Fifteen compounds related to ameltolide with sodium channel inhibitory activity were subjected to a molecular docking study. The chemical structures of all compounds were built using the program HyperChem and conformational studies were performed with a semiempirical method followed by the PM3 method. A docking study was performed using the program AutoDock on all the compounds. To confirm the binding mode of inhibitors, molecular dynamics simulations were performed using GROMACS 4.5.5, based upon the docked conformation of ameltolide.
Turk J Chem (2015) 39: 306 316 ă ITAK ˙ c TUB ⃝ Turkish Journal of Chemistry http://journals.tubitak.gov.tr/chem/ doi:10.3906/kim-1402-37 Research Article Molecular docking analysis and molecular dynamics simulation study of ameltolide analogous as a sodium channel blocker Maryam IMAN1 , Atefeh SAADABADI2 , Asghar DAVOOD2,∗ Chemical Injuries Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran Department of Medicinal Chemistry, Pharmaceutical Sciences Branch, Islamic Azad University, Tehran, Iran Received: 20.02.2014 • Accepted/Published Online: 03.12.2014 • Printed: 30.04.2015 Abstract:Fifteen compounds related to ameltolide with sodium channel inhibitory activity were subjected to a molecular docking study The chemical structures of all compounds were built using the program HyperChem and conformational studies were performed with a semiempirical method followed by the PM3 method A docking study was performed using the program AutoDock on all the compounds To confirm the binding mode of inhibitors, molecular dynamics simulations were performed using GROMACS 4.5.5, based upon the docked conformation of ameltolide The docking analyses indicated that these compounds interacted mainly with residues II-S6 and III-S6 of NaV1.2 by making hydrogen bonds and ( π − π) interactions with domains I, III, and IV in the channel’s inner pore Our docking study reveals that amide linker plays a major role in the drug–receptor interaction The results of molecular dynamic simulations confirmed the binding mode of ligands, the accuracy of docking, and the reliability of active conformations obtained by AutoDock Key words: Ameltolide, molecular dynamics simulation, sodium channel Introduction Epilepsy is a serious neurological disorder that 1% of the world’s population is affected by 1,2 and among them 30% of sufferers have uncontrolled seizures Most antiepileptic drugs are associated with adverse effects, such as sedation, ataxia, and weight loss (e.g., topiramate) or weight gain (e.g., valproate, tiagabine, and vigabatrin) Rare adverse effects can be life threatening, such as rashes leading to Stevens–Johnson syndrome (e.g., lamotrigine) or aplastic anemia (e.g., felbamate) 2,3 Therefore, the development of safer and more effective new antiepileptic drugs (AEDs) is necessary 1−5 Sodium channels are one of the best targets in the treatment of epilepsy Neuronal voltage-gated sodium channels (NVSCs) play an important role in the production and spread of action potentials in neurons and other excitable cells Therefore, NVSC blocking agents constitute a clinically important class of drugs used in the treatment of neurological disorders NVSCs usually include an alpha subunit that organizes the ion conduction pore and one to two beta subunits The alpha subunit has four repeat domains, marked I to IV, each containing six membrane-spanning regions, marked S1 to S6 The family of sodium channels has nine known members The proteins of these channels are labeled Nav1.1 to Nav1.9 7,8 Docking studies are used at different stages in drug discovery such as in prediction of the docked structure of a ligand–receptor complex and also to rank ligand molecules based upon their binding energy Docking ∗ Correspondence: 306 adavood2001@yahoo.com IMAN et al./Turk J Chem protocols aid in elucidation of the most energetically favorable binding pose of a ligand to its receptor Our work is based on one of the potent AEDs, ameltolide, which previously was investigated by Eli Lilly, originally emerging from the laboratories of Clark and coworkers 9−15 These groups of investigators isolated the promising 4-aminobenzamide pharmacophore, which subsequently led to the fruitful design of several new and potent anticonvulsant compounds In this paper, we report the molecular modeling and drug–receptor interaction profile of 15 compounds structurally related to ameltolide (Figure 1) that had been designed and synthesized already It was confirmed that this type of ligand acts as a sodium channel blocker, 16 and so we used a model of the open pore of the sodium channel as a receptor This open pore model was developed recently based on homology modeling of the crystal structures of the K channel 17,18 O H3C CH3 CH3 O R R H3C R S NH NH NH NH NH H3C CH3 CH3 CH3 I-III IV-VI O H3C VII O H3C N CH3 CH3 O N NH N R R O H3C O H3C VIII-X and XIII, XIV CH3 CH3 O R XI, XII XV Comp R Comp R Comp R Comp R Comp R I NO2 IV H VII NO2 X NH2 XIII OH II NH2 V NO2 VIII H XI NO2 XIV Cl III OH VI NH2 IX NO2 XII NH2 XV H Figure The structure of docked compounds I–XV Results and discussion Flexible docking of all data sets used for the computational study 19 was carried out on the active site of the open pore of the sodium channel The lowest energy and maximum number of conformations per cluster were set as the criteria to predict the binding modes of the compounds Our docking results (Figure 2a) indicate that the CO–NH moiety of ameltolide and its benzanilides derivatives bind to the sodium channel in the trans form and this form is stable in the molecular dynamic simulation (Figure 2b) Based on the procedure explained in the experimental section, the binding affinity of the docked molecules was evaluated by binding energy, docked energy, calculated inhibition constants (calc K i ) , and hydrogen bonds in addition to the hydrophobic interactions at the channel pocket The poses of all compounds are presented in the Table 307 IMAN et al./Turk J Chem Figure (a) Drug–receptor interaction after docking (before MD simulation) (b) Average structure based upon the equilibration of the ameltolide in the last ns, drug-receptor interaction after 10 ns MD simulation Table Docking results of ameltolide analogues by using of AutoDock software (version 4) Comp I II III IV V VI VII VIII IX X XI XII XIII XIV XV PHE a BEa –6.81 –5.81 –5.69 –6.82 –6.5 –6.58 –5.42 –5.69 –6.7 –6.07 –6.33 –5.87 –5.91 –5.94 –6.51 –5.83 LEb –0.34 –0.32 –0.32 –0.36 –0.3 –0.33 –0.26 –0.3 –0.3 –0.3 –0.29 –0.29 –0.3 –0.3 –0.31 –0.31 Ki (µM)c 10.25 55.49 67.72 9.99 17.07 15.0 106.8 67.74 12.23 35.45 22.86 49.94 46.59 44.61 17.0 53.37 IE d –7.7 –6.4 –6.28 –7.42 –7.4 –7.48 –6.91 –5.99 –7.0 –6.37 –6.93 –6.46 –6.21 –6.23 –6.8 –6.43 VEe –7.62 –6.38 –6.25 –6.28 –7.42 –6.11 –6.83 –5.94 –7.01 –6.34 –6.7 –6.43 –6.13 –6.21 –6.69 –6.38 EEf –0.08 –0.02 –0.04 –1.14 0.02 –1.37 –0.08 –0.05 0.01 –0.03 –0.23 –0.03 –0.08 –0.02 –0.11 –0.04 TIEg –0.65 –0.43 –0.5 –0.2 –0.48 –0.16 –0.49 –0.47 –0.43 –0.44 –0.47 –0.44 –0.42 –0.5 –0.29 –0.71 TEh 0.89 0.6 0.6 0.6 0.89 0.89 1.49 0.3 0.3 0.3 0.6 0.6 0.3 0.3 0.3 0.60 UEi –0.65 –0.43 –0.5 –0.2 –0.48 –0.16 –0.49 –0.47 –0.43 –0.44 –0.47 –0.44 –0.42 –0.5 –0.29 –0.71 Log P 3.70 2.96 3.46 1.72 1.68 0.94 5.03 3.61 3.56 2.82 3.56 2.82 3.32 4.13 1.59 2.08 DEj –8.35 –6.83 –6.78 –7.62 –7.88 –7.64 –7.4 –6.46 –7.43 –6.81 –7.4 –6.9 –6.63 –6.73 –7.09 –6.74 BE = The predicted binding energy (kcal/mol) is the sum of intermolecular energy and torsional free energy, b LE = Ligand efficiency, Inhibition constant (Ki) = exp (deltaG × 1000)/(Rcal × TK), where deltaG is the docking energy, c Rcalis 1.98719, and TK is 298.15, d IE = Intermolecular energy is sum of Vdw-hb-desolv-energy and Electrostatic-energy, e VE =Vdw-hb-desolv energy, f EE = electrostatic energy, g TIE = total internal energy, h TE = torsional energy, i UE = unbound energy, j DE = Docking energy is the sum of intermolecular energy and ligand’s internal energy 308 IMAN et al./Turk J Chem The predicted binding and docked energies are the sum of the intermolecular energy and the torsional free-energy penalty, and the docking ligand’s internal energy, respectively, and the inhibition constant (K i ) is calculated in AutoDock4 as follows: Ki = exp(∆G × 1000)/(Rcal × T K), (1) where ∆ G is the docking energy, Rcal is 1.98719, and TK is 298 20−24 Our docking results reveal that, based on the predicted binding energy, compounds IV, I, IX, VI, XV, V, XI, X, XIV, XIII, and XII with –6.82, –6.81, –6.7, –6.58, –6.51, –6.5 , –6.33, –6.07, –5.94, –5.91, and –5.87 kcal/mol binding energy, respectively, are more potent than phenytoin as a reference drug, with –5.83 kcal/mol binding energy According to the K i , compounds IV, I, IX, XI, XV, V, XI, X, XIV, XIII, and XII with 9.99, 10.25, 12.23, 15.0, 17.0, 17.07, 22.86, 35.45, 44.61, 46.59, and 49.94 µ M inhibition constant can inhibit the enzyme more efficiently when compared to phenytoin with 53.37 µ M inhibition constant Compounds VII, III, VIII, and II have inhibition constants and binding energy less than those of phenytoin The relationship between binding energy and K i is shown in Figure This relationship is linear with R = 0.9, which means each compound with more binding energy has a higher inhibition constant This molecular docking shows in compounds I, VII, and IX that the oxygen of NO forms hydrogen bonding with Ser84 of domain II-S6 (Figure 4) The oxygen of imide in compounds VIII and XV and the OH of compound XIII form a hydrogen bonding interaction with the OH of Thr87 (Figures and 6) In compounds IV and VI, the NH of amide and the NH of the piperidine ring form a hydrogen bonding interaction with the Asn84 of domain III and Glu7B of domain II, respectively (Figure 7) Figure Relationships between binding energy and Ki (inhibition constant) of compounds I–XV Figure Docked structure of compound I in model of sodium channel Hydrogen bond (distance = 1.87 ˚ A) between oxygen of NO group and Ser84 (binding energy: –6.81 kcal/mol) is represented by a dashed green line Figure Docked structure of compound XV in model of sodium channel; hydrogen bond between carbonyl group and Thr87 (distance: 2.118 ˚ A, binding energy: –6.51 kcal/mol) is represented by a red line 309 IMAN et al./Turk J Chem Figure Docked structure of compound XIII in model of sodium channel; hydrogen bond between OH moiety and Thr87 (distance: 1.75 ˚ A, binding energy: –5.91 kcal/mol) is represented by a red line Figure Docked structure of compound IV in model of sodium channel; hydrogen bonds between the NH of piperidine ring and Glu7B, and NH of amide and Asn84 (distance: 1.931 and 1.945 ˚ A, respectively, binding energy: –6.82 kcal/mol) are represented by a red line The oxygen of NO of the N-aryl part of compound XI forms a hydrogen bonding interaction with the OH of Thr87 In compounds III and IX there is an efficient (π − π) interaction between the phenyl ring and the aromatic ring of Phe84 of domain IV and Phe91 of domain III, respectively (Figure 8) In compound XIV, there is a ( π − π) interaction between the phenyl ring of phthalimide and Phe84 of domain IV and Tyr91 of domain I (Figure 9) While phenytoin interacted with the domain IV of the Na channel, most of compounds I–XV interacted mainly with the domains II-S6 and III-S6 of NaV1.2 by making hydrogen bonds and have a (π − π) interaction with domains I, III, and IV in the inner pore of the channel (Figure 10) This docking analysis reveals that amide linker plays a major role in the drug–receptor interaction because replacing it by thioamide linker in compound VII results in significantly increasing the binding energy and K i , which agree with the biological activity of these derivatives 16 Docking and pharmacological data of the subgroup of ameltolide and phthalimide derivatives confirm that there is no significant reduction in the anticonvulsant effect between the compounds with opening and closing pyrrolidine rings 310 IMAN et al./Turk J Chem Figure Docked structure of compound IX in model of sodium channel; hydrogen bond between oxygen of NO group and Ser84 (distance: 2.219 ˚ A, binding energy: –6.7 kcal/mol) is represented by a red line and ( π −π) interaction between phenyl ring and Phe91 of domain III is represented by a yellow cylinder Figure Docked structure of compound XIV in model of sodium channel; ( π − π) interactions between phenyl ring of phthalimide and Phe84 of domain IV and Tyr91 of domain I are represented by a yellow cylinder, π − π interactions are shown by a yellow cylinder Based on the pharmacological study, ameltolide (compound II) appears to be as potent as phenytoin in interacting with the Na channel The phthalimide (compound X) and nitro counterparts of ameltolide (compound I) are several times more potent than ameltolide and phenytoin in binding tests and oral (maximal electroshock seizure) MES tests in rats, which agree with the docking study of these compounds 16 2.1 Molecular dynamics simulation To confirm the binding profile of ligands and to give an overall impression about the ameltolide derivatives, ameltolide was subjected to 10 ns molecular dynamics (MD) simulations The equilibration was monitored and confirmed by examining the stability of the temperature, pressure, density, and potential energy of the system as well as the root mean squared deviations (RMSDs) of the backbone atoms The average backbone RMSD ˚ with respect to the starting structure and the potential energy equilibrated of receptor was 0.6974 ± 0.0010 A about an average of –556,254 ± 777.026 kJ/mol Average temperature, pressure, and density were 300.001 T (RMSD: 1.48133), 1.03143 bar (RMSD: 115.221), and 988.342 kg/m (RMSD: 2.302), respectively All of the 311 IMAN et al./Turk J Chem Figure 10 Right: docked structure of phenytoin in model of the open sodium channel (Nav1.2) The backbones of S6 α -helices of domains I-IV are shown by red, cyan, yellow, and magenta, respectively Hydrogen bond (distance: 2.06 ˚ A and binding energy: –5.83 kcal/mol) is formed between hydrogen of imide and Ser83 of domain IV-S Left: docked structure of compound I in model of sodium channel (one of ameltolide analogous) has a hydrogen bond with domain II-S6 plots show the normal oscillation behavior of the temperature, pressure, and density The backbone RMSD of protein (Figure 11a) indicates that the rigid protein structure equilibrates rather quickly after ns The drug RMSD (0.1064 ± 0.00023, Figure 11b) indicates that the drug equilibrates after 3.5 ns, and the RMSD for the drug reveals its mobility within the binding site in two phases Figure 11 and the RMSD value (0.1064 ± 0.00023) reveal that the ligands stayed within the binding site It should be mentioned that the RMSD values of ligands are a more reliable indicator of their mobility and here the low RMSD of ameltolide (0.1064 ± 0.00023) over the 10 ns shows that ligands remained within the active site pocket The very small standard deviation of the RMSD demonstrates the stability of ameltolide within the active site The number of hydrogen bonds and the distance between the ameltolide and the receptor (Figures 12a and 12b) were analyzed using a distance cutoff of 2.5 ˚ A and an angle cutoff of 60 ◦ For receptor–drug complex, there is one important hydrogen bond Figure 11 The results of MD simulation (a) The MD simulation time vs RMSD of the backbone atoms (C, N, and C α) of protein (b) The MD simulation time vs RMSD of the ameltolide 312 IMAN et al./Turk J Chem between NH of ameltolide and the oxygen atom in residue Asn88 Figures 2a and 2b show the drug–receptor interactions before and after 10 ns MD simulation; in both of them there is an efficient hydrogen bond between Asn88 and drug Figure 12 The results of MD simulation (a) The MD simulation time vs number of hydrogen bond deviation between ameltolide and receptor (b) Intermolecular distance from NH of ameltolide to oxygen atom of Asn88 Figure 13 Whole protein of sodium channel that was made by homology modeling, a) top view of secondary structure b) side view of secondary structure, different domains are shown in different colors In analyzing the drug–enzyme complex, our observation reveals that the complex was stable to the simulation conditions and ameltolide remained in the active site pocket (Figures 2a and 2b) The results of MD simulations confirmed the binding mode of ligands, the accuracy of docking, and the reliability of active conformations obtained by AutoDock The docking analyses indicated that these compounds interacted mainly with residues II-S6 and III-S6 of NaV1.2 by making hydrogen bonds and (π – π) interactions with domains I, III, and IV in the channel’s inner pore Our docking analysis reveals that amide linker plays a major role in drug–receptor interactions It is noteworthy that the MD simulation study corroborates the accuracy of the results of docking analysis 313 IMAN et al./Turk J Chem Experimental 3.1 Molecular modeling The molecular modeling and docking procedures were based on our previous articles 25−28 X-ray crystallography revealed that ameltolide exists in the trans configuration but using molecular modeling it was suggested that the CO–NH moiety of ameltolide and its derivatives bind to their biological target in their cis configuration 29 Because of the greater reliability of the crystallographic results and small energy difference between the cis and trans forms (3 kcal/mol), 29 in the flexible docking study the trans form was considered and also the amide bond was considered as a rotatable bond to create both the cis and trans forms in docking calculations The chemical structures of inhibitors (Figure 1) were constructed using HyperChem software (version 7, Hypercube Inc.) Conformational analysis of the favorite compounds was executed through semiempirical molecular orbital calculations (PM ) by utilizing the software HyperChem Total energy gradient was assessed using the Polak–Ribiere (conjugate gradient) algorithm as a root mean square (RMS) value, until the RMS gradient was 0.01 kcal mol −1 The gradient (G) is the rate of change (first derivative) of total energy (TE) with regard to movement of each atom in the x, y, and z directions for atoms from to n HyperChem reports this value for geometry optimization and single point calculations An RMS gradient of zero means that the structure is at a local minimum or saddle point in the potential energy surface, not necessarily at the structure and state of the lowest energy (global minimum) Energy minimization alters molecular geometry to lower the energy of the system and yields a more stable conformation The generation of new starting conformations for energy minimization uses random variation of dihedral angles Rotation is used for acyclic bond dihedral angles As the minimization progresses, it searches for a molecular structure in which the energy does not change with miniscule changes in geometry This means that the derivative of the energy with respect to all Cartesian coordinates, called the gradient, is near zero If small changes in geometric parameters raise the energy of the molecule, the conformation is relatively stable, and this is referred to as a minimum If the energy lowers by small changes in one or more dimensions, but not in all dimensions, it is a saddle point A molecular system can have many minima The one with the lowest energy is called the global minimum and the rest are referred to as local minima Among all energy minima conformers, the global minima of compounds were applied in docking calculations and the resulting geometry was transferred into AutoDock (version 4.2), which was developed by Arthur J Olson’s Chemometrics Group 19 The structure of the docked conformer of compound II is shown in Figure 3.2 Docking Docking calculations were executed using AutoDock (version 4.2) A model of the open pore of the Na channel was utilized as a receptor (Figures 13a and 13b) This open pore model was developed based on a homology model of the crystal structures of the K channel 17,18 The model constructed by homology with K channel structures was advisedly successful in accounting for inner pore residue interactions with local anesthetics and anticonvulsant drugs like phenytoin The desired compounds were docked in to the active site as well as phenytoin, which was acting as our reference drug for validation of our procedure Docking was done using AutoDock 4.2; in order to assign the perfect grid of each ligand, grid box values were obtained by trial and error and from previous research 20−23 Grid maps with 60 × 60 × 60 points were ˚ 24 The Lamarckian genetic algorithm (LGA), considered one of made and the grid point spacing was 0.375 A the best docking methods available in AutoDock, was adopted to perform the molecular docking studies The 314 IMAN et al./Turk J Chem parameters for LGA were defined as follows: a maximum number of 250,000 energy evaluations, a maximum number of generations of 27,000, and mutation and crossover rates of 0.02 and 0.8, respectively Pseudo-Solis and Wets parameters were used for the local search, and 300 iterations of the Solis and Wets local search were imposed Both AutoGrid and AutoDock computations were performed on Cygwin and 100 independent docking runs were performed for each phthalimide Final docked conformations were clustered using a tolerance of ˚ A RMSD and the docking log (dlg) files were analyzed using AutoDock Tools, the graphical user interface of AutoDock The docked conformations of each ligand were ranked into clusters based on the binding energy and the top ranked conformations were visually analyzed Hydrogen bonding and hydrophobic interactions between docked potent agents and macromolecules were analyzed using AutoDock Tools (version 1.50) 3.3 Molecular dynamics simulation studies To confirm the binding mode of inhibitors, the MD simulations were performed using GROMACS 4.5.5 30,31 based on the docked conformation of ameltolide and whole protein A united-atom GROMOS96 43A1 force field was used for protein parameters and using the PRODRG server, and a GROMOS87/GROMOS96 force field was used to generate a starting topology for ameltolide One Na + ion was added to neutralize the system, and then solvated in a cubic box of spc water molecules Before the MD simulation, the complexes were subjected to 50,000 steps of energy minimization to relieve any geometric strain and close intermolecular contacts To begin real dynamics, the solvent and ions were equilibrated around the protein Equilibration was conducted in two phases: NVT and NPT In the first phase, with a weak constraint to the system (10 kcal/mol), the system was conducted under an NVT ensemble (constant number of particles, volume, and temperature) and was gradually heated from to 300 K in 100 ps and then equilibrated for 100 ps at 300 K In the second phase of equilibration, the system was conducted under an NPT ensemble (constant number of particles, pressure, and temperature) and equilibrated for 100 ps at 300 K Then a 10 ns MD simulation was performed using the periodic boundary conditions, with constant temperature and pressure (1 bar at 300 K) The output trajectories were recorded every ps for the purpose of subsequent analysis The equilibration was monitored by examining the stability of the temperature, pressure, potential energy, and density of the system as well as the RMSD of the backbone atoms 32,33 Acknowledgments We are grateful to the Azad University for financial support for this research, and to Prof Arthur J Olson and Prof A Fozzard for their kindness in offering us the AutoDock 4.2 program and the homology model of the sodium channel References Walker, M C.; 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