [PubMed] [Google Scholar]
  12. Mazumder A. G.; Sharma P.; Patial V.; Singh D. Ginkgo biloba L. attenuates spontaneous recurrent seizures and associated neurological conditions in lithium-pilocarpine rat model of temporal lobe epilepsy through inhibition of mammalian target of rapamycin pathway hyperactivation. J. Ethnopharmacol. 2017, 204, 8–17. 10.1016/j.jep.2017.03.060. [DOI] [PubMed] [Google Scholar]
  13. van Beek T. A. Chemical analysis of Ginkgo biloba leaves and extracts. J. Chromatogr. A 2002, 967, 21–55. 10.1016/S0021-9673(02)00172-3. [DOI] [PubMed] [Google Scholar]
  14. Ude C.; Schubert-Zsilavecz M.; Wurglics M. Ginkgo biloba extracts: a review of the pharmacokinetics of the active ingredients. Clin. Pharmacokinet. 2013, 52, 727–749. 10.1007/s40262-013-0074-5. [DOI] [PubMed] [Google Scholar]
  15. Li Z.-Y.; Chung Y. H.; Shin E.-J.; Dang D.-K.; Jeong J. H.; Ko S. K.; Nah S.-Y.; Baik T. G.; Jhoo J. H.; Ong W.-Y.; Nabeshima T.; Kim H.-C. YY-1224, a terpene trilactone-strengthened Ginkgo biloba, attenuates neurodegenerative changes induced by β-amyloid (1-42) or double transgenic overexpression of APP and PS1 via inhibition of cyclooxygenase-2. J. Neuroinflammation 2017, 14, 94 10.1186/s12974-017-0866-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Lu X.; Li C.; Liu T.; Ke H.; Gong X.; Wang Q.; Zhang J.; Fan X. Chemical analysis, pharmacological activity and process optimization of the proportion of bilobalide and ginkgolides in Ginkgo biloba extract. J. Pharm. Biomed. Anal. 2018, 160, 46–54. 10.1016/j.jpba.2018.07.037. [DOI] [PubMed] [Google Scholar]
  17. Choi M. S.; Kim J.-K.; Kim D.-H.; Yoo H. H. Effects of Gut Microbiota on the Bioavailability of Bioactive Compounds from Ginkgo Leaf Extracts. Metabolites 2019, 9, 132 10.3390/metabo9070132. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Chan W.-H. Ginkgolides induce apoptosis and decrease cell numbers in mouse blastocysts. Biochem. Biophys. Res. Commun. 2005, 338, 1263–1267. 10.1016/j.bbrc.2005.10.085. [DOI] [PubMed] [Google Scholar]
  19. Shiao N.-H.; Chan W.-H. Injury effects of ginkgolide B on maturation of mouse oocytes, fertilization, and fetal development in vitro and in vivo. Toxicol. Lett. 2009, 188, 63–69. 10.1016/j.toxlet.2009.03.004. [DOI] [PubMed] [Google Scholar]
  20. Gaweska H.; Fitzpatrick P. F. Structures and mechanism of the monoamine oxidase family. Biomol. Concepts 2011, 2, 365–377. 10.1515/BMC.2011.030. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Higuchi Y.; Soga T.; Parhar I. S. Potential Roles of microRNAs in the Regulation of Monoamine Oxidase A in the Brain. Front. Mol. Neurosci. 2018, 11, 339 10.3389/fnmol.2018.00339. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Binda C.; Hubálek F.; Li M.; Herzig Y.; Sterling J.; Edmondson D. E.; Mattevi A. Crystal structures of monoamine oxidase B in complex with four inhibitors of the N-propargylaminoindan class. J. Med. Chem. 2004, 47, 1767–1774. 10.1021/jm031087c. [DOI] [PubMed] [Google Scholar]
  23. Hannon J.; Hoyer D. Molecular biology of 5-HT receptors. Behav. Brain Res. 2008, 195, 198–213. 10.1016/j.bbr.2008.03.020. [DOI] [PubMed] [Google Scholar]
  24. Wang X.; Li J.; Dong G.; Yue J. The endogenous substrates of brain CYP2D. Eur. J. Pharmacol. 2014, 724, 211–218. 10.1016/j.ejphar.2013.12.025. [DOI] [PubMed] [Google Scholar]
  25. Hunziker W.; Spiess M.; Semenza G.; Lodish H. F. The sucrase-isomaltase complex: primary structure, membrane-orientation, and evolution of a stalked, intrinsic brush border protein. Cell 1986, 46, 227–234. 10.1016/0092-8674(86)90739-7. [DOI] [PubMed] [Google Scholar]
  26. Reijnders M. R.; Ansor N. M.; Kousi M.; Yue W. W.; Tan P. L.; Clarkson K.; Clayton-Smith J.; Corning K.; Jones J. R.; Lam W. W. K.; et al. RAC1 missense mutations in developmental disorders with diverse phenotypes. Am. J. Hum. Genet. 2017, 101, 466–477. 10.1016/j.ajhg.2017.08.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Liu H.; Wang L.; Lv M.; Pei R.; Li P.; Pei Z.; Wang Y.; Su W.; Xie X.-Q. AlzPlatform: an Alzheimer’s disease domain-specific chemogenomics knowledgebase for polypharmacology and target identification research. J. Chem. Inf. Model. 2014, 54, 1050–1060. 10.1021/ci500004h. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Xu X.; Ma S.; Feng Z.; Hu G.; Wang L.; Xie X.-Q. Modelling, Chemogenomics knowledgebase and systems pharmacology for hallucinogen target identification—Salvinorin A as a case study. J. Mol. Graphics Modell. 2016, 70, 284–295. 10.1016/j.jmgm.2016.08.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Chen M.; Jing Y.; Wang L.; Feng Z.; Xie X.-Q. DAKB-GPCRs: An Integrated Computational Platform for Drug Abuse Related GPCRs. J. Chem. Inf. Model. 2019, 59, 1283–1289. 10.1021/acs.jcim.8b00623. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Wang L.; Ma C.; Wipf P.; Liu H.; Su W.; Xie X.-Q. TargetHunter: an in silico target identification tool for predicting therapeutic potential of small organic molecules based on chemogenomic database. AAPS J. 2013, 15, 395–406. 10.1208/s12248-012-9449-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Xue Y.; Feng Z.-w.; Li X.-y.; Hu Z.-h.; Xu Q.; Wang Z.; Cheng J.-h.; Shi H.-t.; Wang Q.-b.; Wu H.-y.; et al. The efficacy and safety of cilostazol as an alternative to aspirin in Chinese patients with aspirin intolerance after coronary stent implantation: a combined clinical study and computational system pharmacology analysis. Acta Pharmacol. Sin. 2018, 39, 205. 10.1038/aps.2017.85. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Zhang H.; Ma S.; Feng Z.; Wang D.; Li C.; Cao Y.; Chen X.; Liu A.; Zhu Z.; Zhang J.; et al. Cardiovascular disease chemogenomics knowledgebase-guided target identification and drug synergy mechanism study of an herbal formula. Sci. Rep. 2016, 6, 33963 10.1038/srep33963. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Cheng J.; Wang S.; Lin W.; Wu N.; Wang Y.; Chen M.; Xie X.-Q.; Feng Z. Computational Systems Pharmacology-Target Mapping for Fentanyl-Laced Cocaine Overdose. ACS Chem. Neurosci. 2019, 10, 3486–3499. 10.1021/acschemneuro.9b00109. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Shannon P.; Markiel A.; Ozier O.; Baliga N. S.; Wang J. T.; Ramage D.; Amin N.; Schwikowski B.; Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003, 13, 2498–2504. 10.1101/gr.1239303. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Wang C.; Jiang Y.; Ma J.; Wu H.; Wacker D.; Katritch V.; Han G. W.; Liu W.; Huang X.-P.; Vardy E.; et al. Structural basis for molecular recognition at serotonin receptors. Science 2013, 340, 610–614. 10.1126/science.1232807. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Wacker D.; Wang C.; Katritch V.; Han G. W.; Huang X.-P.; Vardy E.; McCorvy J. D.; Jiang Y.; Chu M.; Siu F. Y.; et al. Structural features for functional selectivity at serotonin receptors. Science 2013, 340, 615–619. 10.1126/science.1232808. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Feng Z.; Alqarni M. H.; Yang P.; Tong Q.; Chowdhury A.; Wang L.; Xie X.-Q. Modeling, Molecular Dynamics Simulation, and Mutation Validation for Structure of Cannabinoid Receptor 2 Based on Known Crystal Structures of GPCRs. J. Chem. Inf. Model. 2014, 54, 2483–2499. 10.1021/ci5002718. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Feng Z.; Kochanek S.; Close D.; Wang L.; Srinivasan A.; Almehizia A. A.; Iyer P.; Xie X.-Q.; Johnston P. A.; Gold B. Design and activity of AP endonuclease-1 inhibitors. J. Chem. Biol. 2015, 8, 79–93. 10.1007/s12154-015-0131-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Maier J. A.; Martinez C.; Kasavajhala K.; Wickstrom L.; Hauser K. E.; Simmerling C. ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB. J. Chem. Theory Comput. 2015, 11, 3696–3713. 10.1021/acs.jctc.5b00255. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Jorgensen W. L.; Chandrasekhar J.; Madura J. D.; Impey R. W.; Klein M. L. Comparison of simple potential functions for simulating liquid water. J. Chem. Phys. 1983, 79, 926–935. 10.1063/1.445869. [DOI] [Google Scholar]
  41. Jakalian A.; Jack D. B.; Bayly C. I. Fast, efficient generation of high-quality atomic charges. AM1-BCC model: II. Parameterization and validation. J. Comput. Chem. 2002, 23, 1623–1641. 10.1002/jcc.10128. [DOI] [PubMed] [Google Scholar]
  42. Wang J.; Wolf R. M.; Caldwell J. W.; Kollman P. A.; Case D. A. Development and testing of a general amber force field. J. Comput. Chem. 2004, 25, 1157–1174. 10.1002/jcc.20035. [DOI] [PubMed] [Google Scholar]
  43. Götz A. W.; Williamson M. J.; Xu D.; Poole D.; Le Grand S.; Walker R. C. Routine microsecond molecular dynamics simulations with AMBER on GPUs. 1. Generalized born. J. Chem. Theory Comput. 2012, 8, 1542–1555. 10.1021/ct200909j. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Salomon-Ferrer R.; Götz A. W.; Poole D.; Le Grand S.; Walker R. C. Routine microsecond molecular dynamics simulations with AMBER on GPUs. 2. Explicit solvent particle mesh Ewald. J. Chem. Theory Comput. 2013, 9, 3878–3888. 10.1021/ct400314y. [DOI] [PubMed] [Google Scholar]
  45. Case D.; Cerutti D.; Cheatham T.; Darden T.; Duke R.; Giese T.; Gohlke H.; Goetz A.; Greene D.; Homeyer N.. AMBER 2016; University of California: San Francisco, 2016.
  46. Loncharich R. J.; Brooks B. R.; Pastor R. W. Langevin dynamics of peptides: The frictional dependence of isomerization rates of N-acetylalanyl-N′-methylamide. Biopolymers 1992, 32, 523–535. 10.1002/bip.360320508. [DOI] [PubMed] [Google Scholar]
  47. Izaguirre J. A.; Catarello D. P.; Wozniak J. M.; Skeel R. D. Langevin stabilization of molecular dynamics. J. Chem. Phys. 2001, 114, 2090–2098. 10.1063/1.1332996. [DOI] [Google Scholar]
  48. Darden T.; York D.; Pedersen L. Particle mesh Ewald: An N· log (N) method for Ewald sums in large systems. J. Chem. Phys. 1993, 98, 10089–10092. 10.1063/1.464397. [DOI] [Google Scholar]
  49. Essmann U.; Perera L.; Berkowitz M. L.; Darden T.; Lee H.; Pedersen L. G. A smooth particle mesh Ewald method. J. Chem. Phys. 1995, 103, 8577–8593. 10.1063/1.470117. [DOI] [Google Scholar]
  50. Ryckaert J.-P.; Ciccotti G.; Berendsen H. J. Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. J. Comput. Phys. 1977, 23, 327–341. 10.1016/0021-9991(77)90098-5. [DOI] [Google Scholar]
  51. Hawkins G. D.; Cramer C. J.; Truhlar D. G. Parametrized models of aqueous free energies of solvation based on pairwise descreening of solute atomic charges from a dielectric medium. J. Phys. Chem. A 1996, 100, 19824–19839. 10.1021/jp961710n. [DOI] [Google Scholar]
  52. Kollman P. A.; Massova I.; Reyes C.; Kuhn B.; Huo S.; Chong L.; Lee M.; Lee T.; Duan Y.; Wang W.; et al. Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models. Acc. Chem. Res. 2000, 33, 889–897. 10.1021/ar000033j. [DOI] [PubMed] [Google Scholar]
  53. Sun H.; Duan L.; Chen F.; Liu H.; Wang Z.; Pan P.; Zhu F.; Zhang J. Z.; Hou T. Assessing the performance of MM/PBSA and MM/GBSA methods. 7. Entropy effects on the performance of end-point binding free energy calculation approaches. Phys. Chem. Chem. Phys. 2018, 20, 14450–14460. 10.1039/C7CP07623A. [DOI] [PubMed] [Google Scholar]
  54. Sun H.; Li Y.; Shen M.; Tian S.; Xu L.; Pan P.; Guan Y.; Hou T. Assessing the performance of MM/PBSA and MM/GBSA methods. 5. Improved docking performance using high solute dielectric constant MM/GBSA and MM/PBSA rescoring. Phys. Chem. Chem. Phys. 2014, 16, 22035–22045. 10.1039/C4CP03179B. [DOI] [PubMed] [Google Scholar]
  55. Sun H.; Li Y.; Tian S.; Xu L.; Hou T. Assessing the performance of MM/PBSA and MM/GBSA methods. 4. Accuracies of MM/PBSA and MM/GBSA methodologies evaluated by various simulation protocols using PDBbind data set. Phys. Chem. Chem. Phys. 2014, 16, 16719–16729. 10.1039/C4CP01388C. [DOI] [PubMed] [Google Scholar]
  56. Tsui V.; Case D. A. Theory and Applications of the Generalized Born Solvation Model in Macromolecular Simulations. Biopolymers 2000, 56, 275–291. . [DOI] [PubMed] [Google Scholar]
  57. Chen F.; Liu H.; Sun H.; Pan P.; Li Y.; Li D.; Hou T. Assessing the performance of the MM/PBSA and MM/GBSA methods. 6. Capability to predict protein–protein binding free energies and re-rank binding poses generated by protein–protein docking. Phys. Chem. Chem. Phys. 2016, 18, 22129–22139. 10.1039/C6CP03670H. [DOI] [PubMed] [Google Scholar]
  58. Bashford D.; Case D. A. Generalized Born Models of Macromolecular Solvation Effects. Annu. Rev. Phys. Chem. 2000, 51, 129–152. 10.1146/annurev.physchem.51.1.129. [DOI] [PubMed] [Google Scholar]
  59. Sitkoff D.; Sharp K. A.; Honig B. Accurate Calculation of Hydration Free Energies using Macroscopic Solvent Models. J. Phys. Chem. A 1994, 98, 1978–1988. 10.1021/j100058a043. [DOI] [Google Scholar]
  60. Still W. C.; Tempczyk A.; Hawley R. C.; Hendrickson T. Semianalytical Treatment of Solvation for Molecular Mechanics and Dynamics. J. Am. Chem. Soc. 1990, 112, 6127–6129. 10.1021/ja00172a038. [DOI] [Google Scholar]
  61. Weiser J.; Shenkin P. S.; Still W. C. Approximate Atomic Surfaces from Linear Combinations of Pairwise Overlaps (LCPO). J. Comput. Chem. 1999, 20, 217–230. . [DOI] [Google Scholar]
  62. Hu J.; Feng Z.; Ma S.; Zhang Y.; Tong Q.; Alqarni M. H.; Gou X.; Xie X.-Q. Difference and influence of inactive and active states of cannabinoid receptor subtype CB2: from conformation to drug discovery. J. Chem. Inf. Model. 2016, 56, 1152–1163. 10.1021/acs.jcim.5b00739. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Hu K.; Zhang C.; Long L.; Long X.; Feng L.; Li Y.; Xiao B. Expression profile of microRNAs in rat hippocampus following lithium–pilocarpine-induced status epilepticus. Neurosci. Lett. 2011, 488, 252–257. 10.1016/j.neulet.2010.11.040. [DOI] [PubMed] [Google Scholar]
  64. Racine R. J. Modification of seizure activity by electrical stimulation: II. Motor seizure. Electroencephalogr. Clin. Neurophysiol. 1972, 32, 281–294. 10.1016/0013-4694(72)90177-0. [DOI] [PubMed] [Google Scholar]
  65. Granon S.; Poucet B. Medial prefrontal lesions in the rat and spatial navigation: evidence for impaired planning. Behav. Neurosci. 1995, 109, 474. 10.1037/0735-7044.109.3.474. [DOI] [PubMed] [Google Scholar]
  66. Yi F.; Xu H.; Long L.; Feng L.; Zhou L.; Li S.; Jiang H.; Xiao B. Vulnerability of calbindin-positive interneurons to status epilepticus varies in different regions of rat hippocampus. Neurochem. J. 2014, 8, 306–310. 10.1134/S1819712414040126. [DOI] [Google Scholar]

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