Novel MAO-B Hit Inhibitors Using Multidimensional Molecular

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Novel MAO-B Hit Inhibitors Using Multidimensional Molecular Modeling Approaches and Application of Binary QSAR Models for Prediction of their Therapeutic Activity and Toxic Effects Yusuf Serhat Is, Serdar Durdagi, Busecan Aksoydan, and Mine Yurtsever ACS Chem. Neurosci., Just Accepted Manuscript • DOI: 10.1021/acschemneuro.8b00095 • Publication Date (Web): 19 Apr 2018 Downloaded from http://pubs.acs.org on April 19, 2018

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ACS Chemical Neuroscience

Novel MAO-B Hit Inhibitors Using Multidimensional Molecular Modeling Approaches and Application of Binary QSAR Models for Prediction of their Therapeutic Activity and Toxic Effects Yusuf Serhat Is1,2,4, Serdar Durdagi1,3*, Busecan Aksoydan1,3, Mine Yurtsever2,* 1

Computational Biology and Molecular Simulations Laboratory, Department of Biophysics, School of Medicine, Bahcesehir University, Istanbul, Turkey; 2Department of Chemistry, Istanbul Technical University, Istanbul, Turkey; 3Neuroscience Program, Graduate School of Health Sciences, Bahcesehir University, Istanbul, Turkey, 4Vocational School Department of Chemistry Technology, Istanbul Gedik University, Istanbul, Turkey

ABSTRACT Monoamine oxidase (MAO) enzymes MAO-A and MAO-B play a critical role in the metabolism of monoamine neurotransmitters. Hence, MAO inhibitors are very important for the treatment of several neurodegenerative diseases such as Parkinson’s disease (PD), Alzheimer’s disease (AD), and amyotrophic lateral sclerosis (ALS). In this study, 256750 molecules from Otava Green Chemical Collection were virtually screened for their binding activities as MAO-B inhibitors. Two hit molecules were identified after applying different filters such as high docking scores and selectivity to MAO-B, desired pharmacokinetic profile predictions with binary QSAR models. Therapeutic activity prediction, as well as pharmacokinetic and toxicity profiles were investigated using MetaCore/MetaDrug platform which is based on a manually curated database of molecular interactions, molecular pathways, gene-disease associations, chemical metabolism and toxicity information. Particular therapeutic activity and toxic effect predictions are based on the ChemTree ability to correlate structural descriptors to that property using recursive partitioning algorithm. MD simulations were also performed to make more detailed assessments beyond docking studies. All these calculations were made not only to determine if the studied molecules had the potential to be a MAO-B inhibitor but also to find out whether they carried MAO-B selectivity versus MAO-A. The evaluation of docking results, pharmacokinetic profile predictions together with the MD simulations enabled us to identify a hit molecule (Ligand 1, Otava ID: 3463218) which displayed higher selectivity towards MAO-B than a positive control selegiline which is a commercially used drug for PD therapeutic purposes.

Key words: MAO-A; MAO-B; docking; binary QSAR models; molecular dynamics (MD) simulations; BBB (blood brain barrier) prediction; G-log P (lipophilicity) prediction

* E-mails: [email protected] (SD); [email protected] (MY)

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1. INTRODUCTION Monoamine oxidase (MAO) is a flavin adenine dinucleotide (FAD) enzyme bound to mitochondrial outer membrane and especially localized in the liver, brain, placenta and intestine.1 The function of MAOs is to catalyze the oxidative deamination of monoamine neurotransmitters like dopamine, serotonin and norepinephrine. The oxidized products are non-enzymatically hydrolized to the corresponding aldehydes together with the production of hydrogen peroxide, H2O2.2,3 There are two isoforms of MAO (i.e., MAO-A and MAO-B) which are encoded by distinct genes and they have ~70% sequence identity.2 These isoforms are differentiated by their substrate and inhibitor sensitivity4-6 and also tissue/cellular distribution.7 Both isoforms contain cofactor FAD which is covalently attached to the enzyme via a Cystein residue; Cys406 and Cys397 in MAO-A and MAO-B, respectively.8,9 FAD is the most important part of the MAOs since the oxidative effect of the enzyme highly depends on this cofactor. Functional MAO-A and MAO-B proteins are homodimer and each subunit consists 527 and 520 aminoacids, respectively.10,11 MAO-A is generally localized in catecholaminergic neurons, preferably deaminates serotonin (5HT) and norepinephrine (NE) and selectively inhibited by low concentration of clorglyine, however MAO-B is mainly found in serotonergic neurons and astrocytes, preferentially oxidase β-phenylethylamine (PEA), benzylamine and selectively inhibited by selegiline (L-deprenyl) irreversibly. There are also common substrates which are metabolized by both MAO-A and MAO-B such as dopamine, epinephrine, tyramine, tryptamine (dietary amine).7 MAOs play a critical role in the metabolism of monoamine neurotransmitters. Hence, MAO inhibitors (MAOI) are very important for the treatment of several neurodegenerative diseases such as Parkinson’s disease (PD), Alzheimer’s disease (AD), amyotrophic lateral sclerosis (ALS), etc. The therapeutic effects of MAOI depend on the isoforms they exhibit their inhibitory properties. For instance, while MAO-A inhibitors are genereally used in the treatment of depression and anxiety, MAO-B inhibitors are especially used in the treatment of PD and AD.12-14 In human brain, the activity of MAO-B increases with age especially in PD. Thus, harmful metabolites produced by MAO-B may increase and it may lead neurodegeneration.15 Selective MAO-B inhibitors such as selegiline and rasagiline are used alone but sometimes these drugs can be applied in combination with levodopa which is the prodrug of dopamine in sympthomatic treatment of PD. The aim is to reduce the metabolic depletion of dopamine.12,13 Selegiline and rasagiline are both irreversible MAO-B inhibitors and they can cause serious pharmacological adverse effects in the long term treatment of PD. The previously used MAOI, like iproniazid, are


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irreversible and nonselective with a wide range of side effects. Especially irreversible MAO-A inhibitors induce cheese reactions which is a hypertensive crisis due to consumption of food containing great amount of tyramine.16,17 However newer reversible MAO-A inhibitors can be easily switched by tyramine, thus they do not cause this harmful reaction. Those who use these irreversible MAO-A inhibitors may go on dietary restriction and especially stay away from cheese because of its tyramine ingredient. As it is mentioned, the cerebral activity of MAO-B increases with aging and it leads to diverse neurodegenerative diseases like AD, PD and Hungtington’s disease (HD). In these disorders the inhibiton of MAO-B not only increases the amount of monoamine neurotransmitters but also decreases the level of H2O2 and hydroxyl radicals, in this way the oxidative stress in brain cells is reduced.18 Therefore, MAO-A and MAO-B are crucial targets for the optimization of selective and reversible lead inhibitors. The selectivity of MAO-B inhibitors is essential to avoid the serious side effects which can occur in long term PD treatment. Choi et al.19 have synthesized a number of unsaturated ketone derivatives and evaluated their MAO-A, MAO-B binding activities as well as the MAO-B selectivity. Two structures among the studied molecules have been found to be potent MAO-B inhibitors. According to their docking results, while two most potent MAO-B inhibitors showed π-π stacking interactions with Tyr326 and Tyr398, they constructed hydrophobic interactions with Leu171, Ile199 and Phe343 residues in the active site of the MAO-B. The authors have suggested that Tyr326 could play a crucial role in the binding site.19 In some studies, especially Leu171, Ile199 and Tyr326 have been shown to be instrumental residues in the binding pocket of MAO-B.20,21 Caulerpin is an alkaloid mainly isolated from the genus Caulerpa has an activity for MAO-B. Lorenzo et al.22 have utilized Volsurf descriptors and Random Forest machine learning algorithm in parallel with docking protocols to predict the activity of this compound at the MAO-B. Together with caulerpin, a set of 108 caulerpin analogs were also evaluated and among these, 9 compounds were determined which can be potential candidates for the inhibition of MAO-B. Jo et al. 23 have done a theoretical study combined with experimental assays using chromenchalcones derivatives. They utilized structure-activity relationship (SAR) studies to obtain MAO-B inhibitors. A few derivatives were found active and synthesized according to these computations. Furthermore, in silico docking studies were performed to evaluate the binding modes of synthesized compounds with active site of MAO-B.23 In another study, a series of


(E)-3-


heteroaryldenechroman-4-ones have been designed, synthesized and evaluated for the inhibition of MAO-A and MAO-B.24 The synthesized molecules showed potent MAO-B inhibition and selectivity. One of these compounds displayed stronger MAO-B activity than the commercially available selegiline and also exhibited reversible property for MAO-B. Docking studies, molecular dynamics (MD) simulations and SAR were carried out in addition to the experimental studies. They have tried to explain the importance of water molecules and hydrogen bonds during binding of ligands into the active site of MAO-B.24 Xie et al. synthesized several tacrine-coumarin compounds and assessed them for selective inhibition of MAO-B. One ligand has been found to be a potent and selective MAO-B inhibitor among these molecules. Docking calculations and MD simulations were performed for this ligand to determine the binding positions and structural stability in the active site of the enzyme.25 In the current study, approximately 256750 molecules were retrieved from Otava Chemical Database (drug-like green collection) to identify novel MAO-B inhibitors by high throughput virtual screening (HTVS) and various docking protocols. Otava drug-like green collection compound library was prepared on the basis of screening compounds for prompt delivery and preformated by Lipinski’s rules of 5. It must be noted that the library does not include reactive groups and biologically unstable compounds. The candidate ligands for inhibition of MAO-B obtained from these docking calculations were further filtered using topology-based binary QSAR models. Therapeutic activity prediction and toxicity QSAR models were applied via MetaCore and MetaDrug tools to predict their first and second-pass metabolism, absorption, distribution, metabolism, excretion (ADME), therapeutic activities, and toxicity properties. After these evaluations, the two most potent candidate molecules as MAO-B inhibitors were determined by making use of both docking and binary QSAR models. In addition, MD simulations were performed to investigate structural and dynamical profiles of these two identified hit molecules as well as selected positive and negative control molecules in the active site of the MAO-B. Furthermore, MAO-B selectivity is also studied. 2. RESULTS AND DISCUSSION In the present study, we screened 256750 small organic molecules from Otava ligand library using combined ligand- and target-based virtual screening approaches to identify selective MAO-B inhibitors. According to the screening results, some of ligands which are thought to carry potential to be a MAO-B inhibitor were further studied with binary QSAR models implemented in MetaCore/MetaDrug suite for their therapeutic activity predictions and toxicity calculations. The two hit molecules among these studied compounds that have a potential to show an


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inhibition for MAO-B have been identified according to IFD/QPLD docking and MetaCore/MetaDrug analysis results. Besides, these two hit molecules were docked into the active site of MAO-A to investigate their superiority against MAO-B selectivity. Moreover, MAO-B and also MAO-A complexes of these two hit ligands were subjected to MD simulations to investigate their structural and dynamical properties. At the end of the combined docking, MD and binary QSAR studies, one of the two identified hit ligands (Ligand 1, Otava ID: 3463218) was found to be more potent and selective MAO-B inhibitor compared to standard approved MAO-B inhibitor selegiline. In previous published reports, crucial amino acids that may form π stacking interactions (i.e., Tyr326, Tyr398), prevalent role of steric interactions along with the important of hydrophobic interactions with the ligands have been highlighted.19,26 It is known that Leu171, Ile199 and Tyr326 residues of hMAO-B are gate-keeper amino acids.20,21 D’Ascenzio et al.27 performed perresidue interaction energy calculations, and they found Ile199 as one of the most crucial amino acids in the MAO-B inhibition. As described earlier, a large aromatic cage is formed in MAO-B by Phe343, Tyr188, Tyr189, Tyr435 and Tyr398 and also a hydrophobic pocket is characterized by Leu171, Tyr326, Phe168, Ile198 and Ile199.28 In an another study, it is shown that one of the studied molecules occupied the specificity pocket formed by the residues Cys172, Gln206, Phe343, Tyr398 and Tyr 435.29 It is known that Tyr398 residue is critical in the substrate cavity of MAO-B for π-π stacking interaction.30 It is revealed in the same study that the following residues Leu164, Phe168, Ile199, Ile316 and Tyr326 located in the entrance cavity of MAO-B are crucial amino acids for electrostatic interactions.30 Several studies explained that Ile199 changes its conformation and allows for the occupation both the entrance and substrate binding cavities.20,31,32 Hence, it can be concluded that Ile199 is a crucial gating residue in MAO-B active site. In a different study, the authors showed that a hydrophobic cage is formed within the active site of MAO-B by π-stacked tyrosine residues 398 and 435.30,33 Tzvetkov et al.34 identified Cys172, Tyr188, Ile199, Tyr201, Gln206 and Tyr326 residues as critical residues for constructing H-bonds at the binding cavity. MAO-B has a binary active site; small entrance room and the second larger inner cavity where FAD is located.31 Two “gate-keeper” residues (i.e., Ile199 and Tyr326) are in charge of this binary shape. Patil et al.35 showed that their studied compounds mainly occupy the entrance hydrophobic cavity which is formed by Phe103, Pro104, Leu174, Phe168, Leu171, Cys172, Ile199, Ile316 and Tyr326 residues. They showed that while van der Waals interactions are mainly observed with Ile199, Phe168, Gln206 and Tyr326, electrostatic interactions were mainly observed with Tyr326 and Leu164 residues. In another study, the



handled molecules form weak π-π stacking interactions with Tyr326 and Tyr398. However, Tyr435 residue can build up H-bonds with the several studied ligands.36 Therefore, these critical residues mentioned above have been analyzed carefully in the course of docking protocols and MD simulations performed in this study. 2.1. Structure-Based Virtual Screening Virtual screening method was used to isolate potential binders from the weak and inactive ones. This process is schematized in Scheme 1. As seen on this scheme Glide/HTVS protocol was performed to eliminate the ligands having low docking scores. One docking pose per ligand was obtained from HTVS. According to the HTVS results the highest and the lowest scores are -10.3 kcal/mol and +1.8 kcal/mol, respectively. We selected 25675 ligands (i.e., 10% of whole molecules) having top docking scores and docked into the active cavity of MAO-B by using Glide/SP protocol. Then, 6247 ligands were chosen according to Glide/SP results and subjected to further docking procedure by Glide/XP method. Finally, 312 molecules which had high docking scores and also established strong binding interactions with the known crucial amino acids were selected and exposed to docking studied by Glide/IFD, Glide/QPLD protocols. MetaCore/MetaDrug applications were also used for these 312 final molecules to investigate their binding potentials and pharmacokinetic properties such as toxicity prediction, number of metabolites and distribution parameters in human body. Two potential inhibitor molecules of MAO-B (Ligand 1, Otava ID: 3463218 and Ligand 2, Otava ID: 7720500779) were identified considering the therapeutic predictions, the number of metabolites and toxicity prediction values. The experimental IC50 values of selegiline and the inactive ligand for MAO-A and MAO-B are shown in Table 1. Docking scores and pharmacokinetic results of the two hits and the reference ligands (for positive and negative controls) are given in Tables 2 and 3, respectively. While Figure 1 demonstrates constructed MAO-B/ligand complex systems, Figure 2 represents the 3D and 2D ligand interaction diagrams and molecular structures of hit molecules and reference compounds, respectively. The docking poses of the ligands 1 and 2 show that the residues in the active site interacting with these ligands are the same as the ones reported in the literature. As seen in Table 2, the ligand 1 has the 11th top-docking score according to IFD results, and the ligand 2 has the 7th top-scored molecule with respect to QPLD results. These rankings are at acceptable levels in terms of the best score proximity. The compounds that have higher docking scores than the ligands 1 and 2 had high toxicity values and were predicted to produce many reactive metabolites according to the results of MetaCore/MetaDrug application. As a result, ligands 1 and 2 were


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chosen as hit molecules since they display strong binding ability and at the same time desired pharmacokinetic properties. 2.2. MetaCore/MetaDrug Applications When Table 2 is examined it can be seen that while ligand 1 may have a therapeutic potential (cut-off value for therapeutic potential is 0.5) against AD and PD, ligand 2 may show therapeutic activity for AD and depression. The values in brackets are Tanimato Prioritization (TP) values. A maximal Tanimato coefficient calculated for all molecules in a training set of a QSAR model indicates the similarity of the analyzed structure to the most similar compound in the training set. The high docking scores and the acceptability of the values of therapeutic activities alone may not be enough for the molecules we choose. Therefore, we have investigated the pharmacokinetic properties like BBB (blood brain barrier), G-log P (lipophilicity), Prot-bind% (binding to blood serum proteins) and Prot-bind, log t (binding to blood serum albumin proteins). These parameters are very important for candidate drug-like molecules. The BBB permeability is a critical parameter for the drugs having effects in the central nervous system (CNS). These ligands must penetrate BBB to show an activity in CNS. This parameter is an especially important parameter for MAO-B since it is more localized in CNS. The cut-off value of BBB permeability is – 0.3 in MetaCore/MetaDrug. When the BBB results are examined, it can be seen that the values of both ligands 1 and 2 are higher than the cut-off value indicating that they have a potential to pass through the BBB. The G-log P parameter gives us information about lipophilicity or in other words, the permeability capacities of molecules through a lipid membrane. Since MAO-B is a peripheral protein bounded to outer membrane of mitochondria, lipophilicity is also crucial for MAO-B inhibitors. As seen in Table 2, both ligands 1 and 2 have acceptable G-log P values. The Prot-bind% parameter indicates the connectivity potentials of the molecules to the blood serum proteins. Binding of drugs to these proteins in the blood serum is an undesirable and Prot-bind% parameter indicates the connectivity potentials of the molecules to the blood serum proteins. As seen in Table 2, both ligands have relatively high values of Prot-bind%. However, when their values compared to the FDA approved MAO-B inhibitor selegiline, it can be seen that ligand 2 has smaller Prot-bind% compared to selegiline. Corresponding result of ligand 1 has slightly higher values. Another evaluated parameter was Prot-bind, log t. This parameter indicates the binding properties of ligands to the albumin proteins in blood serum. In this investigation, the logarithm of the retention times of studied ligands in a chromatography column are taken into consideration. While the negative values show the low binding potential, positive values indicate the opposite (i.e., high binding potential). According to the results seen in Table 2, both of the



two ligands have negative Prot-bind, log t values as desired, but very interestingly, selegiline has positive log t values. The selected hit ligands 1 and 2 were also analyzed with 26 different toxicity QSAR models such as mutagenecity, carcinogenecity, cytotoxicity, anemia, genototoxicity, hepatotoxicity, liver necrosis, neurotoxicity, etc. They have lower calculated toxicity values than the cut-off values in QSAR models indicating that they may not be toxic. (Table S1, Supplementary Materials) The possible metabolites of the ligands 1 and 2 were also determined. The first pass major metabolites which are substances, occurring during the metabolization of drugs taken orally, were taken into consideration. Both ligands were found to have only one reactive metabolite. Since QSAR models are used for pharmacokinetic prediction studies, their statistical information should also be provided. Not only the results of the evaluated parameters are within the acceptable limit values, but also the statistical data such as the size of training set, correlation coefficients and standard error values of the models are important. The statistical information about QSAR models of all parameters employed for the ligands 1 and 2 is shown Table S2, Supplementary Materials. The Ligand 1 and Ligand 2 are very promising candidate drug-like molecules for several reasons: (i) the precision and the accuracy of the statistical methods we employed is trustworthy since the number of molecules and the range of the activity in the training and test sets are quite high, (ii) the important statistical parameters of the therapeutic activity QSAR models such as sensitivity, specificity and accuracy are at the desired levels. Although RMSE values of all other ADMET parameters are very low in QSAR models, the corresponding Prot-bind% parameter is relatively high and this could be the reason for higher predicted values than expected for the Ligand 1 and 2. 2.3 MAO-B Selectivity The IFD and QPLD docking calculations performed for MAO-B have been also repeated for MAO-A in order to predict the selectivity potentials of ligands 1 and 2 against A and B isoforms of MAO. (Table 4) The IFD docking score of ligand 1 at the MAO-B is found as -13.0 kcal/mol and the difference in estimated binding energies of ligand 1 and inactive molecule can be seen clearly. Similarly, the ligand 2 has docking score of -12.4 kcal/mol at the binding pocket of MAO-B and the difference in binding energy is remarkable in comparison to the QPLD score of inactive ligand. It can be also seen that both IFD and QPLD scores of the ligands 1 and 2 are much higher than those of selegiline. (Table 4)


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There is an energy difference (∆∆G) of 4.6 kcal/mol and 0.6 kcal/mol between the IFD docking scores of ligand 1 and ligand 2 at the MAO-B and MAO-A, respectively. In this case, the predicted binding affinity of ligand 1 and ligand 2 to MAO-B is about 2254-fold and 2.7-fold greater than that of MAO-A, respectively (in terms of the equilibrium constant k, calculated by kMAO-B/kMAO-A= e-(∆(∆G)/RT) where RT= 0.5958 kcal.mol-1.K-1 at 300 K). According to the QPLD docking scores, the predicted binding affinity of ligands 1 and 2 to MAO-B is about 421-fold and 16896-fold greater than that of MAO-A, respectively. When the IC50 values for the inhibition of MAO-A and MAO-B by selegiline and rasagiline is considered, it can be said that a difference of more than 3 kcal/mol between the estimated binding energies of the two isoforms may be sufficient in terms of selectivity. When ligand 2 is examined, a slightly different situation arises. The MAO-B selectivity of ligand 2 according to the IFD scores may not be as high as in ligand 1. This result may suggest that ligand 2 may not be as selective as ligand 1 for MAO-B when the IFD scores is taken into account, on the other hand it can be seen that the result is slightly different when it is assessed considering the QPLD scores of ligand 2. The difference between the QPLD scores of ligand 2 is about 5.8 kcal/mol which corresponds to a 16900-fold in terms of calculated binding equilibrium constant. According to these data, the results are consistent within itself. However, in comparison with ligand 2, ligand 1 shows a remarkable selectivity properties for MAO-B with respect to both IFD and QPLD results. According to our docking results, the MAO-B selectivity of selegiline is not at desired levels compared to the ligand 1 and ligand 2. The predicted binding energies of the selegiline to both isoforms of MAO are very close to each other. Considering the measured binding affinities of selegiline in two isoforms, it can be deduced that docking score differences are underestimated with both docking methods. Hence, we can say that the ligands 1 and 2 may have a larger selectivity towards MAO-B. Docking studies are solely not sufficient to shed light onto the binding mechanisms and the dynamic properties of the two selected hits, hence MD simulations of protein-ligand complexes were carried out. 2.4. Molecular Dynamic (MD) Simulations Apo forms, MAO-B/ligand 1, MAO-B/ligand 2, MAO-B/selegiline, MAO-B/inactive ligand, MAO-A/ligand 1, MAO-A/ligand 2, and MAO-A/selegiline complexes were subjected to 50 ns and 100 ns MD simulations. Short simulations were repeated twice. Thus, in total, around 1.5 µs MD simulations were performed in this study. Structural and dynamical properties of the selected



molecules were analyzed via RMSD plots of the ligands and Cα atoms of protein, RMSF plots of critical residues in the active site; radius of gyration, molecular surface area and solvent accessible surface area (SASA) of the ligands. Time-dependent changes in the interaction of the ligands with crucial residues in the active cavity were also examined via interaction histograms and proteinligand contact graphs. 2.4.1. Protein Cα RMSD The average RMSD values the MAO-B complexes of ligands 1, 2, selegiline and inactive molecule were shown in Figure 3. As seen in this figure, in all the systems, average RMSDs of Cα locates between 2.35 to 2.48 Å and reach to a plateau after 10 ns. Since we used homo dimer structure of MAO-B (a ligand-bound-protomer (chain-A) and a ligand-free protomer (chain-B)); RMSD of the chain-A and chain-B were also analyzed, separately. (see Supplementary Materials, Figure S1) It can be clearly seen from these figures that especially hit molecules (ligands 1 and 2) have higher RMSD values at the protomer-A that may show penetration of molecules at the binding pocket. Ligand 1 is indeed bigger than that of selegiline as the molecular structure and therefore its effect to the structure can be expected larger. Since the inactive ligand has rather small structure and cannot fully accommodate the active site, the resulting complex may also show an unstable profile. The average Cα RMSD values of the MAO-B complexes of ligands 1, 2, selegiline, inactive compound and apo form are found as 2.48±0.12, 2.43±0.36, 2.48±0.22, 2.35±0.13 and 2.48±0.29Å, respectively. (10000 trajectory frames throughout 50-ns x 2 replicas were used). Corresponding analysis of individual protomers have been represented at the Figure S1. Together with RMSD of whole protein, RMSD of active site (i.e., 5 Å of residues from ligand) was also considered throughout the MD simulations. In that case, although all have similar RMSD values, selegiline and ligand 2 bound residues showed slightly higher RMSDs compared to others. (Figure S2) When the simulation time was doubled, no significant changes in the RMSD plots after 50 ns was observed. (Figure S3) 2.4.2. RMSD Lig-fit-Prot The RMSD values of the ligand along the MD simulations were also calculated from the coordinates of the atoms in the protein (i.e., translational profiles of the ligands at the binding pocket). RMSD plots of the ligands are useful to see how stable the studied ligand is at the binding pocket with respect to the protein. RMSD Lig-fit-Prot demonstrates the RMSD of ligand when the complex is first aligned on the protein backbone of the reference (i.e., initial position) and then the RMSD of the heavy atoms of the ligand is measured. If the values observed are


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significantly larger than the RMSD of the protein, then it can be interpreted that the ligand may diffuse away from its initial binding site. Consistent results with the active site RMSD data were obtained. Although selegiline and ligand 2 have larger RMSD Lig-fit-Prot values, corresponding values for ligand 1 is much lower especially after second half of the simulations. (Figure 4) 2.4.3. Critical Amino acids The critical amino acids obtained from the examination of docking poses overlap with the results obtained from MD simulations. Interaction histogram of ligand 1 shows that Pro102, Ile198, Ile199, Gly205, and Tyr326 residues are crucial residues. (It must be noted that trajectory frames of one replica from 50-ns MD simulations was used for the generation of histogram and proteinligand contact diagrams, however both replicas were checked carefully and it is observed that both of them produces very similar data). Ile198 mainly forms H-bonding interactions with ligand 1 from backbone. Ile198 constructs also other chemical interactions such as hydrophobic and water-bridge interactions in the active site (Figure 5). The establishment of more than one types of interaction with this aminoacid and especially the predominance of the hydrogen bonds may have contributed to the structural stability of the ligand 1 in the active site. In many previous studies, Ile199 were reported as crucial residue, the ligand 1 forms chemical interactions with Ile199 mainly through water-bridge interactions. The number of critical aminoacids that ligand 2 interacted during MD simulation is not as many as ligand 1. (Figure 6) Interaction histogram diagram shows that crucial amino acids for ligand 2 are Tyr188 (hydrogen bond and hydrophobic), Ile198 (hydrophobic), Tyr398 (hydrophobic), Gly434 (water bridges), and Tyr435 (hydrogen bond and hydrophobic). The interaction histogram of selegiline is shown in Figure S4. It can be seen from this histogram that selegiline has mainly hydrophobic interactions with Ile198, Ile199, Tyr326, Tyr398 and Tyr435 residues. Selegiline does not have strong interactions with critical residues compared to ligand 1 but exhibits a profile almost at the same level as ligand 2. The RMSD values of ligand 2 and selegiline already provided clues in the same direction. (see Figures 3 and 4) It is necessary to examine the interaction histogram of the inactive compound to make the same kind of evaluation. It can be understood that there is a little different situation about the inactive ligand when the Figure S5 at the supplementary materials is examined. As shown in this figure, the inactive ligand also interacts with important amino acids and it also has ionic interactions that none of the others do. This may lead to the conclusion that the inactive compound may have tight binding interactions in the active site. Indeed, in both RMSD graphs of protein, as well as Lig-fit-Prot graph demonstrates relatively low RMSD values for the inactive molecule. This result is contrary to the experimental data of the inactive compound. Ligand 2 can



be protonated in an aqueous medium and it may form ionic interactions. Besides, the inactive molecule is quite small compared to ligand 1 and it is clear that the active cavity of MAO-B cannot be accommodated well with this ligand. Examining the interaction histograms alone may give some misleading results, thus it is necessary to compare each data set with the others. Interaction histograms provide data on which type of interactions as well as average interactions fractions throughout the simulations for the studied ligands in the active site, but they do not show the time-dependent distribution of these interactions during MD simulations. The proteinligand contact graphs must be examined to see if the ligands are in contact with the crucial aminoacids over time. These graphics can support the results obtained earlier. Figure 7 shows that ligand 1 has contacted to Ile198 which forms hydrogen bonding interactions during MD simulations without a major interruption. It was also found in long-term contacts with the wellinteracted Gly205 residue via water bridge. The number of residues that ligand 2 contacts is not as large as ligand 1. (Figure 8) Ligand 2 appears to form a continuous interaction with Tyr188, Gly434 and Tyr435 residues throughout the MD simulations. As seen in this figure the contacts of ligand 2 with Ile198 which is one the most important residues remain limited. When both interaction histograms and the contact graphs are taken into account, it can be concluded that the structural stability of ligand 2 in the active site and the MAO-B/ligand 2 complex may be lower than that of ligand 1. Corresponding figures for complexes of selegiline and inactive molecules have been shown in Figures S6 and S7 at the Supplementary Materials. It is observed that selegiline was in contact with similar critical residues like Tyr326, Tyr398, Tyr435, but it did not form such a continuous interaction throughout the simulation with Ile198 and Ile199. Thus, this result may have caused the selegiline to lag behind the ligand 1 in terms of structural stability (i.e., it may have different binding conformations from its initial binding site interactions, see Figure 4 (top)). Briefly, interaction histograms and contact graphs show us that ligand 1 can significantly form more stable complexes with MAO-B than ligand 2 and commercially available selegiline molecule. This result shows a great consistency with our earlier comments. However, the RMSF values of critical amino acids and free energy calculations must be examined to make more detailed assessments. 2.4.4. RMSF of Critical Residues Common amino acids interacted by each studied molecule were identified and a total of seven zoom regions at the binding pocket were formed considering several residues linked to these common ones. The common amino acids interacted with all studied molecules and in which


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zoom regions they belong are given Table 5. It could be seen in Figure 9 that mainly all of regions have RMSF values up to 1.5 Å, however zoom-region II (Z-II) that includes Ile198, Ile199, Ser200, Thr201, Gly205, and Gln206 has slightly larger RMSF values compared to other regions. When the torsional angle profiles of ligand 1 is examined, it can be seen that mainly all dihedral angles do not show a very diverse change from its initial values, except τ1, τ3 and τ6 which shows that ligand 1 do not change its initial topology dramatically because of its strong interactions with the binding pocket residues. However, because of these strong interactions it penetrates the target structure. 2.4.5. Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) Calculations The free energy calculations of selected hits were performed together with the known ligand selegilin and negative control molecule using MM/GBSA. For this aim, trajectory frames from last 50% of MD simulations were considered. Figure 11 shows the calculated binding energies of ligands at the MAO-B dimer protein. While ∆GMM/GBSA of selegiline was found as -52.45±4.21 kcal/mol, corresponding value for the selected hit ligands 1 and 2 were found as -90.11±4.65 and -62.63±4.18 kcal/mol, respectively. (Figure 11) Similar results were observed when last half of 100-ns MD simulations were considered. (Supplementary Materials, Figure S8) 2.4.6. Solvent Accessible Surface Area (SASA) of Ligands SASA is one of the most important parameters that a molecule displays during MD simulations. SASA values are calculated by circulating a probe molecule (i.e., a pseudo water molecule) around the ligand in the active site. In this process, the area of the curve drawn by the center of the probe molecule is calculated while this probe is moved around the ligand in such a way that the probe contacts the ligand being studied. This calculated area is called SASA. This parameter refers to the surface area that the solvent molecules can reach around the ligand in the active site during MD simulations. The high value of SASA means that the solvent molecules in the system are more likely to cover the periphery of the ligand, which may prevent the ligand from interacting with amino acids forming the active site. The time-dependent changes of the SASA values of the studied molecules are given in the Figure S9. From this figure, it can be seen that average SASA values of ligand 1 are lower than that of ligand 2 and inactive molecule. This result may mean that although ligand 1 has a large van der Waals surface area due to its molecular structure, it does not allow much of solvent molecules to enter between the residues and itself



because of the large number of contacts and good interactions it has with the critical amino acids in the active site. Interestingly, a very small average SASA value was observed for the selegiline. 2.4.7. MAO-B Selectivity versus MAO-A It is important to note that because of the 70% amino acid sequence identity between MAO-A and MAO-B enyzmes, it is quite difficult to capture high MAO-B selectivity for a molecule. One of the most significant criteria in our study is that one of the ligands we have found can show better MAO-B selectivity than commercially available selegiline molecule. The Cα RMSD graphs of both MAO-A/ligand and MAO-B/ligand complexes have been shown in Figure S10. As it can be easily interpreted from this figure, the complexes formed with MAO-B show structurally more rigid than when it formed with MAO-A. There is a big difference between RMSD values for these two complexes. Especially when the curve of MAO-A is examined, it can be clearly seen that the MAO-A/ligand complexes lead very large Cα RMSD values. 2.4.7.1. SASA calculations for MAO-A system The SASA parameter examined during MD simulations for MAO-B was also analyzed in simulations for MAO-A. The SASA curves of ligands in MAO-A and MAO-B are shown in Figure S11. As it can be seen from this figure, the SASA values of all ligands have similar values in MAO-A and MAO-B active sites, except ligand 2. SASA values of ligand 2 is higher in MAOB compared to MAO-A. 3. CONCLUSIONS In this study, ∼256750 molecules from Otava drug-like green collection database were evaluated for their binding activities as MAO-B inhibitors. 312 ligands were selected among these molecules using different modules of Glide. These ligands were then subjected to pharmacokinetic evaluations using MetaCore/MetaDrug platform as well as IFD and QPLD docking protocols. As a result of these calculations, two ligands with higher predicted affinity to MAO-B were selected. These two hits were tested also on MAO-A for their MAO-B selectivity. Apart from these studies, for negative control purposes a molecule obtained from literature which is inactive for MAO-B inhibition and for positive control a molecule called selegiline which is still being used commercially in clinic were subjected to docking calculations on the MAO-B and MAO-A using IFD and QPLD protocols. MD simulations and different postprocessing MD analyses were also performed to make more detailed assessments beyond docking studies.


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It must be noted that availability of binary QSAR models in both therapeutic activity, toxicity as well as diverse pharmacokinetic property prediction pipelines protected us to continue with false positive molecules (i.e., molecules have high docking scores but they may also show undesired pharmacokinetic profiles as well as they may produce many reactive metabolites). For this reason, we continued the analysis with two molecules (ligands 1 and 2) which are not indeed top-docking scored molecules. While ligand 1 has the 11th top-docking score according to IFD results, ligand 2 has the 7th top-scored molecule with respect to QPLD results. However, these rankings are at acceptable levels in terms of the best score proximity and they did not show any toxicity indication in 26 different QSAR toxicity models. Moreover, they do not produce high number of reactive metabolites. All these calculations were performed not only to determine if the studied molecules had the potential to be a MAO-B inhibitor but also to find out whether they carried MAO-B selectivity versus MAO-A. As a result, one of the identified hit (ligand 1, Otava ID: 3463218) among ∼256750 molecules may have a better selective MAO-B inhibitor than selegiline. Further in vitro studies can be carried out to verify therapeutic effects of identified hits from a large-scale small molecule library as novel selective MAO-B inhibitors. 4. METHODS 4.1. Protein Preparation In this study, the crystal structure of MAO-B was obtained from the Protein Data Bank (PDB). For the human MAO-B, PDB code of 1S3B with 1.65 Å resolution has been used.37 The full length of the chains A and B contains 520 residues and unfortunately in this crystal structure there are several missing C-terminal residues actually located in the outer membrane of mitochondria. These missing residues were modeled and known lipid-bilayer merged helix is prepared artificially in Maestro molecular modeling suite.38 Protein Preparation module of Maestro was used for the preparation of the enzyme. Hydrogens were added, bond orders were assigned and protonation states of residues was determined at the physiological pH. This prepared structure were minimized by using OPLS 2005 force field.39 In this stage, the inhibitor was left in the binding site of the enzyme. The obtained structure has been embedded into the 1palmitoyl-2-oleoyl-sn-glycero-3-phosphoethanolamine (POPE) lipid bilayer and subjected to 5 ns MD simulations run for relaxing the whole system by utilizing Desmond40 before docking protocols. A representative structure was determined from obtained trajectories and it is used for docking studies. Water molecules around the co-crystal ligand within a distance of 5 Å have been kept at the binding pocket.



4.2. Ligand Preparation LigPrep module38 with OPLS 2005 force field was used for the preparation of studied molecules retrieved from Otava Chemical Database (http://www.otavachemicals.com/). Protonation states of the compounds were determined using Epik41 at the physiological pH. 4.3. Molecular Docking Glide42-44 module of Maestro molecular modeling package was used to generate docking grid map for the following docking protocols; HTVS, standard precision (SP), extra precision (XP), induced fit docking (IFD)45 and quantum polarized ligand docking (QPLD).46 In docking studies, firstly Glide/HTVS calculations have been performed to detect the possible potent compounds. According to the Glide/HTVS scores, 10% of the top-scored compounds (25675 molecules) have been selected for Glide/SP calculations. In Glide/SP, up to 5 docking poses were generated for each ligand and the compounds determined to be a favorable potent inhibitor according to the docking scores (6247 molecules) were submitted to Glide/XP protocol. Similarly, a series of ligands have been obtained from XP and these 312 molecules were subjected to both IFD and QPLD calculations to find out the most potent inhibitor for MAO-B (Scheme 1). Commercially available MAO-B inhibitor selegiline and an inactive ligand from literature which does not exhibit any MAO-B inhibition47,48 were used for positive and negative control purposes respectively by using both IFD and QPLD docking protocols. The experimental IC50 values of selegiline and the inactive ligand for MAO-A and MAO-B are shown in Table 1. Selected hits were also docked to MAO-A enzyme to investigate their MAO-B selectivity. The crystal structure of MAO-A was used from PDB, (PDB code of 2Z5X human MAO-A with resolution of 2.2 Å).49 This structure has been crystallized as monomer, however, we used homodimer structure for MAO-A. Maestro package was used to generate homodimer from the obtained monomer to overcome this situation. Then, the formed MAO-A homodimer structure was prepared with Protein Preparation module of Maestro with the same protocols as previously explained for MAO-B in the protein preparation section. Then, the studied ligands and selegiline were docked to the MAO-A enzyme and selectivity properties of these ligands and selegiline were investigated. In Glide docking, ligands were used as fully flexible and a limited flexibility was gained using rotamers of hydroxyl groups of Tyr, Thr and Ser residues. However, in IFD docking, residues were refined within 5.0 Å of ligand poses.


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4.4. MetaCore/MetaDrug Applications The hit molecules obtained from XP docking protocol were evaluated for the investigation of their therapeutic activity, pharmacokinetic and toxicity properties by using MetaCore/MetaDrug50 comprehensive systems biology analysis suite of Clarivate Analytics with the help of available ADME, disease and toxicity QSAR models. MetaCore is based on a high-quality, manuallycurated database of molecular interactions, molecular pathways, gene-disease associations, chemical metabolism and toxicity information. The hit molecules obtained from virtual screening that can be the candidates of MAO-B inhibitors were assessed for their therapeutic effects including PD, AD, depression and Schizophrenia. Furthermore, 26 different toxic effects such as mutagenicity,

anemia,

carcinogenicity,

cardiotoxicity,

cytotoxicity,

epididymis

toxicity,

genotoxicity, hepatotoxicity, kidney necrosis, kidney growth, liver choleastasis, etc. were predicted by 26 different toxicity QSAR models available under MetaCore. Besides, the possible metabolites of the studied molecules were searched. Because of the importance of diverse data set in the training set (i.e., molecules that have particular property (actives), molecules with moderate activity, and molecules that do not show particular property (negatives)), the correct negative chemicals also have to be chosen for the training set. The similarity of each molecule that has negative values was calculated with respect to all positives and the highest value of similarity is calculated. The low scores show low similarity between each negative to all positive drugs. Each similarity range stops at the value of similarity of last drug that makes the amount of negative compounds equal to positives. Prediction of metabolite generation properties of screened hits is also checked by cytochromes cytP450 (i.e., cytP450 2B6, cytP450 1A2, cytP450 2D6, cytP450 3A4). This feature predicts the potential metabolism by cytochromes. The predicted QSAR values greater than 0.5 indicate that the molecules are metabolized by cythocrome. The quality of the derived models in MetaCore/MetaDrug is evaluated with specificity, sensitivity, accuracy, and Matthews Correlation Coefficient (MCC). After MetaCore/MetaDrug applications, a few molecules were selected and MD simulations have been run for these protein-ligand complexes together with the positive and negative-control bound and apo form of the enzymes. 4.5. Molecular Dynamics (MD) Simulations Based on IFD and QPLD docking scores and MetaCore/MetaDrug evaluations, two candidate ligands were chosen which have high therapeutic activity scores from binary QSAR models for the diseases mentioned above as well as high IFD/QPLD docking scores. In this study, ligand selection criteria for further MD simulations was not only for the compounds with high docking



scores. Following points were also considered: (i) docking poses including the interactions with crucial amino acids for enzyme activity; (ii) the results from pharmacokinetic calculations. Selected two hit molecules as well as selegiline and an inactive ligand obtained from literature47 have been used in MD simulations. MAO-B complexes were embedded into the POPE membrane bilayer between 500-520 aminoacid residues of both chains. The buffer size of the system box was set to 8-8-40 Å and the box shape was specified as orthorhombic. Explicit water molecules (TIP3P)51 were used in the preparation of the system and also 0.15 M NaCl ion concentrations were added to it for the neutralization of the system. The number of atoms of the generated systems varies between 220000 and 230000. In all MD simulations, NPT ensemble at 310 K with Nose-Hoover temperature coupling52 and at constant pressure of 1.01 bar via Martyna-Tobias-Klein pressure coupling53 were provided. All the systems were prepared and put through the MD simulations by using DESMOND program employing the OPLS2005 force field and RESPA integrator.54 There were no constraints on the generated systems and the initial velocity values are used as default. Each system was subjected to 50 ns of MD simulation runs and simulations were repeated by 2-times starting with different seeding numbers. Although before the simulations systems are relaxed with minimization and 5-ns equilibration and it is observed that all studied systems reach plateau after 10-ns production MD run, in order to investigate structural properties after 50-ns, 100-ns MD simulations were also performed for all systems. Trajectory analysis were carried out with average values of saved 5000-trajectory frames from each run throughout MD simulations. For the determination of the selectivity properties of the studied molecules55, MD simulations of MAO-A/ligand complexes were also carried out with the same parameters mentioned above. MAO-A/ligand complexes were embedded into the POPE bilayer membrane between 498-527 amino acid residues. An example of the generated systems is shown in Figure 1. 4.6. Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) Calculations Schrodinger's Prime module was used to execute molecular mechanics generalized Born surface area (MM/GBSA) binding free energies calculations of selected ligand-protein complexes. 100 trajectory frames were chosen from the last half of the MD simulations (i.e., 4 trajectory frames/1ns). For the prediction of free binding energies of complexes OPLS2005 forcefield and VSGB 2.0 solvation model were applied. 3 Å from ligand was used as flexible residues and “minimize” option was selected for the sampling method.


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Supporting Information: Toxicity predictions of Ligands 1 and 2 (Table S1); statistical results of used QSAR models (Table S2); Cα-RMSD graphs of protomers A and B (Figure S1); Active site RMSDs of studied systems (Figure S2); Cα-RMSD graphs of studied systems for extended simulations (Figure S3); interaction histograms of selegiline and inactive compound (Figures S4 and S5); protein-selegiline and protein-inactive compound contact graphs (Figures S6 and S7); MM/GBSA energies of studied ligands at the binding pocket of MAO-B throughout last half of the 100-ns MD simulations (Figure S8); change of Solvent Accessible Surface Area (SASA) of studied molecules throughout the MD simulations (Figure S9); Cα-RMSD Graphs of MAOA/ligand and MAO-B/ligand systems (Figure S10); Comparison of SASA graphs for all ligands at the binding cavities of MAO-A and MAO-B (Figure S11) AUTHOR INFORMATION ORCID ID Serdar Durdagi: 0000-0002-0426-0905 Author Contributions Y.S.I., S.D. and M.Y. participated in research design; Y.S.I., B.A., S.D., and M.Y conducted experiments; Y.S.I., B.A., S.D., and M.Y participated in data analysis and contributed to the writing of the manuscript. Notes The authors declare no competing financial interest. Funding No funders to report ACKNOWLEDGMENTS The numerical calculations reported in this paper were partially performed at TUBITAK ULAKBIM, High Performance and GridComputing Center (TRUBA resources). REFERENCES 1.

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Schemes

Scheme 1. Virtual Screening Scheme



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Tables

Compound

Selegiline Inactive Ligand

IC50 (µM) ± SD MAO-A MAO-B 1.7 ± 0.44 6.8 ± 1.4 × 10-3 157.1 ± 4.4 6536.8 ± 102.5

Table 1. IC50 values for inhibition of MAO-A and MAO-B by selegiline and inactive ligand.47,48

Ligand

Otava ID

Docking Score (kcal/mol)

Therapeutic Activity/TP* 0.66 (32.37) 0.72 (42.53) 0.35 (38.18) 0.28 (44.21) 0.53 (45.42)

Alzheimer

Ligand 2

7720500779

-12.4 QPLD (7th topdocking pose)

Depression Parkinson Schizophrenia Alzheimer

Ligand 1

3463218

-13.0 IFD (11th-top docking pose)

0.52 (46.39)

Parkinson

ADMET BBB

0.28

G-log P

2.28

Prot-bind,%

67.84

Prot-bind, log t

-0.19

BBB

0.12

G-log P

3.18

Prot-bind, %

82.32

Prot-bind, log t

-0.03

Toxicity

Reactive Metabolites

-

1

-

1

Cut-off Value for Therapeutic Activities: 0.5 Value ≥ 0.5 → Green Colour Value ≤ 0.5 → Red Colour Cut-off Value for BBB: - 0.3 Value ≥ - 0.3 → Green Colour Value ≤ - 0.3 → Red Colour Cut-off Value for G-log P: - 0.4 – 5.6 - 0.4 ≤ Value ≤ 5.6 → Green Colour Value out of range → Red Colour Cut-off Value for Prot-bind,%: 50 Value ≤ 50 → Green Colour Value ≥ 50 → Red Colour Cut-off Value for Prot-bind, log t should be negative values → Green Colour Positive Values → Red Colour

Table 2. Docking Scores and MetaCore/MetaDrug results of ligands 1 and 2. *TP at the table (i.e., values shown in parenthesis) shows Tanimato Prioritization values. A maximal Tanimato coefficient calculated for all molecules in a training set of a QSAR model indicates the similarity of the analyzed structure to the most similar compound in the training set.


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Therapeutic Activity/TP*

Ligand

Alzheimer

Selegiline

Depression

ADMET

0.31 (73.91) 0.75 (73.91)

Parkinson

0.84 (57.58) Schizophrenia 0.86 (100.00) Alzheimer Depression Inactive

0.59 (54.42) 0.93 (51.79)

Reactive Metabolites

Toxicity

BBB

0.52

Cardiotoxicity

G-log P

2.74

Liver Cholestasis

0.76 (69.49) 0.66 (57.14)

Protbind,% Protbind, log t BBB

69.91

Liver Necrosis

0.58 (49.00)

Cardiotoxicity

0.72 (39.81)

G-log P

1.64 Genotoxicity

0.69 (57.78) 0.78 (44.09)

-

0.22 0.52

Parkinson

0.43 (57.78)

Protbind, %

60.27

Liver Necrosis

Schizophrenia

0.40 (54.05)

Protbind, log t

0.38

Neurotoxicity

1

0.75 (39.81)

Cut-off Value for Toxicity: 0.5 Value ≤ 0.5 → Green Colour Value ≥ 0.5 → Red Colour

Table 3. MetaCore/MetaDrug results of reference compounds. *TP at the table (i.e., values shown in parenthesis) shows Tanimato Prioritization values. A maximal Tanimato coefficient calculated for all molecules in a training set of a QSAR model indicates the similarity of the analyzed structure to the most similar compound in the training set.

Method IFD QPLD

Isoform

Ligand 1

Ligand 2

Selegiline

Inactive





MAO-A MAO-B MAO-A MAO-B

-8.4 -13.0 -6.7 -10.3

-9.5 -10.1 -6.6 -12.4

-7.3 -7.3 -6.7 -6.8

- 4.4 -3.9

Table 4. IFD and QPLD Docking Scores of the Studied Molecules on MAO-A and B.



Regions Z1 Z2 Z3 Z4 Z5 Z6 Z7

Residue Numbers Phe168-Leu171-Cys172 Ile198-Ile199-Ser200-Thr201-Gly205-Gln206 Ile316 Tyr326-Leu328 Phe343 Tyr398 Tyr435

Table 5. In order to investigate fluctuations on protein, seven zoom regions are considered in RMSF analysis. Table shows corresponding amino acid residue numbers for these seven zoom regions.


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Figures

Figure 1. MAO-B/Selegiline Complex System

27

ACS Paragon Plus Environment

ACS Chemical Neuroscience 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

Figure 2. (top) 3D ligand interaction diagram of ligand 1 at the binding pocket; (bottom) 2D interaction diagrams of ligand 1 (IFD Pose); ligand 2 (QPLD Pose); selegiline (IFD Pose); and inactive ligand (IFD Pose), respectively. 28


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Figure 3. Cα-RMSD Graph of protein. Average values of RMSD values of two replicate MD simulations (in Å): ligand 1: 2.48±0.12, ligand 2: 2.43±0.36; selegiline: 2.48±0.22; inactive ligand: 2.35±0.13; apo form: 2.48±0.29.



Figure 4. RMSD Lig-fit-Prot Graphs. Average RMSD values (in Å): ligand 1: 2.60±0.25, ligand 2: 2.99±0.46; selegiline: 4.15±0.39; inactive ligand: 2.59±0.23. Shaded regions show error bars.

Figure 5. Interaction histogram diagram for ligand 1.


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Figure 6. Interaction histogram diagram for ligand 2.

Figure 7. Protein/ligand 1 contact graph throughout MD simulations.



Figure 8. Protein/ligand 2 contact graph throughout MD simulations.


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Figure 9. In order to investigate fluctuations on protein, seven zoom regions are considered in RMSF analysis. Figure shows these seven zoom regions (Z-I to Z-VII). Average values of two replica simulations were used.

33



Figure 10. Torsional analysis of ligand 1 during MD simulations.


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Figure 11. MM/GBSA energy values of studied ligands at the binding pocket of MAO-B throughout last half of the MD simulations. Figure includes shaded error bars for each simulation. Average values (in kcal/mol): ligand 1: -90.11±4.65; ligand 2: -62.63±4.18 kcal/mol; selegiline: -52.45±4.21; and inactive ligand: -37.13±3.28.



Table of Contents Graphic


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Novel MAO-B Hit Inhibitors Using Multidimensional Molecular Modeling Approaches and Application of Binary QSAR Models for Prediction of their Therapeutic Activity and Toxic Effects Yusuf Serhat Is, Serdar Durdagi*, Busecan Aksoydan, Mine Yurtsever*


Novel MAO-B Hit Inhibitors Using Multidimensional Molecular

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