Pharmacophore-Based 3D-QSAR Analysis of Thienyl Chalcones as a

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Pharmacophore Based 3D-QSAR Analysis of Thienyl Chalcones as a New Class of Human MAO-B Inhibitors. Investigation of Combined Quantum Chemical and Molecular Dynamics Approach Bijo Mathew, Adebayo A. Adeniyi, Sanal Dev, Monu Joy, Gulberk Ucar, Githa Elizabeth Mathew, Ashona Sing Pillay, and Mahmoud E. S. Soliman J. Phys. Chem. B, Just Accepted Manuscript • DOI: 10.1021/acs.jpcb.6b09451 • Publication Date (Web): 13 Jan 2017 Downloaded from http://pubs.acs.org on January 14, 2017

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The Journal of Physical Chemistry

Pharmacophore Based 3D-QSAR Analysis of Thienyl Chalcones as a New Class of Human MAO-B Inhibitors. Investigation of Combined Quantum Chemical and Molecular Dynamics Approach Bijo Mathew a*, Adebayo A. Adeniyib Sanal Devc, Monu Joy*d, Gülberk Ucare, Githa Elizabeth Mathewf, Ashona Singh-Pillayb, Mahmoud E.S. Solimanb

a)

Division of Drug Design and Medicinal Chemistry Research Lab, Department of Pharmaceutical Chemistry, Ahalia School of Pharmacy, Palakkad 678557, Kerala, India. b) School of Health Sciences, University of KwaZulu-Natal, Westville, Durban 4001, South Africa. c) Department of Pharmaceutical Chemistry, Al Shifa College of Pharmacy, Perinthalmanna 679325, Kerala, India. d) School of Pure & Applied Physics, M.G. University, Kottayam 686560, India. e) Department of Biochemistry, Faculty of Pharmacy, Hacettepe University, 06100 Sıhhiye, Ankara, Turkey. f) Department of Pharmacology, Grace College of Pharmacy, Palakkad, Kerala, India.

*Corresponding author: Dr. Bijo Mathew*, Associate Professor, Division of Drug Design and Medicinal Chemistry Research Lab, Department of Pharmaceutical Chemistry, Ahalia School of Pharmacy, Palakkad-678557, Kerala, India E-mail: [email protected], Phone: +919946700219 * Additional correspondence: Monu Joy, School of Pure & Applied Physics, M.G. University, Kottayam, India. E-mail: [email protected]

1 ACS Paragon Plus Environment


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Abstract Selective monoamine oxidase-B (MAO-B) inhibitors are imperative in the treatment of various neurodegenerative disorders.

Herein we describe the pharmacophore generation and atom-based three

dimensional quantitative structure-activity relationship (3D-QSAR) analyses of previously reported thiophene based hMAO-B inhibitors by our research group. The aim of this study was to identify the principal structural features which could potentially be responsible for the inhibitory activity of hMAO-B inhibitors. The best pharmacophore model generated was the four-point assay of AHRR.8. The pharmacophore model exhibited good correlation with its predictability of the statistically valid 3D-QSAR analyses. Density functional theory calculations were further employed on the lead molecule (2E)-1-(5-bromothiophen-2-yl)-3-[4-(dimethyl amino) phenyl] prop-2-en-1-one (Tb5) in order to investigate the electrostatic potential surface and analyse the natural bond orbital toward the binding characteristics. Molecular dynamics simulations were performed to characterise the molecular level interactions and relative energies of the hMAO isoforms; hMAO-A and hMAO-B with three potent and selective hMAO-B inhibitors (Tb5, Tb6 and Tb9). The results of both continuous and accelerated molecular dynamics simulations demonstrate a distinct preference of the three ligands to bind to the hMAO-B rather than hMAO-A. Keywords: Chalcones, MAO-B, Pharmacophore modelling, DFT, Molecular dynamics. 1. Introduction Human monoamine oxidase (hMAO) is a flavin adenine dinucleotide (FAD) dependent, mitochondrial bound enzyme. It catalyzes the oxidative deamination of monoamine neurotransmitters and dietary amines in the central nervous system (CNS).1, 2 There are two classes of hMAO’s: 1) hMAO-A and 2) hMAO-B, which are classified according to their: 1) tissue distribution, 2) substrate specificity and 3) sensitivity to their selective drug or inhibitor.3 hMAO-A is a selective enzyme responsible for the oxidative deamination of amines; serotonin, noradrenaline and norepinedrine. benzylamine.

hMAO-B preferentially deaminates phenylethylamine and

Tyramine, tryptamine and dopamine are common substrates for both isoforms.4

Selective

inhibitors of hMAO-A are used in the treatment of depression and other mood disorders. hMAO-B has been implicated in age-related neurodegenerative disease particularly those disorders of movement.

Selective

inhibitors of hMAO-B are used to treat Parkinson’s disease (PD) in association with L-3,4-dihydrophenylalanine (L -DOPA) and/or dopamine agonists (DA).5, 6 The activity of hMAO-B has been reported to be greatest in the basal ganglia.7 Selective inhibition of hMAO-B prevent the catalysed metabolism of dopamine in the brain. By


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this mechanism the biogenic amine can exert a prolonged effect specifically reducing the symptoms of Parkinson’s disease. Thus the selective inhibition of hMAO-B is recommended as an adjunctive therapy in PD patients i.e. treatment with L-DOPA (the metabolic precursor of dopamine).8 It has been suggested that the activity of the hMAO enzymes place neurons under oxidative stress. The by-products of hMAO oxidation of amines include; hydrogen peroxide (H2O2) and aldehydes. In individuals of a ‘normal’ state of mental health, glutathione (GSH), an important anti-oxidant in human systems, would inactivate H2O2. However, those patients with PD experience low levels of GSH resulting in an accumulation of H2O2. The excess H2O2 favours a Fenton reaction, whereby Fe2+ reacts with H2O2 generating active free radicals i.e. a hydroxyl radical. These radicals deplete cellular anti-oxidants, causing damage to lipids, proteins and DNA.

9

hMAO inhibitors,

particularly hMAO-B inhibitors, may thus act as potential neuroprotective agents by reducing the formation of these species.10 Currently used hMAO-B inhibitors are selective and react irreversible. These compounds display the typical drawbacks of long-lasting enzyme inhibition, i.e. potential immunogenicity of enzyme-inhibitor adducts with low sensitivity toward ADME parameters and increased duration of action.11As the general body of information grows, it is imperative to continue to design novel selective and reversible hMAO-B inhibitors. The reversible hMAO-B inhibitors would specifically function as neuroprotectant therapeutics with higher specificities and fewer deleterious side effects, since de novo protein synthesis is not essential for the recovery of enzymatic activity.12 For this reason, over the last ten years, studies have been focused on the discovery and development of new classes of reversible hMAO-B inhibitors such as, the recently approved anti-Parkinson drug, safinamide.13 A detailed understanding of the specific molecular interactions that govern hMAO-B inhibition is thus essential to this process. A major breakthrough in understanding the structural requirements for selective substrates and inhibitors was the crystallization of human hMAO-B by Binda et al. in 2001.

14

It was established that the binding site of

hMAO-B was composed of two cavities. The first of which comprises the opening of the active site pocket i.e. an entrance cavity that leads to the substrate cavity positioned in-front of the FAD unit. The substrate molecules and hMAO-B inhibitors must pass through the entrance cavity to reach the substrate cavity.

The amine

oxidative catalytic activity was reported to lie in the substrate cavity. Amino acid Ile199 effectively serves as a ‘gate’ between these cavities.15 Human MAO-B has a narrow substrate cavity with the distinct constriction defined by residues Ile199 and Tyr326, thus preventing larger, more rigid inhibitors from binding. However, it



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has been observed that flexible inhibitors can span both sides at the point of constriction, eg. detergents in the crystal structure of hMAO-B.16 In 2009, Chimenti et al. synthesized and investigated the binding interactions and orientation of selected chalcone-derived compounds based on the structural architype of the active site of the hMAO-B enzyme. This opened a new trend for the discovery and development of chalcone-based hMAO-B inhibitors.17 The basis of Chimenti and his colleagues work led to the discovery of new aryl/heteroaryl-based chalcone hMAO-B inhibitors.

The most active chalcones were characterized by: 1) the presence of hydroxyl and

methoxyl substituents at positions of ortho and para, respectively of the A aromatic ring, and 2) a chlorine, fluorine or trifluoromethyl group at the para position of the B ring.18-20 Recent strategies by medicinal chemists include the introduction of various functional groups on ring A and B of chalcones.21 Further to this substitution with heterocycles such as: furan, chromenes, thiophene, pyridine, piperidine, quinoline, pyrrole and indole; can be used for developing more selective and potent hMAO-B inhibitors.22-25 In this work we describe the pharmacophore-based 3D-QSAR, quantum chemical and molecular dynamics studies of thienyl chalcones reported as new class of reversible hMAO-B inhibitors.

3. Result and discussion 3.1. Pharmacophore Modelling and 3D-QSAR The aim of this study was to elucidate the 3D structural features of thienyl chalcone derivatives crucial for binding. This was accomplished by generating 3D pharmacophores, to quantify the structural features of hMAO-B inhibitors essential for biological activity by generating an atom-based 3D-QSAR model. Sixteen common pharmacophore models were generated with different combinations of variants, in which the top three ranking models were considered based on the survival-inactive score, for further QSAR generation. The best model was found to be associated with four-point hypotheses. It consisted of a hydrogen bond acceptor (A1), a hydrophobic (H3) and two aromatic rings (R4 and R5) and was denoted as AHRR.8. This pharmacophore was used for further analysis of the thienyl class of chalcones. The special arrangement of features along with their distances is depicted in Fig.1.

A statistically significant 3D-QSAR model was obtained using this

pharmacophore hypothesis. The correlation co-efficient value (R2 = 0.7701) and Fisher ratio (F =16.8) for the training set of 19 compounds. Also, the predictive power of the generated model was found to be significant. This was confirmed by the high value of the cross-validated correlation co-efficient (q2 = 0.6826), root mean square error (RMSE) (0.1275) and Pearson-R (0.8812) for the test set of seven compounds. The Pearson-R


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value indicated a good correlation between the predicted and observed activity for the test set. The summary of the various statistical features for the scoring of the atom-based 3D-QSAR hypothesis are mentioned in the Table 1. Table 1: Different parameters of generated hypothesis AHHR.8

Survival Inactive Survival-Inactive Post hoc Matches

4.439 2.383 2.056 3.975 5

A graph of the observed versus the predicted biological activities of the molecules in both the training and test sets are depicted in (Figs. 2a and b), respectively. The experimental and corresponding predicted pKi values of the test and training sets are given in detail in Table 2. The prediction of the different activities has been classified according to the residual scale (Fig. 3). The residual values less than 0.8 are considered good predictions, values between 0.8 and 1.6 are weak and values higher than 1.6 are considered poor predictions. In this study, the residual scale was in the range of ±0.50, which is indicative of a good model. The developed hypotheses and the most active compound was visualized in the context of the QSAR model. This provides additional insight regarding the relevance of certain features essential for biological activity.

Table 2: The data set for 3D-QSAR study with actual and predicted hMAO-B inhibitory activities

O

R S X SI NO

X

R

Observed activity

Predicted activity

1 2 3 4 5 6 7 8 9 10 11 12

H H H H H H Br Br Br Br Br Br

2-F 3-F 4-F 2-CF3 3-CF3 4-CF3 OH OCH3 CH3 N(CH3)2 CH2CH3 NO2

5.592 5.561 5.523 5.77 5.804 6.046 5.991 6.26 6.509 6.959 6.721 5.516

5.74 5.67 5.78 5.78 5.92 5.75 6.0 6.31 6.46 6.72 6.69 5.68


Pharma set

Inactive

Active Active Active Inactive


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13 14 15 16 17 18 19 20 21 22 23 24 25 26

Br Br Br Cl Cl Cl Cl Cl Cl Cl Cl Cl Cl Cl

Br F CF3 H OH OCH3 CH3 N(CH3)2 CH2CH3 NO2 Cl Br F CF3

6.602 5.767 5.708 5.987 5.936 6.125 6.194 6.222 6.509 5.593 6.102 6.155 5.701 5.72

6.18 5.61 6.08 5.93 5.88 6.2 6.27 6.6 6.39 5.52 6.05 6.18 5.99 5.67

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Active

Active

3.1.1. 3D-QSAR contour map analysis The 3D aspect of the QSAR model was examined to understand how the active compound contributes to the biological activity. The colour coded cubes in the space around the ligand are coloured according to the sign of their co-efficient values. By default the blue colour represents positive co-efficients and the red negative coefficients. The contour cubes obtained from the model AHRR.8, help identify those positions that require a particular physiochemical property to enhance the bioactivity. The 3D-QSAR model is visualized in the context of various properties using the most active (compound 10Tb5) and the least active (compound 12) compounds. A pictorial representation of the QSAR model associated with the electron withdrawing property of the active inhibitor, compound 10 is shown in Fig. 4. The red cube adjacent to the benzene ring highlights the negative contribution of the electron withdrawing groups. The addition of an electron withdrawing group at this position may lead to decreased biological activity. With respect to the least active compound (12) the presence of an electron withdrawing nitro group at the para position of the phenyl ring B, of the thienyl chalcone moiety decreases the hMAO-B inhibitory potential. Thus substitution with an electron donating group will have a positive influence on the biological activity. The hydrogen bond contributions were shown for both the thiophene and phenyl system (Fig. 5). The contribution of hydrophobic/nonpolar groups is observed with blue cubes near position 4 and 5 of the benzene and thiophene rings, respectively (Fig. 6). The presence of blue cubes at position 5 of the thiophene ring and position 4 of the phenyl ring, depict the positive potential of a hydrogen bond (H-bond) donor. An increase in biological activity can be expected if these positions are substituted with H-bond donor groups. At the same time, the shifting of these H-bond donors either in the ortho- or meta-positions of the phenyl system disfavours the activity ratio. Two hydrophobic ring structures separated by a hydrogen acceptor (A) i.e. polar linkage, is a common requirement for hMAO-B inhibitory activity. This is consistent with our previous 3D-QSAR analysis of


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hMAO-B inhibitors of furano chalcones.26 Among the series of thienyl chalcones, the most active compounds contain hydrophobic dimethylamino- and ethyl- groups at the para position of phenyl ring, B. The contour map also revealed that shifting of these hydrophobic groups to either the meta- or ortho-positions of the phenyl ring lead to a decrease in activity. 3.2. Quantum chemical calculation From the experimental data, it is evident that compound 10 with a hydrophobic dimethylamino- group present in the bromo-substituted thienyl chalcone demonstrated potent hMAO-B inhibitory activity. 3.2.1. Calculation of molecular electrostatic potential The bioactivity of the compound 10 (Tb5) was further explored with a molecular electrostatic potential (MEP) surface calculation. The MEP surface of the compound was plotted against its experimental geometry using density functional theory (DFT) with B3LYP/6-311++G (d,p) model, Fig. 7. The electrostatic potential scale increases in the order: red < orange < yellow < green < blue.27 Blue indicates the strongest attraction, red indicates the strongest repulsion and green indicates a neutral electrostatic potential. In the present work, the calculated result shows that the negative potentials are mainly over the electronegative carbonyl group. The availability of electron density of the oxygen atom makes it able to act as a H-bond acceptor, leading to a better affinity toward the active site of hMAO-B. A positive potential is concentrated on the N,N-dimethylamino(NNDA) moiety. This occurs as a result of it releasing its electron density toward the aromatic ring. This helps the conjugation of the molecule which is depicted by the pale yellow colour on the phenyl ring. This feature is supported by the olefin linkage.

The bromine atom attached on the thiophene and the sulphur itself is

considerably populated. Although within the ring, thiophene is less populated since both the carbonyl and bromine substitutions on either side of the ring are electron withdrawing in nature. A push-pull electron density mechanism is observed with the electron releasing effect of NNDA on the aromatic ring and the electron withdrawing nature associated with bromine substitution on thiophenyl ring. This makes the molecule become highly delocalized in π-electron density. The phenomina supports the extensive electronic population of the carbonyl oxygen atom. 3.2.2. Natural bond analysis The NBO calculations were carried out using Gaussian09 software package with B3LYP/6-311++G(d,p) method. It offers a platform for exploring charge transfer or conjugative interaction in molecular systems. The NBO calculation is an efficient method for studying molecular bonding characteristics and interactions among bonds. The larger the stabilization energy value, the more intensive the interaction between electron donors and



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electron acceptors, i.e. the more donating the tendency is of electron donors to electron acceptors, the greater the extent of conjugation of the whole system. The NBO analysis has proved to be an effective tool for chemical interpretation of hyperconjugative interactions and electron density transfers from the filled lone pair electrons. The second order, Fock matrix was carried out to evaluate donor–acceptor interaction in the NBO analysis. For each donor (i) and acceptor (j) the stabilization energy E(2) associated with the delocalization i to j is as follows:

‫(ܧ‬2) = ߂‫ܧ‬௜௝ = ‫ݍ‬௜

(‫ܨ‬௜௝ଶ ) ‫ܧ‬௝ − ‫ܧ‬௜

Where qi is the donor orbital occupancy, Ei and Ej are the diagonal elements and Fij is the off-diagonal NBO Fock matrix element. In NBO analysis the E(2) values correspond to the intensive interaction between electrondonors and electron-acceptors. A large E(2) value means there is a greater extent of conjugation of the whole system.

Based on the optimised geometry (Fig: 7) the possible intensive interactions are given in

supplementary material (Table S1). The resonance stabilization of the molecule can be rationalised through the interaction of various orbitals. The aromatic system has extensive conjugation of its π-systems, i.e. π(C16-C24) to π*(C5-C12), with a measured stabilization energy of 39.65 kcal mol-1. Conjugative interactions of the π*(C8-C14) to σ*(C8-C16) and σ*(C16-C24) followed by π*(C18-C19) to σ* (C7-C17) also lead to aromatic delocalization with estimated stabilization energies of 17.88, 9.25 and 180.04 kcal mol-1, respectively. The electron donating nature of NNDA is justified by the interaction of σ(N4-C26) to π*(C18-C19) followed by σ(N4-C30) to π*(C18-C19), which has an extremely high stabilization energy of 4671.38 and 6088.1 kcal mol-1, respectively. This characteristic is supported with the σ to σ* contribution of NNDA, i.e. σ(N4-C26) to σ*(C18-C19) with an estimated energy of 2.91 kcal mol-1. NNDA has varying intramolecular interactions which include σ(N4-C26) to σ*(C7-C10) [26.99 kcal mol-1] and σ(N4-C26) to σ*(C7-C17) [42.01kcal mol-1], σ(N4-C26) to π*(C5-C12) [15.98 kcal mol-1]. The latter interaction supports the π-electron delocalization throughout the molecule, since C5-C12 acts as a bridge (sp2 hybridized). The σ-orbital of N4-C18 strongly interacts with the antibonding σ-orbital of C5-C12 (3063.34 kcal mol-1). The NNDA moiety also interacts with the thiophene ring, favouring the electron withdrawing effect of bromine as σ(N4-C30) to σ*(S1-C7) [48.0 kcal mol-1 ], σ(N4-C30) to σ*(Br2-C21) [27.74 kcal mol-1], respectively. The carbonyl moiety has an observed interaction of σ(N4-C30) to π*(O3-C17) [42.78 kcal mol-1]. The same contribution is detected between N4-C26 to S1-C7, O3-C17 and Br2-C21 with energies of 15.97, 14.53 and 8.81 kcal mol-1, respectively. The N4-C26 to Br2-C21 interaction supports the push-pull electron


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transfer behaviour of the molecule. A chain interaction also favours the delocalization of the π-electron density toward the thiophene ring as π(C5-C12) to π*(O3-C17) followed by π*(O3-C17) to σ*(C7-C17), affording an energy of 37.72 and 111.16 kcal mol-1, respectively. The former interaction is stabilized with back donation of 4.39 kcal mol-1 from π(O3-C17) to π*(C5-C12); σ(N4-C30) also interacts in a similar manner with σ*(C7-C17). Extremely high charge transfers occur between two rings with stabilization energy of 5094.47 kcal mol-1. The electron withdrawing effect of the bromine atom catalyses the overall delocalization of the π-electron density throughout the molecule. This is seen with σ(N4-C18) to σ*(Br2-C21) [11.63 kcal mol-1], σ(C10-C22) to σ*(Br2-C21) [8.58 kcal mol-1], π*(C5-C12) to σ*(Br2-C21) [18.61 kcal mol-1], π*(C7-C10) to σ*(Br2-C21) [17.94 kcal mol-1], π*(C18-C19) to σ*(Br2-C21) [22.92 kcal mol-1]. The phenyl ring and carbonyl group also support this as σ(C18-C19) to σ*(Br2-C21) has a stabilization energy of 9.49 kcal mol-1 and π*(O3-C17) to σ*(Br2-C21) [16.51 kcal mol-1]. 3.3. Molecular dynamics study 3.3.1. Binding energy analysis Normal molecular dynamics (NMD) and accelerated molecular dynamics (aMD) methods were used to study the interaction of Tb5, Tb6 and Tb9 with the receptor hMAO-A and hMAO-B, respectively (Fig: 8). In order to understand the possible rare events that take place in the interaction of the three ligands with the two receptors, we employed an all-atom enhanced sampling method of aMD.28 This method has been used to investigate conformational changes in proteins that typically occur on the millisecond time scale.29 Accelerated MD has the ability to accelerate and reduce the time scale in MD simulations. In doing so, it can enhance the escape rates from potential wells through the addition of a bias potential to the true potential. The results from aMD show a significant difference in the order of the ligand interaction with the two receptors compared to the systems in NMD. The plots for total potential energy (EPTOT) and the room mean square deviation (RMSD) are shown in Fig. 9 and 10 for the complexes of ligand bound to receptor, during the NMD and aMD. There is high similarity in the potential energy change (EPTOT) of the three ligands interacting with hMAO-A and hMAO-B during both the NMD and aMD as evident in the overlap of each of their EPTOT curves. The total RMSD of the Tb5 interactions with hMAO-A and hMAO-B is relatively lower than that of Tb9. While Tb9 is lower than Tb6 when analysing the results from both NMD and aMD. Overall there is no significant change in the potential energy plots from NMD to aMD for the six complexes Tb5: hMAO-A, Tb6: hMAO-A, Tb9:hMAO-A, Tb5:hMAO-B, Tb6:hMAO-B and Tb9:hMAO-B. This is an indication that the aMD method



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only explores the potential energy surface of the systems more effectively without altering the potential energy of the system. The binding free energies for the interactions of Tb5, Tb6 and Tb9 with receptors hMAO-A and hMAO-B were computed using the Poisson-Boltzman (MM-PBSA) and Generalized Born (MM-GBSA) methods. The results are shown in Table 3 for hMAO-A and Table 4 for hMAO-B. The contribution of van der Waals (EvdW), electrostatic (Eelec), polar solvation (Esurf), non-polar solvation (Enpolar), Generalized Born solvent (Egb), Poisson Boltzmann solvent (Epb) energies to the total binding free energies are also tabulated. The results from both NMD and aMD clearly show that the three ligands Tb5, Tb6 and Tb9 preferentially target hMAO-B compared to hMAO-A (Fig. 11). The results from both MM-GBSA and MM-PBSA ranked Tb5 as the better inhibitor of hMAO-A and hMAO-B compared to Tb6 and Tb9 during both the NMD and aMD. The computed order of the binding strength of the ligands in their interaction with hMAO-A is Tb5 > Tb9 > Tb6, while the order of Tb5 > Tb6 > Tb9 was obtained in their interaction with hMAO-B (Fig.11).


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Table 3: The binding free energy of the ligands interactions with hMAO-A during the NMD and aMD using MM-GBSA and MM-PBSA methods

Complex Tb5:MAO-A Tb6:MAO-A Tb9:MAO-A




∆EvdW -41.703 ± 0.063 -37.125 ± 0.058 -38.081 ± 0.083

∆Eelec -6.797 ± 0.101 -1.995 ± 0.083 -5.421 ± 0.066

∆EvdW -41.703 ± 0.063 -37.125 ± 0.058 -38.081 ± 0.083

∆Eelec -6.797 ± 0.101 -1.995 ± 0.083 -5.421 ± 0.066

∆EvdW -40.502 ± 0.068 -37.987 ± 0.084 -38.600 ± 0.079

∆Eelec -5.662 ± 0.087 -4.640 ± 0.096 -6.186 ± 0.061

∆EvdW -40.502 ± 0.068 -37.987 ± 0.084 -38.600 ± 0.079

∆Eelec -5.662 ± 0.087 -4.640 ± 0.096 -6.186 ± 0.061

NMD-GBSA ∆EEGB ∆EESURF 17.653 ± -5.048 ± 0.060 0.006 14.187 ± -4.908 ± 0.084 0.006 14.950 ± -4.734 ± 0.064 0.007 NMD-PBSA ∆EEPB ∆EENPOLAR 32.482 ± -3.833 ± 0.109 0.004 28.630 ± -3.819 ± 0.153 0.003 28.197 ± -3.541 ± 0.104 0.003 aMD-GBSA ∆EEGB ∆EESURF 17.214 ± -4.955 ± 0.075 0.007 16.630 ± -4.891 ± 0.077 0.007 15.580 ± -4.657 ± 0.053 0.006 aMD-PBSA ∆EEPB ∆EENPOLAR 28.581 ± -3.785 ± 0.121 0.005 27.226 ± -3.684 ± 0.166 0.005 27.868 ± -3.421 ± 0.095 0.003

∆Ggas -48.500 ± 0.098 -39.120 ± 0.091 -43.503 ± 0.114

∆Gsolv 12.605 ± 0.062 9.278 ± 0.083 10.216 ± 0.062

∆Gbind -35.895 ± 0.068 -29.842 ± 0.070 -33.287 ± 0.091

∆Ggas -48.500 ± 0.098 -39.120 ± 0.091 -43.503 ± 0.114

∆Gsolv 28.649 ± 0.107 24.811 ± 0.153 24.657 ± 0.103

∆Gbind -19.850 ± 0.142 -14.309 ± 0.135 -18.846 ± 0.111

∆Ggas -46.164 ± 0.095 -42.628 ± 0.155 -44.787 ± 0.102

∆Gsolv 12.258 ± 0.076 11.739 ± 0.073 10.924 ± 0.052

∆Gbind -33.905 ± 0.070 -30.888 ± 0.113 -33.863 ± 0.091

∆Ggas -46.164 ± 0.095 -42.628 ± 0.155 -44.787 ± 0.102

∆Gsolv 24.796 ± 0.120 23.541 ± 0.164 24.447 ± 0.094

∆Gbind -21.368 ± 0.134 -19.086 ± 0.255 -20.340 ± 0.119

The results from aMD show a conformational change in the hMAO-B receptor that can possibly favour the inhibitory activity of Tb5 significantly than Tb6 and Tb9. Although aMD indicates a significant improvement in the binding of Tb6 and Tb9 compared to Tb5 in their interaction with hMAO-A, the overall interaction of the Tb5 with hMAO-A is better. Both MM-GBSA and MM-PBSA methods show improvement in the interaction of Tb5 with hMAO-B through conformational changes. This results in better contributions of the ∆EvdW, ∆Eele and ∆Ggas to the total binding free energy (Table 4).



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Table 4: The binding free energy (Kcal/mol) of the ligands interactions with hMAO-B during the NMD and aMD using MM-GBSA and MM-PBSA methods

Complex Tb5:MAO-B Tb6:MAO-B Tb9:MAO-B




∆EvdW -46.330 ± 0.087 -44.122 ± 0.077 -41.012 ± 0.077

∆Eelec -8.811 ± 0.102 -11.032 ± 0.067 -11.392 ± 0.110

∆EvdW -46.330 ± 0.087 -44.122 ± 0.077 -41.012 ± 0.077

∆Eelec -8.811 ± 0.102 -11.032 ± 0.067 -11.392 ± 0.110

∆EvdW -47.526 ± 0.079 -43.156 ± 0.072 -42.101 ± 0.081

∆Eelec -10.551 ± 0.089 -10.517 ± 0.067 -9.225 ± 0.101

∆EvdW -47.526 ± 0.079 -43.156 ± 0.072 -42.101 ± 0.081

∆Eelec -10.551 ± 0.089 -10.517 ± 0.067 -9.225 ± 0.101

NMD-GBSA ∆EEGB ∆EESURF 17.024 ± -5.746 ± 0.050 0.005 18.421 ± -5.617 ± 0.044 0.004 17.315 ± -4.997 ± 0.043 0.004 NMD-PBSA ∆EEPB ∆EENPOLAR 29.765 ± -3.770 ± 0.082 0.003 30.469 ± -3.635 ± 0.100 0.003 27.989 ± -3.439 ± 0.074 0.002 aMD-GBSA ∆EEGB ∆EESURF 18.049 ± -5.792 ± 0.055 0.004 17.219 ± -5.621 ± 0.043 0.004 16.392 ± -5.051 ± 0.040 0.004 aMD-PBSA ∆EEPB ∆EENPOLAR 30.089 ± -3.751 ± 0.077 0.003 28.194 ± -3.615 ± 0.080 0.003 27.784 ± -3.385 ± 0.068 0.002

∆Ggas -55.142 ± 0.097 -55.154 ± 0.086 -52.404 ± 0.102

∆Gsolv 11.278 ± 0.050 12.804 ± 0.043 12.318 ± 0.043

∆Gbind -43.863 ± 0.079 -42.349 ± 0.077 -40.086 ± 0.086

∆Ggas -55.142 ± 0.097 -55.154 ± 0.086 -52.404 ± 0.102

∆Gsolv 25.995 ± 0.082 26.835 ± 0.099 24.550 ± 0.074

∆Gbind -29.147 ± 0.116 -28.319 ± 0.125 -27.854 ± 0.113

∆Ggas -58.077 ± 0.110 -53.673 ± 0.079 -51.326 ± 0.085

∆Gsolv 12.257 ± 0.054 11.598 ± 0.043 11.340 ± 0.040

∆Gbind -45.820 ± 0.089 -42.075 ± 0.067 -39.985 ± 0.070

∆Ggas -58.077 ± 0.110 -53.673 ± 0.079 -51.326 ± 0.085

∆Gsolv 26.338 ± 0.077 24.579 ± 0.079 24.399 ± 0.068

∆Gbind -31.739 ± 0.116 -29.094 ± 0.096 -26.927 ± 0.099

The first ten residues which undergo the most significant energy changes and contribution to the ligands interaction with the receptors are shown in Table 5 for hMAO-A and Table 6 for hMAO-B using only the results from the MM-GBSA method. A more comprehensive feature for the residue contribution to the binding interaction are shown in Fig. 12, for any residue of the receptor which contributes a value that is ≥ ± 0.2 Kcal/mol. The residue contribution plots in Fig. 12 give better insight into the reason why the interacting energies calculated from MM-PBSA are lower in magnitude compared to MM-GBSA.

Residues Arg41,

Asn166, Ile192, Glu198 and Thr318 in hMAO-A and Pro101, Cys171, Tyr187, Gln205 and Ser432 in hMAO-B are computed in MM-PBSA to be significantly unfavourable to the binding free energy. This is characterized by high positive values. Unfavourable residue contributions from MM-PBSA are more prominent in the


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computed results from NMD compare to aMD. The set of ranked residues from both NMD and aMD that contribute most significantly to the ligand interaction with hMAO-B and hMAO-A are very similar. Table 5: Binding energy (Kcal/mol) contribution of each ligand and the top fifteen residues of the receptors with the highest energy change during the ligand interactions with hMAO-A using MM-GBSA method

Tb5:MAO-A Tb5 PHE 193 TYR 426 ILE 317 ILE 165 TYR 389 LEU 319 ILE 307 ASN 166 SER 194 VAL 195 Tb5:MAO-A Tb5 PHE 193 ILE 165 TYR 389 ILE 317 TYR 426 ASN 166 ILE 307 SER 194 LEU 319 LEU 87

Decomposition during NMD using GBSA Values ± error Tb6:MAO-A Values ± error Tb9:MAO-A -19.375 ± 1.09 Tb6 -16.640 ± 0.95 Tb9 -2.123 ± 0.596 TYR 59 -1.645 ± 0.566 ILE 165 -1.578 ± 0.456 TYR 389 -1.431 ± 0.525 ILE 317 -1.569 ± 0.294 LEU 319 -1.194 ± 0.383 PHE 193 -1.518 ± 0.311 TYR 426 -1.045 ± 0.484 LEU 319 -1.432 ± 0.266 PHE 334 -0.923 ± 0.448 SER 194 -0.977 ± 0.320 ILE 165 -0.816 ± 0.331 TYR 426 -0.811 ± 0.216 VAL 195 -0.740 ± 0.400 TYR 59 -0.779 ± 0.644 PHE 193 -0.645 ± 0.273 TYR 389 -0.745 ± 0.560 ILE 317 -0.626 ± 0.284 ILE 307 -0.592 ± 0.300 GLY 57 -0.602 ± 0.434 PHE 334 Decomposition during aMD using GBSA Values ± error Tb6:MAO-A Values ± error Tb9:MAO-A -18.114 ± 1.09 Tb6 -17.272 ± 1.65 Tb9 -2.121 ± 0.658 LEU 319 -1.682 ± 0.491 ILE 317 -1.608 ± 0.414 VAL 195 -1.436 ± 0.700 PHE 193 -1.438 ± 0.496 ILE 317 -1.349 ± 0.508 ILE 165 -1.332 ± 0.245 PHE 334 -1.194 ± 0.323 ILE 307 -1.035 ± 0.515 VAL 81 -1.001 ± 0.454 TYR 389 -0.877 ± 0.551 CYX 305 -0.925 ± 0.641 SER 194 -0.865 ± 0.356 ILE 165 -0.806 ± 0.344 LEU 319 -0.767 ± 0.564 PHE 193 -0.795 ± 0.264 TYR 59 -0.704 ± 0.194 TYR 389 -0.659 ± 0.709 PHE 334 -0.614 ± 0.217 TYR 59 -0.431 ± 0.519 CYX 305


Values ± error -17.846 ± 1.43 -1.829 ± 0.340 -1.626 ± 0.290 -1.587 ± 0.501 -1.026 ± 0.250 -0.966 ± 0.574 -0.933 ± 0.336 -0.928 ± 0.306 -0.919 ± 0.353 -0.843 ± 0.199 -0.829 ± 0.228 Values ± error -18.268 ± 1.36 -1.975 ± 0.360 -1.952 ± 0.433 -1.376 ± 0.251 -1.376 ± 0.361 -1.037 ± 0.361 -0.966 ± 0.598 -0.918 ± 0.220 -0.900 ± 0.305 -0.693 ± 0.213 -0.674 ± 0.198


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Table 6: Binding energy (Kcal/mol) contribution of each ligand and the top fifteen residues of the receptors with the highest energy change during the ligand interactions with hMAO-B using MM-GBSA method

Tb5:MAO-B Tb5 ILE 198 LEU 170 TYR 59 TYR 434 CYS 171 TYR 397 ILE 197 GLN 205 ILE 315 TYR 325 Tb5:MAOB Tb5 ILE 198 TYR 59 TYR 397 TYR 434 LEU 170 ILE 315 ILE 197 GLN 205 TYR 325 PHE 167

Decomposition during NMD using GBSA Values ± error Tb6:MAO-B Values ± error Tb9:MAO-B -22.629 ±1.19 Tb6 -22.119±1.21 Tb9 -2.360 ± 0.341 TYR 59 -2.397 ± 0.405 ILE 198 -2.052 ± 0.373 ILE 198 -2.091 ± 0.432 LEU 170 -1.944 ± 0.809 TYR 434 -2.064 ± 0.465 TYR 59 -1.536 ± 0.333 LEU 170 -1.929 ± 0.462 TYR 434 -1.341 ± 0.262 TYR 397 -1.692 ± 0.342 GLN 205 -1.311 ± 0.481 CYS 171 -1.366 ± 0.279 TYR 397 -1.225 ± 0.291 ILE 197 -1.076 ± 0.212 TYR 325 -1.196 ± 0.346 TYR 325 -0.916 ± 0.253 ILE 197 -1.125 ± 0.292 ILE 315 -0.730 ± 0.225 PHE 167 -1.047 ± 0.276 PHE 167 -0.724 ± 0.266 CYS 171 Decomposition during aMD using GBSA Values ± error Tb6:MAOB Values ± error Tb9:MAOB -23.150 ± 1.455 Tb6 -21.977 ± 1.060 Tb9 -2.309 ± 0.402 TYR 59 -2.303 ± 0.392 LEU 170 -2.189 ± 0.698 ILE 198 -2.121 ± 0.361 TYR 59 -2.093 ± 0.379 LEU 170 -1.975 ± 0.366 TYR 434 -2.013 ± 0.399 CYS 171 -1.539 ± 0.410 ILE 198 -1.905 ± 0.410 TYR 434 -1.403 ± 0.419 CYS 171 -1.283 ± 0.285 TYR 397 -1.327 ± 0.483 ILE 197 -1.252 ± 0.248 ILE 197 -1.199 ± 0.252 TYR 397 -1.223 ± 0.504 TYR 325 -0.938 ± 0.285 GLN 205 -1.211 ± 0.278 GLN 205 -0.908 ± 0.402 TYR 325 -1.103 ± 0.393 ILE 315 -0.873 ± 0.245 PHE 167

Values ± error -20.903 ±1.27 -2.022 ± 0.367 -1.669 ± 0.527 -1.563 ± 0.778 -1.355 ± 0.313 -1.350 ± 0.584 -1.345 ± 0.384 -1.108 ± 0.313 -1.071 ± 0.281 -1.063 ± 0.372 -1.026 ± 0.468 Values ± error -20.985 ± 1.087 -1.968 ± 0.350 -1.680 ± 0.941 -1.620 ± 0.308 -1.566 ± 0.696 -1.485 ± 0.340 -1.185 ± 0.277 -1.167 ± 0.310 -1.098 ± 0.237 -1.060 ± 0.319 -0.792 ± 0.266

3.3.2. Conformational analysis The plots of the first three principal component analyses (PCA) demonstrate the conformational changes during the NMD (Fig. 13). The plot of PC1 versus PC2 for the trajectories of the aMD computation is shown in Fig. 14 with the fluctuation of the residues along the PC1 (black) and PC2 (green). The positive or negative value of the eigenvectors of PCA is arbitrary. However, the region with the same sign are correlated in their conformational evolution while the regions with opposite signs are anti-correlated.30 The conformational changes of the complexes during the course of the MD simulations are grouped into three; from black to red and finally to green. Therefore, the clustering of the colour of the PCA can be used to describe the conformational changes from open conformations (black or green) to occluded conformations (red) and to closed conformations (black or green).


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The PCA clustering group along the first two PCA during both the NMD (Fig. 11) and aMD (Fig. 14) are mapped to the potential energy surface (PES) which was constructed from the potential energy difference of each of the trajectories from that of the first trajectory. In addition, the potential energy surface constructed from the reweighing energy script for aMD is included in Fig. 14. In all the PES, the lowest energy regions are indicated in a circle which reflects the points that contain the lowest energy structures. There is high similarity between the PCA results obtained during both the NMD and aMD for hMAO-A and hMAO-B. The best conformation for the interactions of Tb5 with hMAO-A is the middle conformation (red colour of the PCA cluster). While that of hMAO-B are found to be closed (black or green). As clearly depicted in the PES, the energy barrier crossed during the aMD (Fig. 14d) is far lower than that of NMD (Fig. 13). This is due to the external energy supplied to fill the potential energy well during aMD. PES mapping show that there is a high possibility of lower energy conformational sampling which makes significant contribution to the total binding energy. The two best inhibitors of hMAO-A (Tb5 and Tb9) have lower energies on the PES compared to Tb6. Although, the two best inhibitors of hMAO-B (Tb5 and Tb6) have the lowest energies on the PES compared to Tb9 (Fig. 13 and Fig. 14). The average structure for each of the three groups of the conformational cluster is shown in Fig. 15 according to their clustering colour on the PCA plots. The RMSD for the protein backbone from the first group of PCA (black) to the second group (red) are 0.552, 0.766 and 0.538 Å; and to the third group (green) is 0.795, 0.817 and 0.697Å, respectively for Tb5, Tb6 and Tb9 interaction with hMAO-A. While the RMSD obtained for similar interactions with hMAO-B are 0.638, 0.642, 0.853 and 0.622, 0.995, 0.983 Å. The features of the residue fluctuation during the MD (Fig. 15a) are very similar to the features of the fluctuation obtained from the normal mode analysis (NMA) of the average geometry of each of the three groups on the PCA (Fig. 15b). There is a wide range of residual fluctuation but of lower magnitude in the interaction of the ligands with hMAO-A compared to hMAO-B.

These are prominent on the first set and the last set of the few residues as

shown for the RMSF (Fig. 15a and 15b). The conformational differences for the best energy conformation obtained from the NMD and aMD are shown in Fig. 16. The RMSD values for the conformational difference between the best energy structures obtained for the aMD and NMD for the interaction of Tb5, Tb6, Tb9 with hMAO-A are 1.187, 1.341 and 1.486. While the RMSD value for similar interactions with hMAO-B are 1.228, 1.588, 1.519.



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The best of the geometries obtained from the aMD for the interaction of ligands with hMAO-A and hMAO-B are shown in Fig. 17. The structure clearly shows that the two proteins assume a significantly different conformation during their interaction with the ligands as indicated with the arrow. The assumed conformational changes obtained for the interaction of Tb5 (yellow) are widely different from that of Tb6 (blue) and Tb9 (orange). This is especially evident at the loop regions of the proteins. The super-positioning of the best conformation obtained from the interaction of Tb5: hMAO-A with that of the Tb6 and Tb9 gives an RMSD of 1.235 and 1.60, respectively. The RMSD value obtained for the super-positioned interaction of the Tb5: hMAO-B system with that of Tb6 and Tb9 are 1.384 and 1.581, respectively.

The geometries of the

superimposed structures in Fig. 16 confirm that Tb5 interaction with hMAO-A is an occluded conformation while the interaction of Tb6 is closed and Tb9 is open. The structures for hMAO-B interaction suggest Tb5 and Tb6 to be closed while Tb9 is in an occluded conformation. 3.3.4. Binding site residues interaction and the correlation during dynamics The analysis of the binding site interactions of the ligands with the protein residues are shown in Fig. 18, but only for the best energy conformations obtained from aMD. Most of the protein residue interactions with the inhibitors are defined as hydrophobic. The interactions of ligands with the two receptors are characterized by hydrophobic interactions with binding site water molecules and protein residues. With respect to the interaction of the ligands with hMAO-A, only the Tb5 complex is characterized by a hydrogen bond with Ser194. However, all three ligands have hydrogen bonding interactions with Tyr59 when bound to hMAO-B. The correlation and anti-correlation in the movement of the residues of the complexes during the aMD simulation are shown in Fig. 19. Dynamical cross-correlation matrix (DCCM) was used according to:

C( i , j ) =

c( i , j ) 1 /2 1/ 2 c (i, i) c ( j , j)

Where c(i,j) is the covariance matrix element of protein fluctuation between residues i and j. The corresponding 3D geometries showing the most significant networks of correlation (red) and anti-correlation (blue) with C(i,j)| ≥ 0.6. The interactions of Tb5, Tb6 and Tb9 with both receptors are characterized mainly with intra-domain correlation and a little feature of inter-domain. A higher level of residue-residue correlation was observed for the interaction Tb6 with hMAO-A and hMAO-B compared to Tb5 and Tb9 interactions. There is no noticeable intra- or inter-domain anti-correlation in the interaction of Tb5 and Tb9 in their interaction with the two


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receptors. Only the interaction of Tb6 is characterized with a high level of significant long distance interdomain anti-correlation. Conclusion The present study employed an atom-based 3D-QASR analysis of thienyl chalcone hMAO-B inhibitors followed by the combined quantum chemical and molecular dynamics study. The atom-based 3D-QSAR generated model AHRR.8 exhibited good correlation and predictive power and satisfactory agreement between experiment and theory. The results of the NMD and aMD simulations clearly show that the three ligands preferentially bind to the hMAO-B rather than h MAO-A. The binding affinities of the ligands using the results from both MM-GBSA and MM-PBSA during the both the NMD and aMD simulation are in the order of Tb5 > Tb9 > Tb6 for hMAO-A and Tb5 > Tb6 > Tb9 for the hMAO-B. The three ligands preferentially target hMAO-B compared to hMAO-A. The results from aMD show a possible rare event in the conformational changes of the hMAO-B receptor that can possibly favour the inhibitory activities of Tb5 significantly than Tb6 and Tb9. The conformational changes for the interaction of Tb5 with the receptors are widely different from that of Tb6 and Tb9 especially at the loop regions of the proteins. The study on the correlation in the dynamics of the residues shows that Tb6 systems imposed a higher level of intra-domain correlation and inter-domain anti-correlation in dynamics of the protein residues. 3. Materials and methods 3.1. Pharmacophore modeling and 3D-QSAR 3.1.1. Dataset preparation A dataset comprising 26 hMAO-B inhibitors with a thiophene scaffold (reported by our lab) were used in the present study.31-33All molecules were built in Maestro 9.3 and prepared using the LigPrep 2.5 module. The LigPrep module converts two-dimensional (2D) structures to 3D, generates stereoisomers, determines the most probable ionization state at the user-defined pH, neutralizes charged structures, adds hydrogens and generates the possible number of tautomers. An energy minimization of the bioactive conformers was performed using ConfGen, by applying the OPLS-2005 force field.

The conformational space was explored through a

combination of Monte-Carlo Multiple Minimisation (MCMM) and Low Mode (LMOD) approach.

The

maximum number of conformers was set at 1000 per structure and the number of minimization steps was maintained at 10000.34 Each minimized conformer was filtered through a relative energy window of 50 kJ/mol and a redundancy check of 2Å in the heavy atom positions.



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The compounds under investigation shared the same assay procedure with wide variations in their potency profiles. The dataset was made in such a way that highly active, moderately active and least active compounds to develop a predicative model. The hMAO-B inhibitory potencies of the dataset compounds, reported as Ki values, spanned a wide range from 0.11 to 9.30 µM and were converted into pKi values. To perform 3D-QSAR, the dataset was divided into: 1) a training set comprising 19 molecules, and 2) a test set of seven molecules. The training and test sets were used to validate the developed model. The threshold pKi values for actives and inactive molecules were fixed to 5.53 and 6.5 respectively.

This further compartmentalised the dataset

molecules, i.e. five active, two inactive and 19 moderately active compounds.

This was utilised in the

development of the pharmacophore model and a measure for the subsequent scoring function. 2.1.2. Scoring hypothesis The resulting pharmacophores were then scored and ranked. The scoring algorithm included the contributions from: 1) the alignment of site points and vectors, 2) volume overlap, 3) selectivity, 4) number of ligands matched, 5) relative conformational energies, and 6) activity.35 2.1.3. Perceiving common pharmacophores The quantitative pharmacophore model was built using the Phase module (Schrödinger).36 The generated common pharmacophore hypotheses were examined by scoring the alignment of actives against a reference ligand by using default settings. The scoring protocol provided a ranking of different hypotheses to choose the most appropriate one for further investigation. The inactive molecules were scored in order to observe the alignment of these molecules with respect to the different pharmacophore hypotheses to select the best fit molecules. The larger the difference between the score of active and inactives, the better the hypothesis in discriminating the active from inactive molecules. 2.1.4. Building 3D-QSAR models using Partial Least Square (PLS) analysis The QSAR models were developed from the selected series of molecules.

The molecules although

presenting varying degrees of activity, were aligned to a common pharmacophore hypothesis that was associated with a single reference ligand. All hypotheses that were successfully generated and scored, were then used to build atom-based 3D-QSAR models with grid spacing 1.0 Å, a random seed value of zero, and three PLS factors. Statistics on the correlation of the predicted with actual activity, were performed for the top ten scoring hypotheses, by the default hypothesis scoring functions.

Each of the developed 3D-QSAR models were

validated by predicting activities of seven test set molecules. The predictive ability of the models was measured by means of the Pearson-R value .37


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2.2. Quantum chemical calculation The molecular geometry optimization was carried out in gas phase with the Gaussian 09W software package using the density functional theory, DFT/B3LYP combined with the standard 6-311++G(d,p) basis set .38 DFT employs the B3LYP keyword which, invokes Becke’s three parameter hybrid method i.e. Becke used the correlation function of Lee et al.39 The molecular electrostatic potential (MEP) analysis was performed with the same level of theory as used for optimization. The electrostatic potential contour map with the negative region (coloured red) is related to the electron rich portion of the molecule where the positive region (coloured blue) is related to the deficiency of the electron cloud. Natural bond order (NBO) analysis was performed using the Gaussian 09W package at the B3LYP/6-311++G(d,p) level in order to understand various second-order interactions between the filled orbital’s of one sub-system and vacant orbital’s of another sub-system. By this method the intra-molecular delocalizations or hyper conjugations can be measured.40 2.3. Molecular dynamics study The experimental data revealed that the brominated series of thienyl chalcones were the more potent hMAOB inhibitors than the chlorinated series. Thus further study of the three experimentally potent molecules (Tb5 (Ki= 0.11 µM), Tb6 (Ki=0.19 µM), Tb9 (Ki=0.25 µM), using molecular dynamics (MD) was performed. MD of these systems will provide insight into the specific molecular interactions between hMAO-A and hMAO-B enzymes when in complex, at an atomic level. Unrestrained normal and accelerated molecular dynamics simulations (NMD and aMD respectively) were conducted for the systems of Tb5, Tb6 and Tb9 bound with hMAO-A (PDB: 2BXR) and hMAO-B (PDB: 2BYB). The Amber 14 GPU based PMEMD programme was used to perform the MD.41 The protein structures were parameterized with amber force field (leaprc.ff12SB) whilst the force field of the ligands Tb5, Tb6 and Tb9, were derived using the restrained electrostatic potential (RESP) method of the R.E.D package. This method uses Gaussian 09 with functional/basis set HF/6-31G* for optimization and calculation of charges as well as Amber tools for the atomic partial charges fitting.42 The MD studies were carried out in an explicit solvent of an orthorhombic box of TIP3P water molecules with a cut-off of 8 Å.43 The hydrogen atoms of the two receptors were added using the tLeap module of Amber 15.

Three Cl- and two Na+ counter ions were added for the neutralization of hMAO-A and hMAO-B,

respectively. The Partial Mesh Ewald (PME) method was used for long-range electrostatic potentials with vdW cut-off of 12 Å.44 Energy minimization of the systems were carried out first with strong constraints on both ligand and the protein for 1000 steps (500 steepest descent followed by 500 steps of conjugate gradient)



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followed by a second minimization which was carried out for 1000 steps with the constraint limited to the ligand atoms. The systems were gradually heated to a temperature of 300.00 K using a canonical ensemble (NVT) over 50 ps using a Langevin thermostat with a collision frequency of 1.0 ps-1 and a harmonic restrained of 5 kcal/mol Å on the solutes. The density of the system was equilibrated for another 50 ns in the NPT ensemble. Followed by a final equilibration at 300.00 K temperature, 1 bar pressure and a coupling constant of 2 ps was carried out for 500 ns (NMD) and 10 ns (aMD). The MD trajectory frames for both normal and accelerated MD were recorded at every 500 steps of simulation at a time step of 2 fs throughout the simulation. The SHAKE algorithm was used to restrain all bond lengths involving hydrogen atoms.45 The conformational changes in the receptors during the MD trajectory were analysed using BIO3D.46 The results of the MD simulation were visualized and their graphical representation were done using VMD , Chimera , Pymol and Ligplus. Both Poisson-Boltzman (MM-PBSA) and Generalized Born (MM-GBSA) methods were applied for the computation of the binding free energy of the six interacting complexes (Tb5:hMAO-A, Tb6: hMAO-A, Tb9:hMAO-A, Tb5:hMAO-B, Tb6:hMAO-B and Tb9:hMAO-B) using a total of 1000 snapshots taken from 10000 ps of MD trajectory at interval of 10 ps for each system. The final binding free energy is represented with the equation (1): ∆G0bind =∆G0Gas, complex +∆G0solv, complex – [∆G0receptor +∆G0ligand] (1) Solvation free energies were calculated as contribution from electrostatic by either solving the linearized Poisson-Boltzman (GPB) or Generalized Born equation (GGB) with addition of empirical term for hydrophobic contributions (GSA). The GSA can be calculated from the solvent accessible surface area (SASA) equation (2). ∆G0solv = ∆G0elect, ε =80 - ∆G0elect, ε =1 +∆G0hydrophobic

(2)

The free energy in vacuum can be approximated as the average interaction energy between receptor and ligand, and putting the entropy change upon binding into consideration in equation (3). ∆G0Gas =∆E0MD -T. ∆S0NMA

(3)

References (1) Murphy, D.L. Substrate-selective monoamine oxidases: Inhibitor, tissue, species and functional differences. Biochem. Pharmacol. 1978, 27, 1889–1893.


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Table of Content (TOC)



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Fig. 1. Common pharmacophore distance [H3= hydrophobic group; A1= = carbonyl group; R4= thiophene ring; R5= phenyl ring]

(a)

(b)

Fig. 2. (a) Graph of training set; (b) Graph of test set

ACS Paragon Plus Environment

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Fig. 3. Residual scale

Fig. 4.. Electron withdrawing contribution of Tb5 (red cube)

Fig. 5.. Hydrogen bonding contribution of Tb5 (blue cube)



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Fig. 6. Hydrophobic contribution of Tb5 (blue cube)

Fig. 7. Molecular electrostatic potential of Tb5

Br

N O Br

O

O

S

Br compound 10 Tb5

S compound 11 Tb6

Br

Fig. 8. Structures of potent thiophene based chalcones.


S compound 13 Tb9

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


a1

a2

b1

b2

Fig. 9. The potential energy (EPTOT) of Tb5, Tb6 and Tb9 complexes with a1): MAO-A during NMD; a2): MAO-A during aMD; b1): MAO-B during NMD; b2): MAO-B during aMD

a1

a2

b1

b2

Fig. 10. The RMSD of Tb5, Tb6 and Tb9 complexes with a1): MAO-A during NMD; a2): MAO-A during aMD b1): MAO-B during NMD; b2): MAO-B during aMD



GBSA-NMD

PBSA-NMD

Tb5:A Tb6:A 0 Binding Energy

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GBSA-AMD

Tb9:A

Tb5:B

PBSA-AMD

Tb6:B

Tb9:B

-100

-35.895 -29.842 -33.287 -43.863 -42.349 -40.086 -14.309 -18.846 -19.85 -29.147 -28.319 -27.854 -30.888 -33.863 -33.905 -19.086 -39.985 -20.34 -45.82 -42.075 -21.368

-120

-29.094 -26.927

-20 -40 -60 -80

-140

-31.739

-160

Fig. 11. The changes in the total free energy of the Tb5, Tb6 and Tb9 interacting with MAO-A and MAO-B using the MM-PBSA and MM-GBSA methods from the trajectory of the normal MD and aMD


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a1

a2

b1

b2

Fig. 12. Residues with significant contribution to the binding energy (≥ 0.2 Kcal/mol) of ligands with a1): MAO-A during NMD; a2): MAO-A during aMD; b1): MAO-B during NMD; b2): MAO-B during aMD computed using both the MM-GBSA and MM-PBSA



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

1a

2a

3a

1b

2b

3b

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Fig. 13. The conformational variation (separated by different colours in black, red and green) of the receptor along the first three PC1, PC2 and PC3 from the NMD for the interaction of the ligands with MAO-A (1a, 2a, 3a) and with MAO-B (1b, 2b, 3b). The point of the lowest energy structure is marked with circle on the potential energy surface


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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


a

1a

b

b

2a

c

d

a

b

1b c

a

c

a

c

b

3a d

d

c

b

2b d

a

a

b

3b d

c

d

Fig. 14. a): The PC1 versus PC2 plots; b): The potential energy surface (PES) from the aMD reweighing script c): The residues fluctuation on the PC1 (black) and PC2 (blue); d): the PES plotted from change in energy of each trajectory from initial trajectory during aMD for the interaction of the ligands with MAO-A (1a, 2a, 3a) and with MAO-B (1b, 2b, 3b)



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

a

a

b

b

2a

1a

c

2b

1b c

c

a

b

a

b

b

a

3a

c

a

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b

3b c

c

Fig. 15. a): The root mean square fluctuation (RMSF) of the residues; b): The NMA prediction of the fluctuation of the average structure of each of the three groups of the PCA; c): The structure of the average conformation obtained for each of the three group during the aMD for the interaction of the ligands with MAOA (1a, 2a, 3a) and with MAO-B (1b, 2b, 3b)


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Fig. 16. The structure of the best conformation obtained for the interaction of the ligands with MAO-A (1a, 2a, 3a) and MAO-B (1b, 2b, 3b) during the NMD (grey) and aMD (cyan)



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MAOB MAOA

Fig. 17. 3D structure of the best conformation for the interaction of Tb5 (yellow) with MAO-A and MAO-B compared with Tb6 (blue) and Tb9 (orange) from the aMD


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1a

1b

2a

3a

2b

3b

Fig. 18. The binding site interaction of the ligands with the residues receptors from the best conformation obtained during the aMD



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1b

1a

2a

2b

3a

3b

Fig. 19. The correlation (red lines) and anti-correlation (blue lines) in the residues of the receptors during the aMD simulation obtained using DCCM method for ligands interaction with MAO-A (1a, 2a, 3a) and with MAO-B (1b, 2b, 3b)


Pharmacophore-Based 3D-QSAR Analysis of Thienyl Chalcones as a

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