Understanding the Molecular Determinant of Reversible Human

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Understanding the Molecular Determinant of Reversible Human Monoamine Oxidase B Inhibitors Containing 2H-chromen-2-One Core: Structure-Based and Ligand-Based Derived 3-D QSAR Predictive Models Milan Mladenovic, Alexandros Patsilinakos, Adele Pirolli, Manuela Sabatino, and Rino Ragno J. Chem. Inf. Model., Just Accepted Manuscript • DOI: 10.1021/acs.jcim.6b00608 • Publication Date (Web): 14 Mar 2017 Downloaded from http://pubs.acs.org on March 16, 2017

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Journal of Chemical Information and Modeling

Understanding the Molecular Determinant of Reversible Human Monoamine Oxidase B Inhibitors Containing 2H-chromen-2-One Core: Structure-Based and Ligand-Based Derived 3-D QSAR Predictive Models Milan Mladenović,†* Alexandros Patsilinakos,‡,§ Adele Pirolli,‡ Manuela Sabatino,‡ and Rino Ragno‡,§* †

Kragujevac Center for Computational Biochemistry, Faculty of Science, University of Kragujevac, Radoja Domanovića 12, 34000 Kragujevac, P.O. Box 60, Serbia

‡

Rome Center for Molecular Design, Department of Drug Chemistry and Technologies, Faculty of Pharmacy and Medicine, Rome Sapienza University, P.le A. Moro 5, 00185, Rome Italy §

Alchemical Dynamics srl 00125 Rome, Italy

To whom correspondence should be addressed: E-mail: [email protected]; Phone: +381 34336223; Fax: +381 34335040 E-mail: [email protected]; Phone: +39 49913927; Fax: +39 4991 3627

1 ACS Paragon Plus Environment


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ABSTRACT: Monoamine oxidase B (MAO B) catalyzes the oxidative deamination of aryalkylamines neurotransmitters with concomitant reduction of oxygen to hydrogen peroxide. Consequently, the enzyme’s malfunction can induce oxidative damage to mitochondrial DNA and mediates development of Parkinson’s disease. Thus, MAO B emerges as a promising target for developing pharmaceuticals potentially useful to treat this vicious neurodegenerative condition. Aiming to contribute to the development of drugs with the reversible mechanism of MAO B inhibition only, herein, an extended in silico-in vitro procedure for the selection of novel MAO B inhibitors is demonstrated, including: (1) definition of optimized and validated structure-based (SB) 3-D QSAR models derived from available co-crystallized inhibitor-MAO B complexes; (2) elaboration of structure-activity relationships (SAR) features for either irreversible or reversible MAO B inhibitors to characterize and improve coumarin-based inhibitor activity (Protein Data Bank ID: 2V61) as the most potent reversible lead compound; (3) definition of structure-based (SB) and ligand-based (LB) alignment rules assessments by which virtually any untested potential MAO B inhibitor might be evaluated; (4) predictive ability validation of the best 3-D QSAR model through SB/LB modeling of four coumarin-based external test sets (267 compounds); (5) design and SB/LB alignment of novel coumarin-based scaffolds experimentally validated through synthesis and biological evaluation in vitro. Due to the wide range of molecular diversity within the 3-D QSARs training set and derived features, the selected N probe-derived 3-D QSAR model proves to be a valuable tool for virtual screening (VS) of novel MAO B inhibitors and a platform for design, synthesis and evaluation of novel active structures. Accordingly, six highly active and selective MAO B inhibitors (picomolar to low nanomolar range of activity) were disclosed as a result of rational SB/LB 3-D QSAR design;


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therefore, D123 (IC50 = 0.83 nM, Ki =0.25 nM) and D124 (IC50 = 0.97 nM, Ki =0.29 nM) are potential lead candidates as anti-Parkinson’s drugs.



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INTRODUCTION The mitochondrial outer membrane-anchored monoamine oxidases (MAOs) represent flavoenzymes that catalyze oxidative deamination of arylalkylamines neurotransmitters with concomitant reduction of oxygen to hydrogen peroxide.1 Gene coded biosynthesis in mammalian cells results in the existence of two separate isoforms sharing ~70% of sequence identity,2 MAO A, predominantly found in brain’s catecholaminergic neurons,3 and MAO B, the most abundant in serotonergic and histaminergic neurons and glial cells.4 MAO A oxidizes serotonin and exhibits overlapping specificity with MAO B towards adrenalin, noradrenalin and dopamine, whereas MAO B metabolizes arylalkylamines like dopamine, phenethylamine, and benzylamine.5 With aging, MAO A activity changes only slightly and its deficiency or low level of expression results in aggressive behavior phenotype.6 On the other hand, due to the proliferation of glial cells,5 loss of substantia nigra dopaminergic neurons or xenobiotic metabolites induced destruction of nigrostriatal neurons,7,8 the MAO B expression in neuronal tissues increases about 4-fold and consequently causes oxidative damage to mitochondrial DNA. Particular physiological alternation is thought to play a role in the etiology of neurodegenerative diseases like Parkinson’s2. In addition, MAO B increased levels have also been demonstrated in plaque-associated astrocytes in the brains of Alzheimer’s disease patients.9 Owing to the pharmacological relevance, the malfunction of MAO A and MAO B distinguishes these enzymes as interesting targets for drug design. Thus, hitherto developed MAO A inhibitors are clinically used as antidepressants and/or anxiolytics, whereas MAO B inhibitors are therapeutics in the treatment of Parkinson’s disease.1 Drugs that control MAO B metabolism are not yet major therapeutics for treating Parkinson’s disease, as the first-line therapy is the replacement of dopamine with L-DOPA.10 Hence, due to the rapid metabolic


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transformation of L-DOPA, the drug is typically combined with benserazide and carbidopa, aromatic amino acid decarboxylase (AADC) inhibitors, and/or with reversible and selective catechol O-methyltransferase (COMT) inhibitors (entacapone and tolcapone). Even the irreversible MAO B inhibitors (MAOIs), such as: l-deprenyl, rasagiline, mofegiline,11 and hydrazines or non-hydrazine inhibitors, such as: tranylcypromine and pargyline,12 represent active pharmaceutical ingredients for combination drug products used in clinical practice. Selegiline (l-deprenil) in low doses exerts a significant neuroprotective effect by blocking neuronal apoptotic cell death,13 while, paradoxically, at high doses both l-deprenyl and L-DOPA аre reported to induce apoptosis.5 Liver toxicity, hypertensive crises, hemorrhage and death14 are known problems arising after the application of hydrazide and non-hydrazide inhibitors. Unlike the nonselective irreversibles, selective reversible MAO B inhibitors have no side effects known as “cheese” reaction, induced by the intake of foods rich in arylalkylamines (tyramine and other sympathomimetic amines physiologically metabolized by MAO A, commonly found in cheese, chocolate, and wine).12 Due to the use of MAOIs and tyramine-rich diet, tyramine can displace stored monoamines, dopamine, norepinephrine, and epinephrine from pre-synaptic vesicles. Hence, patients with compromised monoamine metabolism may be affected by hypertensive crises, which may sometimes lead to fatal outcomes. Therefore, various observations indicate the need for new, possibly reversible and safer MAO B inhibitors in the therapy of Parkinson’s disease. Monoamine oxidase B is an enzyme with two-domain molecular architecture (Figure 1).1 Each identical monomer consists of 520 amino acids. The tertiary structure is made of Nterminal solvent-exposed globule (residues 1-488), anchored to the membrane through a Cterminal helix (residues 489-520).1 The active site is protected from the solvent by a loop (Figure



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1, green ribbons, residues 99-112)1 partly embedded in the membrane to serve as a gate towards the active site for MAO B substrate or inhibitor. The active site is located in the N-terminal domain and is composed of two separate but jointly operating sub-domains called the entrance cavity (Figure 1, blue surface, ~290 Å3 in size) and the hydrophobic substrate cavity (Figure 1, red surface, ~420 Å3 in size), respectively.15 Amino acids Phe168, Leu171, Ile199, and Tyr326 form the internal gate which separates the entrance from the substrate cavity.1,15,16 Binding of biogenic amines or small inhibitors in MAO B induces the Ile199 side chain rotation into a closed conformation, leading to bipartite active site formation. Bulky inhibitors push Ile199 to preserve the open conformation and merge two cavities into one of ~700 Å3 in volume, replenishing the newly established space. The Ile199 alteration is a determinant for selective MAO B inhibitor recognition1,15,16 especially if the molecule is capable of binding to the whole active site. The side chain of Tyr326 exhibits modest conformational changes upon inhibitor binding.1,15,16 At the end of the substrate cavity, there is the recognition aromatic cage delimited by Tyr398 and Tyr435, approximately perpendicular to the FAD flavin ring plane. The FAD represents the bottom of the active site, it is located within cofactor binding domain (residues 461-488) and it is covalently bound to Cys397 via 8α-(S-cysteinyl)-riboflavin linkage.1,15,16 Upon reaching the recognition cage, amine substrate -CH2-NH2 group undergoes to a FAD-mediated hydrolysis.1,15,16 The reduction of cellular O2 to H2O2 is the cognate semi-reaction of FAD oxidation.


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Figure 1. Crystal structure of MAO B in resolution of 1.7 Å (PDB entry code 2V61). Extracellular domain ribbons are depicted in light gray, while the magenta ribbons represent the transmembrane domain. The active site gate loop (residues 99-112) is shown in green, blue transparent surface outlines the entrance cavity while the red transparent one delineates the substrate cavity. Co-crystallized FAD and the coumarin inhibitor are depicted in purple and black, respectively. Although l-deprenyl, pargyline, rasagiline and other similar drugs are well established MAO B clinical inhibitors, their mechanism of action by means of covalent FAD modification and enzyme permanent deactivation17 opens the opportunity for the development of novel reversible therapeutics in MAO B malfunction therapy. Yet, until recently, their rational design was strongly hampered by a lack of reliable 3-D structural information on the ligand interactions with the enzyme active site. To overcome this deficiency, cheminformatics as a tool was initially used to derive some classical QSAR models using Artificial Neural Networks (ANNs),18 Principal Component Analysis/Linear Discriminate Analysis (PCA/LDA)19 and LDA20 statistical 7 ACS Paragon Plus Environment


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techniques to generate new rasagiline bioisosteres and predict the activity of some coumarin derivatives.21 The “dimensional” progress was made when the ligand-based (LB) approach was applied in constructing of several 2-D QSAR models using pyridazines and pyrimidine derivatives,22 caffeine analogues,23 and phenyl alkylamines24 as training set compounds. Examples of LB 3-D QSAR CoMFA based models were derived from pirlindoles,25 5Hindeno(1,2-c)pyridazines,26 and indolylmethylamines,27 without reflections on compounds interaction with MAO B active site. CoMFA/GOLPE models, derived from tetrazoles, oxadiazolones, oxadiazinones, N-acylhydrazones, and coumarins,28 along with the CoMFA and CoMFA/GOLPE based studies29,30,31 on 3-,4-,7-polysubstituted coumarins, revealed that coumarin core saturated at positions 3, 4, and 7 with steric and high electron density substituents, or with the 7-benzyloxy scaffold, may represent a valuable basis for design of powerful MAO B inhibitors. The first pharmacophore-based 3-D QSARs were developed for furanochalcones,32 Npropargylaminoindan carbamates and N-propargylphenethylamines,33 while the first receptorbased 3-D QSARs were generated after the molecular docking of some hydrazothiazole derivatives.34 In order to contribute to the design of innovative reversible inhibitors only, the present paper summarizes the generation of structure-based (SB) 3-D QSAR models, with the aim to describe the molecular determinants for enzyme inhibition and define molecular features required for non-covalent MAO B inhibitors to suppress the origination of Parkinson's disease. Herein, 3-D QSAR models were built with available co-crystallized MAO B structures in complex with either covalent or reversible inhibitors.35-44 As the reversible co-crystallized coumarin-based compound was among the most potent inhibitors known,41 the 3-D QSAR predictive ability and robustness were validated by applying four external test sets compiled


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from literature available on coumarin-based MAO B inhibitors. Finally, information received from 3-D QSAR led to design, synthesis, and evaluation of novel selective and highly active coumarin compounds as potent MAO B inhibitors.

GENERAL PROCEDURE Briefly (Figure 2), the MAO B training set (23 compounds in total, Tables 1, 2, see Training Set Compilation and Preparation section, and Supporting Information Table S1), had been composed of both irreversible and reversible co-crystallized MAO B inhibitors

35-44

available from MAO B-inhibitor complexes deposited at Protein Data Bank (PDB). The MAO B training set was submitted to recently developed alternative procedure to build 3-D QSAR models, named 3-D QSAutogrid/R,45 to generate partial least squares (PLS) based 3-D QSAR models using eight probes, namely A, C, OA, HD, NA, N, e, and d (Supporting Information Table S2), and to describe the activity of MAO B inhibitors. Prior to extracting irreversible inhibitors, they were converted to the respective non-reacted (non-covalent or reversible) form4649

and submitted to a minimization procedure (Supporting Information Training Set

Compilation and Preparation section). The criterion for the successful minimization was no detected steric clash between the re-constituted inhibitors and recognition/FAD area. Afterwards, former irreversible inhibitors were merged with the reversible ones, likewise minimized and extracted, to create the final MAO B training set. Models predictive abilities were evaluated utilizing the test set (Table 3) compiled of coumarin-based compounds only,19,50-65 inasmuch as the most active reversible training set compound was coumarin derivative (PDB ID: 2V61). Initial generated 3-D QSAR models were firstly optimized through the systematic grid spacing variation, using the standard pretreatment protocol (Supporting Information, Tables S3S10). A second optimization stage was achieved by means of variable pretreatment optimization 9 ACS Paragon Plus Environment


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(VPO) procedure (Table 4, Supporting Information Tables S11-S18). Final 3-D QSAR models (Table 5, Supporting Information Tables S19-S26, Figures 3 and 4, Supporting Information Figures S1-S26) were obtained by Monte Carlo Simulated Annealing (SA) feature selection procedure. The 3-D QSAR models’ internal validation (robustness) was assessed through classical cross-validation (CV) techniques (leave-one-out, LOO; 5-random-groups-out, K-5Fold, K5FCV) while the lack of chance correlation was evaluated by means of CV and YScrambling combination (Supporting Information Tables S27-S42). The chemical information received from the best 3-D QSAR models were summarized into a SAR/pharmacophore model (Figure 5), later on used to design novel coumarin scaffolds as potential MAO B inhibitors. The models’ predictive ability was evaluated with external test sets composed of published coumarin compounds (see Test Set Compilation and Preparation section, a total of 267 compounds) with known anti-MAO B activities and unknown binding modes19,50-65

(Table 3, Supporting

Information Tables S43-S50). Test set molecules alignment was achieved through different alignment rules by either SB or LB approaches. The alignment procedures were assessed for their ability to reproduce the known experimental binding modes (docking or alignment accuracies) of the sole reversible inhibitors and were performed utilizing AutoDock Vina (hereinafter: Vina),66 DOCK,67 PLANTS (with all its available scoring functions: chemplp, plp, and plp95),68 and Surflex-Dock69 programs for the SB (Tables 6 and 7, Figure 6, Supporting Information Figure S27), and Balloon/ShaEP70,71 programs couple for the LB alignment assessment (Table 8, Figure 7, Supporting Information Figures S28-S30). The protocol72 consisted of experimental (EC) or modeled (randomized, RC) ligand conformation re-docking (RD)/re-alignment (RA) or cross-docking (CD)/cross-alignment (CA) assessments (see Alignment assessment section). The best 3-D QSAR models were utilized to predict the activity


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of either SB or LB aligned test set compounds (Tables 9 and 10, Supporting Information Tables S43-S50, Figure 8, Supporting Information Figures S31-S33). Finally, the SAR/pharmacophore model driven design of 128 novel coumarin scaffolds as potential MAO B inhibitors was performed. The designed compounds (Table 11, Figure 9, Supporting Information Figures S34S35) were SB/LB aligned and MAO B inhibitory predicted by means of the above described 3-D QSAR models, while some derivatives were synthesized (Scheme 1, Supporting Information Figures S36-S57), biologically evaluated (Table 12, Figure 10), and profiled for pharmacokinetics profile with experimental evaluation of log P73 and PAMPA Permeation Assays (Supporting Information Table S51).74



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Figure 2. Procedure workflow. 12 ACS Paragon Plus Environment

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COMPUTATIONAL METHODS Training Set Compilation and Preparation. During the last fifteen years, 37 crystal structures of MAO B in complex with miscellaneous inhibitors became available from Protein Data Bank (Supporting Information Table S2), creating the favorable environment for SB and LB design of novel drug-like compounds. In this study, the available inhibitors crystallography data have been exploited in order to build SB 3-D QSAR models. A training set was composed of 23 MAO-B complexes comprising either covalent (12 compounds, Table 1) or reversible inhibitors (11 compounds, Table 2). Detailed analysis of the training set preparation is reported as Supporting Information (section Training Set Compilation and Preparation).



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Table 1. PDB Codes, Ligand Names and Ligand Structures of 12 Co-Crystallized Irreversible Monoamine Oxidase B Inhibitors. Ki (µM)

pKi

Ref.

pargyline

0.7

6.15

12, 37

1OJC

NAa

0.084

7.06

39

1S2Q

(R)-rasagiline

0.7

6.15

36

1S2Y

(S)-rasagiline

127

3.90

36

1S3E

NAa

17

4.77

36

2BYB

l-deprenyl

0.0168

7.77

13, 35

2C65

NAa

130

3.88

35

2C66

NAa

31

4.51

36

2VRL

NAa

26

4.58

38

2VRM

phenelzyne

15

5.88

38

2VZ2

mofegiline

0.028

7.56

11, 40

2XFU

tranylcypromine

16

4.79

39

a

PDB Code

Generic name

1GOS

Ligand structure

NA, not available


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Table 2. PDB Codes, Ligand Names and Ligand Structures of 11 Co-Crystallized Reversible Monoamine Oxidase B Inhibitors.

a

PDB Code

Generic name

1OJ9

Ligand structure

Ki (µM)

pKi

Ref.

1,4-dyphenyl-2butene

35

4.46

39

1OJA

isatin

3

5.52

40

2BK3

(E,E)-farnesol

2.3

5.64

44

2C67

NAa

17

4.77

36

2V5Z

safinamide

0.45

6.39

41

2V60

NAa

0.4

6.39

41

2V61

NAa

0.1

7

41

2XFN

NAa

8.3

5.08

39

3PO7

zonisamide

3.1

5.51

42

4A79

pioglitazone

0.5

6.30

43

4A7A

rosiglitazone

4.2

4.38

43

NA, not available

Test Sets Compilation and Preparation. Coumarin-based MAO B inhibitors, with known activities and unknown binding modes, which are according to their structure divided into 15 ACS Paragon Plus Environment


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four sub-groups (Table 3, Supporting Information, Tables S43-S50), were collected from literature.19,50-65 The first sub-group (7-BOC) consists of 7-(benzyloxy)-2H-chromene-2-one derivatives; the second one (3-AC) is formed by 3-acetyl-2H-chromene-2-one condensates; the third one (3-PC) is composed of various 3-phenylcoumarins; in the fourth group (COM) the coumarin core is substituted with various functional groups. All of the tests set structures were modeled by applying the Chemaxon's msketch module75 by means of molecular mechanics’ optimization upon which the hydrogen atoms, appropriate for pH 7.4, were assigned.

Table 3. The Overall Structure and Range of Activity of Compounds That Formed Four Test Sets Used for External Validation of Generated Structure-Based 3-D QSAR Models. Name

Base scaffold

Number of compounds 115

Activity range pIC50 4.19 – 8.59

50-52, 62-64

67

4.19 – 8.65

51-53

3-PC

48

4.16 – 9.51

54-61

COM

37

3.06 – 8.92

19,64,65

7-BOC

O

3-AC O

Refs.

O

Structure-Based 3-D QSAR Building. All calculations were done on a 6 blades (8 IntelXeon E5520 2.27 GHz CPU and 24 GB DDR3 RAM each) cluster (48 CPU total) running Debian GNU/Linux “Wheezy” 7.5 64 bit operating system. Grid optimization and Molecular Interaction Fields (MIF) Calculation. The training set was submitted to the 3-D QSAR procedure in order to provide answers to the question: what are the structural requirements needed for the MAO B inhibitor to successfully take control over the 16 ACS Paragon Plus Environment

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enzyme’s activity? As implemented in the 3-D QSAutogrid/R procedure,45 MIFs were generated using the AutoGrid software (AutoDock Suite,76 based on the AMBER united-atom Force Field), considering 8 different probes (Supporting Information Table S2). The 3-D QSAR models were built for each probe, using maximum 5 principal components. Initial models, derived using standard settings (grid spacing = 1 Å, energy cutoff of ± 5 Kcal/mol, zeroing = 0.01 Kcal/mol and minimum standard deviation = 0.05), were then optimized for top scoring grid spacing by systematically varying grid step from 0.5 to 2.5 Å (Supporting Information Tables S3-S10) while saving corresponding standard (r2) and cross-validated (q2) correlations coefficients. The xyz coordinates (in Ångströms) of the cuboid grid box used for the computation were Xmin/Xmax = 2.600/29.400, Ymin/Ymax = 116.000/142.800, Zmin/Zmax = 8.900/35.700 to embrace all the minimized inhibitors spanning 10 Å in all three dimensions. Statistical Analysis. Upon finding appropriate grids for each probe, value redundancy elimination and systematic data pretreatment were achieved through the VPO procedure, using Leave-One-Out (LOO) and Leave-Some-Out (LSO; i.e. k-Fold, K5FCV, 5-random groups and 100 iterations) cross-validation, while monitoring q2, Standard Deviation of Calculation (SDEC) and Standard Deviation of Prediction (SDEP) values (Table 4, Supporting Information Tables S11-S18). Full pretreatment of the data derived from the MIFs calculations was performed by exploring the combinations of cutoff values from -5 to 5 Kcal/mol with intervals between the cutoffs equal to 1, zeroing values from -0.005 to 0.05 Kcal/mol with interval of 0.005, and standard deviation values form -0.01 to 0.1 with the interval of 0.01. The VPO derived models were finally optimized by Simulated Annealing (SA) algorithm variable selection using following parameters: initial temperature 100ºC, final temperature 1x10-3ºC, and cooling factor of 0.999 (Table 5, Supporting Information Tables S19-S26). Models’ chance correlations were



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evaluated via the Y-Scrambling approach, using 100 iterations. The obtained statistical results, confirmed both the internal predictive capabilities and robustness of the models (Supporting Information Tables S27-S42) and their eligibility to be used as 3-D QSAR models. 3-D QSAR Models’ interpretation. Upon obtaining the numerical definition of the model, the final stage of any 3-D QSAR model building is the graphical representation and interpretation of results. The interpretation of 3-D QSAutogrid/R procedureʼ derived models relies on the alignment of PLS-coefficients, which represent 3-D QSAR model globally, and the Actual Activity Contribution (AAC) maps, which describe the biopotential of each training set compound individually.77 In a nutshell, overlapping of the positive probe-derived (P) PLScoefficients (PPLS-coefficients, red polyhedra) with the positive inhibitor-derived (I) AACs (IAACs green contours) derived from the same probe, as well as overlapping of the negative PPLS-coefficients (blue regions) with the negative IAAC (yellow maps) indicates favorable effect of the superimposed molecular portion (functional group or base scaffold) on the activity. The latter is negatively responsible for lowering the activity in case an overlap between the positive PPLScoefficients

and negative IAAC (and vice versa) occurs. Detailed analysis of the 3-D QSAR model was

conducted and is reported as Supporting Information (3-D QSAR Models’ Interpretation subsection). Alignment Assessment. Even though SB 3-D QSAR models provide the basis for design and activity prediction of new molecules, the workflow is incomplete if no alignment rules are given for the new upcoming molecules (test set or prediction set). Therefore, the superimposition of molecules under prediction must be carefully carried out and assessed for either SB or LB derived 3-D QSAR models. In the case of SB aligned 3-D QSAR, alignment of the test set molecules should be performed by means of molecular docking. Nevertheless, docking


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algorithms are not yet fully optimized and reliable, and, moreover, during the simulation the proteins are still mainly considered as rigid.72 Therefore, the SB alignment of the test set conformations could result in non-optimal positioning and lead to a higher prediction error. The latter can be in part by-passed if LB approaches are taken into account as alternative aligning methodology. The SB (i.e. Vina, DOCK, PLANTS, and Surflex-Dock algorithms) and LB (employing software couple Balloon/ShaEP70,71) alignments were assessed for training set experimental conformations (EC) through the protocols similar to the previously reported ones.72 Further details are reported in Supporting Information (Alignment Assessment subsection). Moreover, the best performing SB and LB protocols were utilized to align the test sets compounds (Table 3, Supporting Information Tables S43-S50).

DESIGN AND SYNTHESIS OF NOVEL MAO B INHIBITORS Design of Novel MAO B Inhibitors. In structure-based drug design, the currently available molecular docking programs unfortunately cannot reliably and consistently predict a ligand-protein binding mode and the binding affinity simultaneously.78 In ligand-based drug design, on the other hand, the result of the alignment between the co-crystallized inhibitor and the previously untested molecule often gives a false picture that there is a high level of similarity between the structures, given that there is no information about the examined molecule binding mode.78 Therefore, a consensus score strategy, which is based on the synergic use of the two main computer-aided drug design (CADD) methodologies (SB and LB methods), may be used in design as it could improve the VS capability in recognizing new bioactive compounds. For the purpose of this paper, the rational design of novel MAO B inhibitors was conducted by modifying 2V61 crystal as a lead, by applying the references received from the best generated SB 3-D QSAR model (i.e. the N probe SB 3-D QSAR model, Table 5) and 19 ACS Paragon Plus Environment


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summarized within the universal SAR model (Figure 5). Additional information concerning which structural changes are required to modify 2V61 were obtained from the SB/LB alignment assessment and the activity prediction of test set compounds. As a result, a number of 128 coumarin-based structures were rationally designed and aligned into MAO B active site by applying either the best performing SB or LB protocols. The rationale for designing of novel anti-MAO B scaffolds is presented in detail in Rules for Design of Novel MAO B Inhibitors section. The eighteen structures (Table 11) met all of the requirements postulated by SB 3-D QSARs to be considered as novel MAO B inhibitors. Six of them (D116, D120, D121, D123, D124, and D128) were synthesized and evaluated as MAO A and MAO B inhibitors in vitro, for the purpose of defining their activity and selectivity. Synthesis of Designed Compounds D116-D128. The synthetic pathway under which the designed compounds D116-D128 (Table 11) were produced had assumed three consecutive steps (Scheme 1). By utilizing the conventional Hoesch reaction, 4-hydroxyscopoletin79 (A) was initially synthesized to serve as a starting core for designed scaffolds production. Synthon A was then submitted to three-component microwave-assisted Biginelli coupling with 2-oxoacetamide and either urea, N-methylurea, or N,N’-dimethylurea to create benzopyranopyrimidines B1-B4, respectively.62 Upon adding of N-methylurea a mixture of compounds B3 and B4 in ratio 52:30% was obtained, after which intermediates were separated by flash column chromatography. In the final step, the benzylation of B1-B4 with the appropriate benzylbromides (1-(bromomethyl)-3-chlorobenzene, (bromomethyl)-3-chloro-5-methylbenzene,

4-(bromomethyl)-2-chloro-1-methylbenzene, or

1-

2-(bromomethyl)-4-chloro-1-methylbenzene)

furnished target compounds D116-D128 in high yields, respectively.80 Detailed procedures by which the described compounds were obtained and structurally interpreted are reported in


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Supporting Information Chemistry and Spectral Data Interpretation section, along with appropriate NMR spectral data (Supporting Information Figures S36–S57). The synthesis and evaluation of other compounds will be the topic of future work.

Scheme 1. Synthesis of designed compounds D116-D128. Reagents and conditions: (i) fused ZnCl2,

anhydrous

ether,

HCl↑;

(ii) hydrolysis,

130°C,

(iii) 3

–

3.2

min;

(iv)

R3R4R5ClC6H4CH2Br, K2CO3, EtOH, reflux.

BIOCHEMISTY METHODS Inhibition of human MAO A and MAO B activity in vitro. The effects of the newly designed (Table 11, Figure 9) and synthesized compounds (Scheme 1) on human MAO (hMAO) isoforms enzymatic activity were evaluated by a fluorimetric method81 (λem/ex = 585/530 nm) defined by the EnzyChromTM Monoamine Oxidase Assay Kit (BioAssay Systems), using a 21 ACS Paragon Plus Environment


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multidetection microplate fluorescence reader (FLX80, Bio-Tek Instruments, Inc., Winooski, VT, USA). The synthesized compounds were assayed in the concentration range from 100 to 0.78 nM (standard solutions were prepared by double dilution in Assay Buffer) and compared with reference inhibitors: clorgyline (to inhibit MAO A) and pargyline (to inhibit MAO B). In the experimental conditions, MAO A catalyzed the oxidation of 150 nM of p-tyramine to phydroxyphenetylacetaldehyde, while MAO B converted 22 nM of substrate into its product. The results are expressed as IC50 values, presenting the concentration of the test compound that reduces 50% of the initial MAO activity, calculated with OriginPro 8 statistical software82 by using Nonlinear Curve Fit Growth/Sigmoidal Dose-response function. The percentage of inhibition was plotted against the inhibitors’ concentration, for the purpose of obtaining the inhibitors’ IC50 value. To evaluate the mechanism of inhibition of MAO A and MAO B, the effect of the inhibitors on the Michaelis-Menten constant (Km) and maximum reaction rate (Vmax) values was obtained by plotting the data according to the Lineweaver-Burk method. The Ki values for the inhibition of MAO A and MAO B were determined from the double reciprocal plot: 1/rate of formation (1/V) versus 1/substrate concentration (0.78 – 100 nM) in the presence of IC50 concentrations of tested inhibitors. The Ki values were calculated from the interception of the curves obtained by plotting 1/V versus the inhibitor concentration for each substrate concentration. Additionally, the Ki value was estimated by plotting the slope of each Lineweaver-Burk plot versus the inhibitor concentration. The log P and PAMPA Permeation Assays. The n-octanol-water partition coefficient of MAO inhibitors was measured by applying the potentiometric method73 by means of the Sirius GLpKa instrument. The blood-brain barrier penetration potential was determined by PAMPA 22 ACS Paragon Plus Environment

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(parallel artificial membrane permeability assay), a method developed for rapid determination of passive transport. In PAMPA, an artificial liquid membrane is used to separate two compartments, one containing a buffer solution of compounds to be tested (defined as the donor compartment) and the other containing an initial fresh buffer solution (defined as the acceptor compartment), which are assembled in a “sandwich-like” configuration. The permeability of tested compounds through the artificial membrane is determined after a fixed incubation time by disassembling the “sandwich” and measuring sample concentrations in the donor and the acceptor compartments. The HDM-PAMPA assay74 (hexadecane membrane assay targeting GI track) was employed to predict passive transcellular permeability by using an artificial liquid membrane composed of hexadecane supported on polycarbonate filters. RESULTS AND DISCUSSION 3-D QSAR Models Analysis. The initial models, generated on default setup, led to unsatisfactory r2 and q2 values (Supporting Information Tables S3-S10, grid spacing equal to 1Å). Therefore, after the systematic search for optimal grid spacing (Supporting Information Tables S3-S10), variable pretreatment by VPO (Table 4, Supporting Information Tables S11S18) and feature variable selection by SA (Supporting Information Tables S19-S26) have been performed, the eight final 3-D QSAR models (Table 5) were generated by means of PLS statistical analysis as implemented in 3-D QSAutogrid/R protocol.45 Satisfactory results were obtained for all of the probes except for the desolvation probe, confirming the predictive capabilities and robustness of the 3-D QSARs (Supporting Information Tables S27-42). Three of them (N, NA, and HD) were selected for further investigations. The derived probe PLScoefficients and Activity Contribution plots are depicted in Figures 3, 4, and Supporting Information Figures S1-S26. 23 ACS Paragon Plus Environment


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Table 4. Statistical Results of 3-D QSAutogrid/R Models Derived after Variable Pretreatment Optimization Analysis. Model Probe Grid Step PCa CutOffb Zeroingc MinStdd r2e q2LOOf q2K5FCVg A 2.1 3 4 0.009 0.005 0.852 0.081 0.047 1 C 2.1 3 5 0.01 0.005 0.849 0.066 0.036 2 HD 2.2 2 5 0.01 0.05 0.751 0.106 0.103 3 OA 2.25 3 4 0.01 0.05 0.873 0.200 0.132 4 N 2.25 3 5 0.007 0.015 0.859 0.123 0.117 5 NA 2.25 4 4 0.01 0.05 0.867 0.187 0.176 6 e 2.45 2 5 0.001 0.05 0.629 0.128 0.118 7 d 2.50 3 1 0.005 0.05 0.379 0.051 0.046 8 a PC, optimal number of principal components/latent variables; b CutOff, maximum and minimum grid energy value; c Zeroing, zeroing of very low data points; d MinStd, Minimum Standard Deviation cut off; e 2 r , conventional square-correlation coefficient; f 2 q LOO, cross-validation correlation coefficient using the leave-one-out method; g 2 q K5FCV, cross-validation correlation coefficient using the cross-validation with 5-randomgroups-out (K-5-Fold) and 100 iterations.

Table 5. Statistical Results of 3-D QSAutogrid/R Models Derived after Simulated Annealing Optimization. Model Probe Grid step PCa r2b q2LOOc q2K5FCVd r2YS LOOe q2YS K5FCVf A 2.1 3 0.852 0.322 0.324 0.752 -0.210 9 C 2.1 3 0.849 0.466 0.433 0.741 -0.279 10 HD 2.2 2 0.751 0.514 0.432 0.714 -0.181 11 OA 2.25 3 0.873 0.498 0.418 0.714 -0.246 12 N 2.25 3 0.859 0.576 0.497 0.726 -0.226 13 NA 2.25 4 0.867 0.536 0.317 0.712 -0.137 14 e 2.45 2 0.629 0.368 0.323 0.552 -0.196 15 d 2.50 3 0.379 0.094 0.075 0.325 -0.278 16 a PC, optimal number of principal components/latent variables; b 2 r , conventional square-correlation coefficient; c 2 q LOO, cross-validation correlation coefficient using the leave-one-out method; d 2 q K5FCV, cross-validation correlation coefficient using the cross-validation with 5-randomgroups-out (K-5-Fold) and 100 iterations; e 2 r YS, average square correlation coefficient obtained after Y-scrambling process using 100 iterations; f 2 q YS, average cross-validation correlation coefficient using the leave-one-out method obtained after Y-scrambling process, using 100 iterations.


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The substrate cavity within the MAO B active site region was accurately characterized by the amide nitrogen (N) probe PLS-coefficients plots (NPLS-coefficients). As described, the most important pocket within the substrate cavity, the recognition area, includes Gln206, Tyr398, Tyr435, and FAD, while the bottom of the pocket is made of Tyr60 and Phe343.1,15,16 This hydrophobic pocket establishes interactions with inhibitors precisely predicted by polyhedra of positive NPLS-coefficients located in proximity of the recognition area (Figures 3 and 4). In particular, the majority of positive NPLS-coefficients are positioned towards Tyr435 while the remaining smaller area overlaps the central FAD ring. Notably, polyhedrons inserted between the amino acids are shared by all training set compounds, while those overlapping the cofactor can be mainly attributed to the irreversible inhibitors bearing acetylenic or amine moieties linked to phenyl or aminoindan rings. The acetylenic moiety is a crucial pharmacophore for l-deprenyl (2BYB, Fig 3A), (R)-rasagiline analogues (1S2Q, Figure 3E; 1S3E, Supporting Information Figure S1B; 2C66, Supporting Information Figure S1D, 2C65, Supporting Information Figure S1F), and (S)rasagiline (1S2Y, Supporting Information Figure S1E). Those drugs modify the FAD structure by establishing the conjugate diene with cofactor N5 atom via the prop-1-yne scaffold. Covalent modification is confirmed by the position of corresponding AAC fields, inasmuch as the acetylenic scaffold is covered with coherent repulsive 2BYBAAC, 1S2QAAC, 1S3EAAC, 2C66AAC, 2C65AAC, and 1S2YAAC plots. The orientation of positive NPLS-coefficients and green ACCs towards Tyr435 indicates that acetylenic moiety is stabilized by steric interactions with Tyr435 prior to FAD covalent alteration. A further confirmation of this observation is the similar alignment of A and C probe-generated PLS-coefficients and AAC fields contours (Supporting Information Figures S3-S10). Another pathway for covalent modification involves the diene bridge formation between a primary amine and FAD C(4a) position, leading to the covalent adducts in the case of



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mofegiline (2VZ2, Figure 3B), 1OJC (Figure 3C), phenelzyne (2VRM, Figure 3F), tranylcypromine (2XFU, Supporting Information Figure S1A), and benzylhydrazine (2VRL, Supporting Information Figure S1C). Considering the absolute lack of protein embedding, the 3D QSAR model correctly predicts the “irreversible” moieties through the alignment of repulsive 2VZ2ACC, 1OJCACC, 2VRMACC, 2XFUACC, and 2VRLACC polyhedrons which cover all of the amine functional groups responsible for the interaction with FAD. In the structure of mofegiline there is a (Z)-2-(fluoromethylene)pentan-1-amine scaffold containing fluorine, but this particular atom has no influence on activity since it is eliminated during the formation of covalent bond with FAD.40 The model further indicates the significance of aromatic amino acids within the recognition site. In general, according to the localization of A, C, and N probe-derived PLScoefficients (Supporting Information Figures S1-S10, Figures 3-4, respectively) as well as of HD, OA, and NA probe-generated PLS-coefficients (Supporting Information Figures S11-S22), Tyr435 is pointed out as a residue that can equally suffer steric clash and/or act as hydrogenbond donor while interacting with the inhibitor. Thus, Tyr435 is activated if inhibitor contains Nmethyl function as in l-deprenyl’s N-methyl-N-(prop-2-ynyl)butan-2-amine scaffold. Such a bulky group has a strong influence on irreversible inhibition, given that it establishes hydrophobic interactions with Tyr435 aromatic side chain, as recognized by repulsive 2BYBACC maps. When N-methyl group is displaced from Tyr435, like in the structure of pargyline (1GOS, Figure 3D), the activity of the inhibitor is reduced in comparison with l-deprenyl 2BYB (Figure 3A). Corresponding methyl groups of 2BYB (Figure 3A) and 1GOS (Figure 3D) are misaligned in such a manner that pargyline’s N-methyl group is oriented towards Gln206 and its presence is not anticipated by 1GOSACC maps. On the other hand, MAO proteins react better with


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deprotonated amines as substrates or inhibitors;35 thus, when occupied with particular compounds the recognition site seems not to be involved in the proton deprivation.38 Therefore, the protonated nitrogen atoms of the N-propargyl-1-aminoindan scaffold (1S2Q, 1S2Y, 1S3E, and 2C66) cannot be involved in hydrogen bonds with Tyr435, which is best defined and validated upon the analysis of hydrogen bond donor (HD) model (Supporting Information Figures S11E, S12E, S12B, and S12D, respectively) where in case of (R)-rasagiline, there are HDPLS-coefficients and 1S2QACC of opposite signs. Even in methylated amines, as in 2C66 (Supporting Information Figure S12D), other structural parameters are responsible for the activity decrease. The inversion of N-atom stereochemistry form (R)- to (S)- strongly decreases the MAO B inhibitory activity (1S2Y, Table 2). Taken together, the N-methyl-N-(prop-2ynyl)butan-2-amine scaffold is designated as a pharmacophore appropriate to be converted to N,N-dimethyl-1-phenylpropan-2-amine frame after excluding the acetylenic moiety. Such converted scaffold might be considered as a starting point for future design of novel MAO B inhibitors.



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Figure 3. PLS-coefficients contour maps (PPLS-coefficients positive red, negative blue) and Activity Contribution plots (IACC, positive green, negative yellow) derived from N probe model analysis for compound 2BYB (A), 2VZ2 (B), 1OJC (C), 1GOS (D), 1S2Q (E), and 2VRM (F). Compounds are sorted in decreasing order of activity. Fields are aligned into the MAO B active site. Amino acid residues are depicted in white, FAD is depicted in medium purple.


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Further positive interactions between the N-methyl group and Tyr435 were observed during the interpretation of 2V61 activity (Figure 4A). Within the structure of 2V61, coumarin core is substituted at position 4 by N-methylethanamine, while the position 7 is occupied by mchlorobenzyloxyl scaffold. Despite the fact that 2V61 is unable to covalently modify FAD, it exerts high activity primarily due to the hydrophobic interactions between the N-CH3 and Tyr435, described by a large green 2V61ACC map. The maps further indicate that Tyr435 may be involved in hydrogen bonding via hydroxyl group owing to residue’s ability to recognize the hydrogen bond accepting amide carbonyls of the central phenyl ring side chains, i.e. within N-(2aminoethyl)acetamide of 1OJC (Figure 3C), 2-(ethylamino)propanamide function of safinamide (2V5Z, Figure 4C), 5-ethylthiazolidine-2,4-dione scaffold of pioglitazone (4A79, Figure 4D), and aldehyde carbonyl group at position 4 of coumarin core (2V60, Figure 4B). These observations were also predicted by HD probe in the form of the superimposed positive HDPLScoefficients

and HD probe-derived 1OJCACC, 2V60ACC, and 4A79ACC contours (Supporting

Information Figures S11C, S13B, and S13D, respectively). The N and HD probe-derived contours indications were in full agreement with 2V60 crystallography data where the C4 aldehyde oxygen is H-bonded with both the hydroxyl group of Tyr435 and a water molecule in front of the flavin.41 However, this ligand-Tyr435 H-bond linking was not found in the structures of other reversible co-crystallized MAO B complexes, where binding interactions were generally limited to van der Waals interactions or hydrophobic contacts. On the other hand, a novel clinical drug safinamide (2V5Z, Figure 4C) exerts its activity by means of two hydrogen bonds that interconnect its amide group and Gln206.41 Hydrogen bonding with Tyr435 is partially excluded as the negative HD probe-derived 2V5ZACC field overlaps the positive HDPLS-coefficients (Supporting Information Figure S13C), which implies that such an interaction is unfavorable for the activity,



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while the combination of red OAPLS-coefficients, NAPLS-coefficients and corresponding green 2V5ZACC maps predicts the opposite (Supporting Information Figures S17C and S21C, respectively). Therefore, this disagreement between the probes somehow indicates that a hydrogen bond with Tyr435 may be unfavorable for safinamide. It is also possible to conclude that both N and HD models imply establishment of the hydrogen bond between the carbonyl in proximity to the central phenyl ring and the recognition area, and could give some hints for design of novel reversible MAO B inhibitors. As 2V61 and 2V60 share identical backbone in the form of 7-(3chlorobenzyloxy)-coumarin, the slightly lower potency of 2V60 can be attributed to the absence of methyl group in the recognition area. Furthermore, crystallography findings44 demonstrate that the attachment of 5-ethylthiazolidine-2,4-dione ring to central phenyl residue, as observed in the structures of pioglitazone (4A79, Figure 4D, activity comparable to safinamide) and rosiglitasone (4A7A, Supporting Information Figure S2B), still keeps the activity of MAO B inhibitor at a notable level. Within both of the compounds the 4-carbonyl group of 5ethylthiazolidine-2,4-dione is oriented towards the recognition site. Notwithstanding that this group is predicted, by N or HD models, as a potential hydrogen-bond acceptor for Tyr435, it is involved in hydrogen bonding with ordered water molecules.44

A closer look into the

pioglitazone bioactive conformation revealed the reason of 4-carbonyl group inability to accept the hydrogen-bond from protein: by comparison of 2V60 and 4A79 alignments it was clear that pioglitazone 4-carbonyl group is in the spatial position equivalent to the methylene group of 2V60 aldehyde function, i.e. the carbonyl oxygen is too far away from Tyr435 in comparison with aldehyde to accept the hydrogen bond. The N probe model characterizes the interactions between inhibitors, Tyr435, and FAD in detail, but also specifies that interactions with Tyr 398 are not important for the inhibitors’ activity.


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Figure 4. PLS-coefficients contour maps (PPLS-coefficients positive red, negative blue) and Activity Contribution plots (IACC positive green, negative yellow) derived from N probe model analysis for compound 2V61 (A), 2V60 (B), 2V5Z (C), 4A79 (D), 2BK3 (E), and 1OJA (F). Compounds are sorted in decreasing order of activity. Fields are aligned into the MAO B active site. Amino acid residues are depicted in white, FAD is depicted in medium purple.



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Further on, the model analyzed the MAO B gate between the entrance and the substrate site cavity comprising Phe168, Leu171, Ile199, and Tyr326, given that the positive NPLS-coefficients plot covers the region towards Ile199. Considering the inhibitors’ structures alone, there are three structural patterns that can open the gate, thus providing some selectivity for a given inhibitor: the central benzene ring, the aminoindan ring, or the coumarin ring. As the majority of aminoindan compounds ((R)-rasagiline derivatives) exert lower potential, phenyl and coumarin cores are pointed out as optimal for the construction of novel compounds. The lower activity of (R)-rasagiline derivatives can be attributed to the not fully favorable alignment of aminoindan ring position as related to Ile199. The troublesome orientation is a consequence of inadequate substitution of aminoindan with hydroxyl group or other functional groups at various positions, despite the fact that the Cys172 is engaged in a hydrogen bond with hydroxyl group at position 6 (1S3E, Supporting Information Figure S1B),36 with hydroxyl group at position 4 (2C66, Supporting Information Figure S1D), or with carbamate at position 4 (2C65, Table 2, Supporting Information Figure S1F). Nevertheless, the positive pair of NPLS-coefficients and N probe-derived 1S3EACC maps in front of Cys172 may be considered as an indicator that the introduction of hydrogen-bond acceptor group into the position C6 of coumarin core, to face the Cys172, may additionally enhance the activity of MAO B inhibitor. According to the orientation of NPLScoefficients,

even the group containing both hydrophobic and hydrogen-bond accepting features may

be appropriate at position 6 since such bioisoster may also activate Ile199, besides Cys172. The model also indicates that the central core of MAO B inhibitor must possess the ability to interact with the recognition area bottom residues, Tyr60, Phe343, and Leu328, located beneath it. As it fulfills those demands, coumain core is presented as an ideal foundation in future considerations of novel MAO B inhibitors design. Hence, for both 2V61 and 2V60


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coumarin representatives (Figure 4A and 4B), the phenyl ring of chromen-2H-dione is placed in the proximity of Ile199 and covered by favorable combination of positive N probe-derived PLScoefficients and 2V61ACC/2V60ACC maps. This is also a feature of all benzene-based and aminoindan-based compounds which share the common aromatic moiety with coumarin. Therefore, hydrophobic interactions between the central ring and Ile199 are of exciting importance as they suggest that the presence of aromatic scaffold is mandatory to open the gate between the entrance and substrate site cavity. However, differently from other molecules, coumarins set up interactions with Tyr60, Leu328, and Phe343. In particular, electrostatic interactions between the coumarin lactone carbonyl and Tyr60 were confirmed on two levels: first by the alignment of negative NPLS-coefficients and analogous 2V61ACC/2V60ACC maps over the coumarin lactone carbonyl and second by the superposition of positive NPLS-coefficients and interrelated 2V61ACC/2V60ACC contours in front of the Tyr60 hydroxyl group. External validation of peculiar interactions was received from analogous NA (Supporting Information Figures S21A and S21B) and OA (Supporting Information Figures S17A and S17B) probes maps which suggested that coumarin lactone oxygen could accept the hydrogen from Tyr60 in order to form intermolecular hydrogen bond. Still, for 2V61 and 2V60, particular interaction is anticipated only by 3-D QSAR maps and not confirmed by crystallography. Similar affinities to interfere with Tyr60 were also anticipated for carbonyl groups at position 2 of pioglitazone’s and rosiglitazone’s 5-ethylthiazolidine-2,4-dione ring, as well as for indole-2,3-dione of isatin (1OJA, Figure 4F). Crystallography, however, reveals that the 2-carbonyl group of 4A79 and 4A7A, along with the 2-oxo group and the pyrrole NH of 1OJA, are H-bonded to ordered water molecules present in the active site, whereas the 3-oxo function is not involved in any Hbond,40,43 which generally makes 2,4-diones unfavorable pharmacophores for future design.



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Moreover, the positioning of isatin ring was balanced within the active site as its binding interactions involve many van der Waals contacts with the recognition area bottom residues Tyr60, Phe343, and Leu328 in the hydrophobic substrate cavity, placing the aromatic scaffold far from the gate residues. Meanwhile, high activity of benzene derivatives l-deprenyl, safinamide, 2V60, and 2V61, in comparison with isatin, can be attributed to the fact that their central aromatic moieties established only weak hydrophobic interactions with Tyr60, Phe343, and Leu328 side chain while the main interference was with the gate residues. As already emphasized, Phe168-Leu171-Ile199-Tyr326 gate opening provides a possibility for potential MAO B inhibitor to fill out both cavities and, thus, exert higher selectivity towards MAO B than towards MAO A.1,15,16 However, little is known about the actual intermolecular interactions between the enzyme and inhibitors required to trigger the gate opening. For that purpose, the intriguing orientation of superimposed PLS-coefficients and Activity Contribution Fields bearing the same signs made the N probe model competent to describe the interactions between the inhibitor and the entrance cavity. The central phenyl core, common for all of the inhibitors, unequivocally forces Ile199 towards the conformational change as confirmed by the position of positive NPLS-coefficients and N probe-based ACC contours inbetween the Ile199 and benzene ring (Figure 3 and 4, Supporting Information Figures S1 and S2). Similar orientation of PLS-coefficients was also retrieved from all of the remaining probes (Supporting Information Figures S3-22) except for the e probe, while only the inhibitors with the phenyl ring in proximity of Ile199 featured corresponding positive ACC fields. Moreover, the conformations of two of the covalent compounds, mofegiline (2VZ2, Figure 3B) and 1OJC (Figure 3C) suggested that the presence of electron-withdrawing substituent at p-position, with the strength to penetrate into the entrance cavity, facilitates Ile199 shift and may be decisive


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when speaking about the inhibitors’ potency and selectivity. Hence, p-F and p-Cl substituents raise the 2VZ2 and 1OJC activities, respectively, to a nanomolar level. Yet, the observed compounds fill out the substrate cavity only, which makes them eligible to inhibit MAO A as well.1,15,16 Within the entire training set, only seven compounds (2V61, 2V60, 2V5Z, 2BK3, 4A79, 4A7A, and 1OJ9) which are all classified as reversible inhibitors, are comfortably situated across both entrance and substrate site areas. The most active compound inside this subgroup, 2V61, contains the p-O as electron-withdrawing group which serves as a bridge between the benzyl scaffold and coumarin core. Compounds 2V60 and 2V5Z, as 5th and 6th compounds in overall activity ranking, also possess this chemical feature. But, what makes the p-electronwithdrawing group so important for the activity of MAO B inhibitor? It holds a scaffold that will replenish the entrance cavity and, equally important, it establishes electrostatic interactions with Tyr326, by means of which it is sealed in the active site and prevents Ile199 from restoring the initial closed conformation. This conclusion is based on the orientation of the negative NPLScoefficients

and the attractive N probe-derived 2V61ACC/2V60ACC/2V5ZACC maps, on the one hand,

and positive ePLS-coefficients and appertaining repulsive e probe-derived 2V61ACC/2V60ACC/2V5ZACC fields (Supporting Information Figures S25A, S25B, and S25C, respectively). There is an notable difference in the activity of compounds like (E,E)-farnesol (2BK3, Figure 4B) and 1,4-dyphenyl2-butene (1OJ9, Supporting Information Figure S2E) which do not include p-O in their structure but also span both cavities. The outstanding contribution to the activity is also given by mchlorobenzyl scaffold (2V61, Figure 4A; 2V60, Figure 4B) and m-fluorobenzyl (2V5Z, Figure 4C) linked to given p-oxygen atom. This scaffold is recognized by repulsive N probe-derived 2V61ACC/2V60ACC/2V5ZACC maps inserted in front of the Phe103 (comparative AAC maps also generated by A, and C probes, Supporting Information Figures S5A, S5B, S5C, S9A, S9B, and



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S9C, respectively), implicating strong hydrophobic attractions between two aromatic moieties. However, the model does not describe the influence of m-halogen which is most likely involved in induced dipole-dipole interactions with Phe103, providing the additional stabilization for the molecule within the cavity. The m-Cl-Ph frame is also attracted by Ile316 at the bottom of the entrance

cavity

according

to

the

alignment

of

yellow

N

probe-derived

2V61ACC/2V60ACC/2V5ZACC maps. Judging by the results obtained from even three probes that provide coherent results, m-chlorobenzyloxy group must be considered as a platform in future design of MAO B inhibitors, particularly given the fact that swapping of this pharmacophore may negatively alter the inhibitor’s potential. Thus, the replacement of m-chlorobenzyloxyl scaffold with 5-ethyl-2-propylpyridine or N-methyl-N-propylpyridin-2-amine diminishes the biopotential of 4A79 (Figure 4D) and (4A7A, Supporting Information Figure S2B), respectively. As a consequence, Phe103, located in the loop guarding the active site cavity, is slightly displaced in crystal structure of 4A79 to avoid the steric clashes with the pioglitazone pyridine ring side chain ethyl substituent.43 On the other hand, the electron density does not even include the pyridyl ring of rosiglitasone, most likely considering that during the crystallization this compound undergoes slow oxidative cleavage of the bond adjacent to the tertiary aniline nitrogen accompanied by a consequent loss of the pyridyl ring.43 The analysis of HD and NA (Supporting Information Figures S11-S14 and S19-S22) probe maps led to similar conclusions, which are not reported here for the sake of clarity and avoiding redundancy. Based on some remarks already reported in the discussion of N probe maps, it may be further concluded that probes with similar physico-chemical properties, i.e. N (Figures 3 and 4, Supporting Information Figures S1 and S2) and A (Supporting Information Figures S3-S6), N and C (Supporting Information Figures S7-S10), or OA (Supporting


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Information Figures S15-S18) and NA (Supporting Information Figures S19-S22), as well as NA and e (Supporting Information Figures S23-S26), supported each other by means of providing similar maps in corresponding MAO B active site domains. MAO B Inhibitors Molecular Determinant Analysis. The rationale arising from the indepth consideration of all 3-D QSAR maps was consolidated into the overall structure–activity relationships (SAR) model. A unique pharmacophore scheme comprising all the required MAO B inhibitor features was further derived from the SAR model, using the 2V61 compound’s structure as a template (Figure 5). Accordingly, there are three essential structural features that a single selective MAO B inhibitor should contain in order to inhibit the enzyme with high potency. First, the active molecule should contain a central phenyl ring that keeps the gate between the entrance and the substrate cavity opened by forcing Ile199 to adopt unnatural open side chain conformation. The central core should be considered as the first steric feature. Second, aromatic moiety should be fused with six-membered aliphatic cycle which can bear substituents capable of animating Tyr 435 from the substrate cavity recognition area, as well as Tyr60, Phe343, and Leu328 at the bottom of the recognition area. Third, the fundamental aromatic moiety should be extended with p-positioned electron-withdrawing ether linker that merges benzyl scaffold with the remaining part of the molecule, in order to fill out the entrance cavity. This benzyl scaffold can be considered as the second steric feature. The pharmacophoric motif in the form of essential central phenyl ring is a result of the entire training set superimposition. All of the quantitative models suggest that its repulsive steric interactions are crucial for Phe168-Leu171-Ile199-Tyr326 gate opening. Furthermore, considering that m-position of the central phenyl ring is to be additionally substituted by H-bond



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acceptor group, the central core should be maintained to establish an H-bond interaction with Cys172 at the top of the active site and, consequently, to increase the biological activity. The improvement of the central core with the five- or six-membered rings is considered as important for the high bioactivity of MAO B inhibitors. However, according to the 3-D QSAR models obtained, five-membered rings should be replaced with six-membered ones for several reasons. First, the orientation of PLS-coefficients common to all of the probes proved that the high activity of aminoindane ring derivatives (the cyclopentane as an extension of the central core) is mainly the result of the presence of acetylenic moiety, which covalently modifies the active site FAD. The cyclopentane scaffold within the aminoindan contributes only slightly to the activity, through the weak hydrophobic interactions with Tyr326. The presence of fivemembered heterocycles like pyrrolidine-2,4-diones decreases the activity as the central core is moved deeper into the substrate cavity, away from Ile199. The complete avoidance of the basic ring upgrade by means of five- or six-membered rings attachment has a twofold scenario: to keep the acetylenic moiety directly attached to the central core by some linker and to rule out the potential new drug as another highly active irreversible inhibitor having an undesired mode of action; or to retain the substituents, like 2-(ethylamino)propanamide or 5-ethylthiazolidine-2,4dione, which cannot establish H-bond interactions with Tyr435 and exert full potential as MAO B inhibitors. Each of the cases leads to reduced applicability in comparison with compounds 2V61 and 2V60. These are the only MAO B inhibitors that contain six-membered ring central core upgrade in the form of 5,6-dyhidro-2H-pyran-2-one; accordingly, the coumarin core is presented as the best synthon to build the new active compound. Coumarin ring may serve as an H-bond acceptor to Tyr60 at the bottom of the active site, and, more important, as a bearer of functionalities that can trigger recognition cavity to embrace the inhibitor. Thus, the coumarin


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core nicely fills out the entire substrate cavity and it may be considered as a mixed steric/H-bond acceptor feature. Further, the substitution of coumarin C4 position with a group containing both H-bond acceptor and hydrophobic features, like the acetyl one, would guarantee a certain biological response. After precise modification, a molecule would be stabilized in the active site on two levels: by hydrophobic interactions between the methyl group, as a third steric function, and Tyr435 aromatic scaffold, as well as by a hydrogen bond between the carbonyl group and Tyr435 hydroxyl. Moreover, both 3-D QSARs and crystallography for 2V5Z further suggest that the coumarin C3 hydrogen should be replaced with isobytiramide in order to activate Gln206 through the potential establishment of hydrogen bonding. Those H-bonds may be considered in future design as capital assets since they can additionally stabilize the coumarin-based inhibitor in the MAO B active site. Further derivation of amide nitrogen with a bulkier aromatic group or an aliphatic group might stabilize the new compound towards the FAD flavin ring. Between the first and the second steric feature, a flexible node (such as an ether group) would be preferred to assure the compound’s flexibility and facilitate the alignment of the inhibitor in the active site when rigid aromatic systems are used. At the same time this ether linkage should be electrostatically attracted to Tyr326 to provide the additional stabilization for the inhibitor. The second steric function, the benzyl moiety, is supposed to gather Phe103, Ile199, and Ile316 around the molecule, thus satisfying the entrance cavity need for advantageous repulsive interactions. The substitution of the second steric function with additional methylene groups (i.e. the introduction of propyl- or butylbenzene scafolds) should be excluded in order to avoid the steric collision between the benzyl moiety and Phe103. However, the orto-carbon atoms of the second steric feature might be additionally substituted with a bulky



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group to clash with Ile199 or Ile316. Groups capable of establishing electrostatic and limited steric interactions with Phe103 are preferred at meta- or para-positions.

Figure 5. SAR of MAO B inhibitors, taking into account the most active reversible inhibitor 2V61 structure as a template. The red rectangle encircles groups occupying the substrate cavity; the blue rectangle specifies linkers between the cavities; the green rectangle indicates groups located in the entrance cavity.

Alignment Rules Assessment. The 3-D coordinates of the reversible MAO B inhibitors taken from their corresponding minimized complexes were afterwards used in order to learn how to reproduce the co-crystallized bioactive conformations by means of either the SB or the LB


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methods. At this stage, the contribution of alignment assessment in future design of MAO B inhibitors was considered by examining whether or not there are SB and LB algorithms capable of reproducing the poses of reversible inhibitors only. The bipartisan nature of the MAO B active site dictates that the future design should be focused on creating compounds that will captivate both cavities, thus forcing Ile199 of MAO B to adopt the open conformation.1,15,16 Theory also implies that such drug candidates may be much more selective towards MAO B than towards MAO A.1,15,16 Irreversible MAO B inhibitors are located in the substrate cavity only, but their ability to inactivate the MAO B completely rules them out as leads for future design. On the other hand, the majority of reversible inhibitors (7 out of 11 in this training set: 1OJ9, 2BK3, 2V5Z, 2V60, 2V61, 4A79, and 4A7A, Table 2) are traversed through the complete active site. Among the reversible inhibitors, the highest activity was recorded in 2V61, and 3-D QSAR studies specified this compound as a potential lead in future design. Therefore, the “learning-toalign” procedure with all its re-alignment and cross-alignment protocols exploited several SB and LB alignment programs in order to provide and optimize the methodology by means of which some external highly active reverse MAO B inhibitors with unknown binding mode can be adequately positioned into the enzyme's active site. Structure-Based Alignment Assessment. As for the SB approach, the four-step procedure included the application of Experimental Conformation Re-Docking (ECRD), Randomized Conformation Re-Docking (RCRD), Experimental Conformation Cross-Docking (ECCD), and Randomized Conformation Cross-Docking (RCCD) was applied, thus implicitly including the protein flexibility in a discrete fashion way. Docking assessment was conducted by four different docking programs: Vina,66 DOCK,67 PLANTS68 and Surflex-Dock,69 utilizing all available



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scoring functions. The summary of SB alignment assessment is presented in Tables 6 and 7, and by Figure 6 and Supporting Information Figure S27. When docking assessment is performed, there is a need to rapidly evaluate the successful placement of a ligand into the protein from the native or a different complex with reasonable accuracy. The latter ability is measured by means of the docking accuracy (DA) function,68 which relies on the RMSD values. During the ECRD stage, Vina, DOCK (Figure 6), and PLANTS chemplp scoring functions possessed the highest ability to reproduce the known binding conformations with DA values of 70%, 50%, and 55%, respectively (Table 6). Notably, Vina reproduced the bioactive conformations for three of the four most active reversible inhibitors (2V61, 2V60, and 4A79), with the highest accuracy (RMSD values equal to 1.081, 0.498, and 0.723 Å, respectively), while the structure of safinamide (2V5Z) was completely misdocked (RMSD = 9.109 Å). The highest level of accuracy was retained by DOCK program which predicted the crystal poses of 2V61 (RMSD = 2.645 Figure 6A), 4A79 (RMSD =1.435 Å, Figure 6D), and safinamide (RMSD = 1.851 Å, Figure 6C) while 2V60 was incorrectly docked (Figure 6B). PLANTS chemplp algorithm proved to be the worst one in the re-docking of the most active compounds, whereby only safinamide and pioglitazone were correctly repositioned (RMSD = 1.381 Å, RMSD = 1.436 Å, respectively), while both coumarin derivatives were superimposed to the native conformation with high errors (RMSD ≥ 8 Å). In the second docking assessment step (RCRD), the re-docking was repeated using randomly generated conformations; consequently an increase of DA for Vina and DOCK was observed (Table 6).


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Table 6. Structure-Based Re-Alignment Assessment for the Reversible Monoamine Oxidase B Inhibitors. PLANTS Comp. Vina DOCK Surflex-Dock a (ЕС ) chemplp plp plp95 1.837 1.844 2.412 2.880 1OJ9 0.835b 2.510 4.478 3.405 2.264 3.555 1OJA 3.292 3.298 8.012 7.988 8.598 8.326 2BK3 8.294 2.054 2.266 2.258 2.244 2.416 2C67 2.300 0.645 1.381 1.345 1.664 9.400 2V5Z 9.109 1.851 8.268 8.297 8.319 3.769 2V60 0.498 8.222 8.510 8.516 8.540 4.398 2V61 1.081 2.645 1.661 1.651 6.297 5.217 2XFN 1.614 8.670 c NA NA NA 3.751 3PO7 2.538 2.435 0.723 1.435 1.436 0.784 1.345 10.401 4A79 1.343 1.185 7.979 8.429 4A7A 1.976 2.688 70 50 55 45 35 10 DA% 1.587 2.599 2.438 2.814 1OJ9 0.899 2.568 3.398 3.395 2.292 3.630 1OJA 3.288 2.245 8.026 8.399 2.282 4.903 2BK3 1.722 1.783 2.281 5.534 2.260 2.179 2C67 2.386 0.331 1.604 1.589 9.574 1.129 9.586 9.460 2V5Z 8.207 8.234 8.236 8.036 2V60 2.517 1.528 8.468 8.468 8.473 4.477 2V61 1.073 2.673 1.720 1.658 6.287 8.009 2XFN 0.521 8.626 NA NA NA 5.250 3PO7 1.270 2.382 1.069 0.681 1.569 9.989 4A79 0.773 10.15 1.097 1.082 8.354 3.678 4A7A 1.974 1.525 90 65 45 45 30 10 DA% a EC, experimental conformation; a Root-Mean-Square-Deviation measured between the heavy atoms of the ligand’s experimental and the ligand’s re-aligned conformation upon ECRD and RCRD procedures; b NA, not available. Experimental Conformation Re-Docking (ECRD)

Procedure

Randomized Conformation Re-Docking (RCRD)

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The third level of docking assessment (Table 7) was achieved by means of a crossdocking procedure using the experimental conformation docked in all the available receptors while excluding the native ones (ECCD). Surprisingly, DOCK (DA of 35%) was the only program able to perform cross-docking of the most active reversible inhibitors. Despite the higher DA, Surflex-Dock was discarded as a cross-docking tool as it was completely



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inapplicable during the re-docking stage. The 2V61, 2V60, and 2V5Z poses were correctly cross-docked (RMSD = 2.27 Å, RMSD = 2.253 Å, RMSD = 0.928 Å, respectively), while 4A79 was mis-docked. In the last step (RCCA, Table 7), the cross-docking procedure was repeated using the modeled ligands and DOCK preserved its cross-docking effectiveness with DA% of 45 (RMSD values for 2V61, 2V60 and 2V5Z were 2.663 Å, 2.015 Å, 1.636 Å, respectively). The subsequent inspections of complexes indicated that low DA values in the cross-docking experiments can be attributed to the closed Ile199 conformation in the crystal structures of 1OJA40 and 2C67.36


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Table 7. Structure-Based Cross-Alignment Assessment for the Monoamine Oxidase B Reversible Inhibitors. PLANTS Comp. Vina DOCK Surflex-Dock a (ЕС ) chemplp plp plp95 8.460b 2.454 12.298 5.135 8.478 NAc 1OJ9 9.120 4.046 5.139 12.841 4.139 2.249 1OJA 4.979 2.206 9.166 5.900 3.399 2.309 2BK3 5.325 0.456 7.903 5.556 5.342 4.234 2C67 4.204 0.928 5.833 1.045 4.164 1.455 2V5Z 10.962 2.253 4.320 11.164 1.488 0.637 2V60 4.694 2.27 4.254 8.532 8.325 2.631 2V61 15.780 9.651 8.810 8.086 10.466 8.351 2XFN 4.096 2.443 10.822 13.968 5.549 2.458 3PO7 6.262 9.127 9.977 9.891 10.396 10.216 4A79 6.739 3.08 9.209 12.48 8.003 10.216 4A7A 0 35 0 0 0 40 DA% 8.460 2.454 12.298 5.135 8.478 n/a 1OJ9 9.120 4.046 5.139 12.841 4.139 2.249 1OJA 4.979 2.206 9.166 5.900 3.399 2.309 2BK3 5.325 0.456 7.903 5.556 5.342 4.234 2C67 11.196 1.636 3.833 1.367 9.555 1.637 2V5Z 7.612 2.015 11.224 11.109 8.257 1.637 2V60 4.694 2.270 4.254 8.532 8.325 2.631 2V61 15.780 9.651 8.810 8.086 10.466 8.351 2XFN 4.096 2.443 10.822 13.968 5.549 2.458 3PO7 6.146 15.510 10.009 9.934 9.841 15.720 4A79 10.888 1.507 11.348 9.033 8.328 1.387 4A7A 0 45 0 0 0 50 DA% a EC, experimental conformation; b Root-Mean-Square-Deviation measured between the heavy atoms of the ligand’s experimental and the ligand’s re-aligned conformation upon ECRD and RCRD procedures; c NA, not available. Experimental Conformation Cross-Docking (ECCD)

Procedure

Randomized Conformation Cross-Docking (RCCD)

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Figure 6. Structure-based alignment assessment of reversible MAO B inhibitors using DOCK: 2V61 (A), EC tan, ECRD blue, RCRD pink, ECCD green, RCCD red; 2V60 (B), EC yellow, ECRD blue, RCRD pink, ECCD green, RCCD red; 2V5Z (C), EC blue, ECRD purple, RCRD pink, ECCD green, RCCD red; 4A79 (D), EC pink, ECRD blue, RCRD pink, ECCD green, RCCD red; 2BK3 (E), EC green, ECRD blue, RCRD pink, ECCD yellow, RCCD red; 1OJA (F), EC blue, ECRD black, RCRD pink, ECCD green, RCCD red. 46 ACS Paragon Plus Environment

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The analysis of SB alignment assessment confirmed that none of the applied scoring functions are fully acceptable for docking of reversible co-crystallized MAO B inhibitors. According to the results in Tables 6 and 7, and Figure 6 and Supporting Information Figure S27, DOCK reproduced the crystal structures of reversible MAO B inhibitors with the lowest overall error (highest DA) and was therefore selected for the subsequent SB alignment of test set compounds. Ligand-Based Alignment Assessment. The LB based alignment assessment procedure was carried out in four consecutive steps: Experimental Conformation Re-Alignment (ECRA), Randomized Conformation Re-Alignment (RCRA), Experimental Conformation Cross-Alignment (ECCA), and Randomized Conformation Cross-Alignment (RCCA). For the procedures purpose, experimental conformations were extracted from their native complexes; all of the randomly generated conformations were obtained by applying the Balloon program,70 while the alignment procedure was conducted by means of the ShaEP toolkit.71 During the alignment, two conformations for each compound were produced: one generated using the field-graph matching and shape-density, called shaep conformation, and another using shape-density only, entitled shaep-onlyshape conformation. When the LB assessment is performed there is a requirement for an alignment program to successfully superimpose an experimental or random conformation of the ligand upon itself in the absence of the receptor 3D information, taking into account only the spatial arrangement of particular inhibitor. The alignment fitness can be quantified by evaluating either the RMSD or the subsequent alignment accuracy. When using the RMSD as a threshold, alignment assessment follows the same rules as already described for docking assessment.77 On the other hand,



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alignment accuracy (AA) can range between 0 (no alignment) and 1 (maximum performance of alignment),71 which is coherent with the results in this study. The results outlined in Table 8 indicate that the ECRA and RCRA experiments were performed with maximum accuracy for both types of conformations (Figure 7, Supporting Information Figures S28-30). The accuracy level slightly dropped during the ECCA evaluation where shaep conformations were aligned with the AA of 80% and shaep-onlyshape ones were reproduced with the AA of 75%. Inhibitors 1OJ9 (Supporting Information Figures S29E and S30E) and 4A7A (Supporting Information Figure S29B and S30B) were mis-aligned in both cases, while 1OJA was incorrectly aligned (Supporting Information Figure S28F). A drastic drop in the alignment accuracy of shaep structures was observed after conducting the RCCA experiments where only 45% of AA was achieved. As for the ECCA, the shaep-onlyshape conformers were aligned with the AA of 75%. Compounds 1OJ9 (Supporting Information Figures S29E and S30E), 4A79 (Figure 6D, Supporting Information Figure S28D) and 4A7A (Supporting Information Figures S29B and S30B) were inadequately positioned for both conformers while 2BK3 (Figure 6E, Supporting Information Figure S28E), 2V61 (Supporting Information Figure S28A), and 2V60 (Supporting Information Figure S28B) shaep conformations alignment represented additional weak points within the RCCA assessment protocol. Considering the fact that the only major difference in favor of shaep-onlyshape alignment was recorded in the RCCA (75% of AA in relation to 45% of AA for shaep), the shaep-onlyshape path reproduced the crystal structures of reversible MAO B inhibitors with higher accuracy and was later on used in the LB alignment of test set compounds during the validation of generated 3-D QSARs.


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Table 8. Ligand-Based Re-Alignment and Cross-Alignment Assessment of Reversible Monoamine Oxidase B Inhibitors. Comp. shaepComp. shaepshaepb Procedure shaepb a c a (ЕС ) onlyshape (ЕС ) onlyshapec 0.993 0.662 1OJ9 0.992d 1OJ9 0.616d 0.997 0.889 1OJA 0.997 1OJA 0.864 0.997 0.559 2BK3 0.998 2BK3 0.571 0.996 0.998 0.724 0.756 2C67 2C67 0.995 0.768 2V5Z 0.994 2V5Z 0.754 0.998 0.996 0.813 0.822 2V60 2V60 0.997 0.998 0.796 0.797 2V61 2V61 0.995 0.850 2XFN 0.995 2XFN 0.831 0.994 0.859 3PO7 0.996 3PO7 0.832 0.996 0.996 0.726 0.717 4A79 4A79 0.996 0.778 4A7A 0.993 4A7A 0.764 100 100 100 100 AA% AA% 3.532 3.646 1OJ9 4.597 1OJ9 4.653 0.817 0.591 1OJA 3.974 1OJA 0.842 2.637 2.643 2BK3 0.672 2BK3 3.471 0.701 0.594 0.724 0.617 2C67 2C67 0.618 0.639 2V5Z 0.742 2V5Z 2.753 0.781 0.662 3.939 0.681 2V60 2V60 0.757 0.643 3.474 0.655 2V61 2V61 0.594 0.663 2XFN 0.812 2XFN 0.813 0.672 0.659 3PO7 0.795 3PO7 0.815 9.602 0.704 9.543 4A79 4A79 11.397 3.613 3.665 4A7A 3.727 4A7A 4.226 80 75 45 75 AA% AA% a EC, experimental conformation; b The alignment conformation generated using the field-graph matching and shape-density, shaep conformation; c The alignment conformation generated using shape-density only, shaep-onlyshape conformation; d Alignment accuracy measured between the heavy atoms of the ligand’s experimental and the ligand’s re-aligned/cross-aligned conformation upon either ECRA, RCRA, ECCA, or RCCA procedures. Experimental Conformation Re-Alignment (ECRA)

Randomized Conformation Re-Alignment (RCRA) Randomized Conformation Cross-Alignment (ECCD)

Procedure

Experimental Conformation Cross-Alignment (ECCA)

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Figure 7. Ligand-based alignment assessment of reversible MAO B inhibitors virtually superimposed to MAO B active site using shaep-onlyshape module: 2V61 (A), EC tan, ECRA blue, RCRA pink, ECCA green, RCCA red; 2V60 (B), EC yellow, ECRA blue, RCRA pink, ECCA green, RCCA red; 2V5Z (C), EC blue, ECRA purple, RCRA pink, ECCA green, RCCA red; 4A79 (D), EC purple, ECRA blue, RCRA pink, ECCA green, RCCA red; 2BK3 (E), EC green, ECRA blue, RCRA pink, ECCA yellow, RCCA red; 1OJA (F), EC blue, ECRA black, RCRA pink, ECCA green, RCCA red.


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The External Validation of the N Probe 3-D QSAR Model. The predictive ability of N probe 3-D QSAR model was estimated by using the test set comprising coumarin derivatives available in literature as MAO B inhibitors (Tables 3 and 10, Supporting Information Tables S43-S50),19,46-61 classified by structural similarities into 7-BOC, 3-AC, 3-PC, and COM subgroups, respectively. Once the alignment rules for the reversible crystals were assessed, the 267 test set compounds were SB and LB aligned by applying DOCK and Balloon/ShaEP programs, respectively. Each of the subgroups showed a large variety in activity (pIC50 in range equal to 4.4, 4.46, 5.35, and 5.86 units for 7-BOC, 3-AC, 3-PC, COM, respectively). Therefore, for the entire set of compounds, SDEPLOO and SDEPK5FCV, (equal to 1.542 and 1.392 after SB alignment, or 1.439 and 1.328 after LB alignment, respectively), were highly acceptable (Table 9).

Table 9. Statistical Parameters of the N probe 3-D QSAR Model Test Set Predictions. SB alignment LB alignment Test set SDEPLOOa POSb SDEPK5FCVc POS SDEPLOO POS SDEPK5FCV POS 1.542 56 1.392 56 1.439 62 1.328 62 ALL 1.666 47 1.727 46 1.287 46 1.275 46 7-BOC 1.438 35 1.398 35 1.326 49 1.332 48 3-AC 1.747 40 1.783 39 1.502 42 1.427 40 3-PC 1.379 57 1.334 55 1.351 62 1.325 62 COM a SDEPLOO, standard deviation error of prediction using leave-one-out cross-validation; b SDEPK5FCV, standard deviation error of prediction using 5-random-groups-out (K-5-Fold) crossvalidation; c POS, test set/subgroup prediction of success expressed in %.

The 7-BOC Test Set Activity Prediction. In cases where 3-D information on enzymeinhibitor complexes are available, SB protocols (i.e. molecular docking) are mainly applied to validate previously untested ligands as potential inhibitors.78 Hence, the external validation of the N probe-derived 3-D QSAR model (Table 5, model 13) was in this work mainly relied on the test



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set SB alignment performed using DOCK. Among the four different subgroups, the 7-BOC was considered as the most important, owing to the structural similarity of the compounds with 2V61. The 7-BOC SB prediction was summarized with relatively high SDEPLOO and SDEPK5FCV values (1.666 and 1.727, respectively), and the average percentage of success (compounds predicted within an error of ± 1 pIC50 unit) of about 47 % (Supporting Information Table S43). However, an analysis focusing only on the statistical SDEP values could be misleading. As a result of thorough inspection of 7-BOCs lowest energy conformations (SB conformations, SBCs), it was determined that 19 compounds in total adopted docking poses comparable to 2V61 (Supporting Information Table S43, correctly docked ligands labeled as D). As many as eleven of them (7BOC_41, 7-BOC_43, 7-BOC_47, 7-BOC_50, 7-BOC_51, 7-BOC_53, 7-BOC_55, 7-BOC_56, 7-BOC_58, 7-BOC_76, and 7-BOC_77) were among the best predicted compounds (Supporting Information Table S43, Absolute Error of Prediction, AEP ≤ 1). Given that the model 13’ predictive ability seems to be SB alignment dependent, it was further postulated that the correct alignment may lead to accurate activity prediction (correct alignment-accurate activity prediction, CA-AAP), and vice versa. To confirm this statement, selected test set compounds (Table 10) were further loaded into NPLS-coefficients and N probe-derived 2V61ACC maps in order to perform a comprehensive quantitative analysis, insofar as none of the test set compounds were previously tested against the human MAO B as target of interest by means of binding mode and structure-activity relationships analysis. In the present paper, this step is concomitantly used to express the model 13 predictive ability even for molecules differently aligned as compared to those in the training set. Hence, the remarkable result of NPLS-coefficients/2V61ACC-based predictions (in conjunction with NPLS-coefficients/2V60ACC/2V5ZACC-based statements, in general) is that the best predicted


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compound within this fraction, 7-BOC_55 (Table 10 and Supporting Information Table S43, Figure 8A), accomplishes all of the molecular determinants predicted by the model for powerful MAO B inhibitor: a functional group bearing both H-bond acceptor and H-bond donor in the form of propionamide at the position 4 of the coumarin core to activate Tyr435, and electronwithdrawing group in the form of m-fluorobenzyl scaffold capable of establishing electrostatic or limited steric interaction with Phe103. However, it should be stressed that BOC_55 was not among the most active inhibitors within the subgroup of eleven best predicted compounds. In fact, given that the cross-validated pIC50 values were significantly lower in comparison with the bioactive ones, model 13 failed to predict the activity of all of the highest active 7-BOC ligands (pIC50 higher than 8). This imperfection of the model 13 may originate in its reliance on the training set and its narrow range of activity distribution (3.88 – 7.77 pKi units), in consequent inevitable DOCK alignment limitations, or in inaccurate activity data of the test set compounds. A drastic example of imprecise docking on activity prediction is observed in compound 7-BOC_3 (Table 10 and Supporting Information Table S43, Figure 8B), which was structurally modified so that it bears methyl groups at positions C3 and C4 of coumarin core, and m-position of benzyloxy moiety, respectively, whereby the error of prediction of -4.25 pIC50 units was a direct consequence of inaccurate alignment in comparison with 2V61. Within the structure of 7BOC_3, the 3,4-dimethyl substituted coumarin ring occupies the entrance cavity while the mCH3-benzyloxy residue pervades the substrate cavity; hence, the molecule adopts a completely inverse alignment than the one required by model 13. Consequently, this molecule has been discarded by the N probe-derived 3-D QSAR model for several reasons. First, the presence of the phenyl ring within the recognition area meets the basic requirements for hydrophobic features while the additional hydrogen-bond accepting groups which are expected to interact with Tyr60,



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Gln206, and Tyr435 are absent. Second, the MAO B recognition area hardly tolerates small hydrophobic voluminous intruders like C3 methyl group in front of FAD, and, consequently, the enzyme’s active site inverts the orientation of the molecule as a whole. Finally, the MAO B entrance cavity seems to be impatient with m-methyl or any bulky hydrophobic group situated in front of Phe103. Therefore, C7 m-chlorobenzyl or m-fluorobenzyl scaffolds somehow possess a unique ability to irritate Phe103. Any other functional group lessens the potential of inhibitors. According to the SAR model (Figure 5), a bulkier substituent may be introduced within the second steric feature to enhance the probability of interaction with Ile199, Ile316, or Tyr326. The borderline case in which the activity of compound is considerably over-predicted is observed in compound 7-BOC_65 (Table 10 and Supporting Information Table S43, Figure 8C). Although very similar by structure to 7-BOC_55, the introduction of a more voluminous N(hexan-2-yl)propionamide into coumarin C4 forces the compound to adopt inverse conformation in comparison with 2V61, leading to the noticed large error of prediction.


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Table 10. Summary of the N probe 3-D QSAR Model Structure-Based/Ligand-Based Predictive Ability. Entry

Ligand structure

pIC50

SB pred. pIC50a LOOb K5FCVc 6.30 6.14

LB pred. pIC50a LOOb K5FCVc 6.75 6.37

Ref.

7-BOC_55

7.30

7-BOC_3

8.36

4.11

4.07

6.91

6.37

47

5.00

7.49

7.13

6.07

6.32

57

3-AC_17

7.17

6.73

6.33

6.56

6.43

46

3-AC_18

8.55

5.96

5.74

6.42

5.48

46

3-AC_39

4.29

7.28

6.71

5.45

5.33

47

3-PC_2

7.10

6.89

6.43

6.59

6.33

52

3-PC_19

9.51

6.60

6.24

6.62

6.13

52

3-PC_51

4.37

7.22

7.62

5.36

5.22

56

COM_19

5.17

5.34

5.54

5.31

5.12

60

COM_35

8.92

6.02

6.65

6.26

6.13

61

COM_41

4.24

7.84

7.13

5.16

5.17

61

O

7-BOC_65

46

N H Cl

O

O

O

a

Predictions were obtained with SA models optimized with LOO and K5FCV cross-validations; LOO, leave-one-out cross-validation; c K5FCV, 5-random-groups-out (K-5-Fold) cross-validation. b



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Figure 8. The superimposition between the SB aligned 7-BOC_55 (A), SBC pink; SB aligned 7BOC_3 (B), SBC yellow; SB aligned 7-BOC_65 (C), SBC blue; LB aligned 7-BOC_55 (D), LBC pink; LB aligned 7-BOC_3 (E), LBC yellow; LB aligned 7-BOC_65 (F), LBC blue; and 2V61, EC tan, loaded into the N probe PLS-coefficients contour map and 2V61 Actual Activity Contribution plots (surfaces presented with 65% transparency) within the MAO B active site.


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The Balloon/ShaEP combination was superior in contrast to DOCK in aligning the 7BOC sub-group to 2V61, where 87 molecules adopted similar LB Conformations (LBCs) with the crystallized inhibitor (Supporting Information Table S44, correctly aligned compounds labeled as A). As a consequence, the use of this assembly led to the improved SDEPLOO and SDEPK5FCV values of 1.287 and 1.275, respectively. Yet, with approximately 46% of compounds correctly predicted, it provided virtually no improvement in regard of percentage of success of compounds activity prediction in comparison with the SB protocol. With that finding, both SB DOCK and LB Balloon/ShaEP alignment performances are deemed to be equally suitable to participate in future design of novel MAO B inhibitors. After the CA-AAP paradigm had been confirmed by Balloon/ShaEP couple, it was further used as a sort of filter to set aside the compounds from the discussion that were adequately aligned but incorrectly predicted (Supporting Information Table S44), to define which are the functional groups that should be avoided in future design, and to provide some lead compounds for future modification. Therefore, as many as 8 out of 11 best SB predicted compounds (Supporting Information Table S44), including 7-BOC_41, 7-BOC_43, 7-BOC_47, 7-BOC_50, 7-BOC_53, 7-BOC_55 (Figure 8D), 7-BOC_56, and 7-BOC_58, were also well LB aligned and predicted. Therefore, those hits may serve as a small database for extracting and designing new potential MAO B inhibitors. As within the SB assessment, the Balloon/ShaEP combination was unable to correctly align the voluminous structure of 7-BOC_65 (Figure 8F) which consequently led to misprediction. The disagreement between the SB and LB alignment was observed in 7-BOC_3 where the Balloon/ShaEP procedure almost perfectly aligned 7-BOC_3 to 2V61 (Figure 8E), overcame all the problems faced by the DOCK assessment and raised the prediction result by 2.8 pIC50 units in comparison to the SB prediction.



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The Prediction of 3-AC, 3-PC, and COM Test Sets Activities. The CA-AAP paradigm and the model 13 predictive potential were additionally confirmed by the remaining test set compounds. Details of models’ predictive ability for 3-AC, 3-PC and COM test sets’ predictions are reported as Supporting Information (The Prediction of 3-AC, 3-PC, and COM Test Sets Activities subsection). Rules for Design of Novel MAO B Inhibitors. The N probe SB 3-D QSAR/SAR driven rational design of novel MAO B inhibitors was conducted by modifying 2V61 crystal as a lead. In addition, 2V60 was also considered for modification along with the already specified small data set including 7-BOC_41, 7-BOC_43, 7-BOC_47, 7-BOC_50, 7-BOC_53, 7-BOC_55 (Figure 8D), 7-BOC_56, and 7-BOC_58. Several basic principles were followed: (1) the position C3 of coumarin core of every designed compound was modified by introducing isobutyramide to preserve the pharmacophoric properties of 2-(ethylamino)propanamide, like in 2V5Z crystal; (2) the C4 was saturated with either N-methylethanamine (as in 2V61), acetyl group, N-methylacetamide, N-methylpropionamide, or N,N-dimethylpropionamide; (3) the C3=C4 double bond was improved with 1-methyl-6-oxopiperidine-2-carboxamide ring, or with 2-oxo-1,2,3,4-tetrahydropyrimidine-4-carboxamide ring and its methylated forms; (4) the C5 and C6 coumarin carbons were in different combinations substituted by hydroxyl or methoxy functions; (5) the m-chlorobenzyloxyl moiety at C7 was retained as universal feature as it exists in 2V60 and 2V61 crystals; (6) the phenyl ring of m-chlorobenzyloxyl scaffold was saturated by methyl groups at either o-, m-, or p-position. All of the designed solutions (128 molecules in total) were SB and LB aligned into the MAO B active site by means of DOCK and Ballon/ShaEP, respectively. Further on, high correlation between the best docked and best aligned result was obtained for the 18 structures (Table 11): D116, D120, D121, D123, D124,


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D128 (Figure 9), D28, D45, D56, D57, D65, D83 (Supporting Information Figure S34), and D14, D15, D19, D26, D55, D87 (Supporting Information Figure S35). The SB and LB solutions of D116-D128 (Figure 9), D56 (Supporting Information Figure S34C), and D57 (Figure Supporting Information Figure S34D) were superimposed on 2V61 crystal in an inhibitor-required manner. Derivative D57 is most similar to 2V61, while D56 and D116-D128 are interesting as they bear 1-methyl-6-oxopiperidine-2-carboxamide and 2-oxo1,2,3,4-tetrahydropyrimidine-4-carboxamide systems on coumarin core C3 and C4 positions, respectively. The particular functionalities introduced carbonyl and N-methyl groups in close proximity to Tyr435 while the acetamide nitrogen is pointed towards Gln206. For the clarity of further interpretation, acetamide nitrogen was labeled as N while the N-methyl nitrogens within 2-oxo-1,2,3,4-tetrahydropyrimidine-4-carboxamide

and

3-methyl-6-oxopiperidine-2-

carboxamide systems were labeled as N’ and N’’, respectively (see Supporting Information Synthesized Compounds Spectral Data Interpretation section). To the best of the authors’ knowledge, D56 and D116-D128 are herein presented for the first time as potential MAO B inhibitors. Thus, due to their high SB/LB predicted potential as MAO B inhibitors, derivatives D116-D128 were further on submitted to the synthetic protocol and subsequent evaluation.



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Table 11. Designed Monoamine Oxidase B inhibitors. Code

Ligand structure

Code

D14

D55

D15

D56

D19

D57

D26

D65

D28

D83

D45

D87

D116

D123

Ligand structure

O N

Cl

D121

NH NH2

O

D120

O

O

O

D124

O

D128


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Figure 9. The superimposition between the designed compounds D123 (A), SBC red, LBC blue; D124 (B), SBC pink, LBC grey; D128 (C), SBC purple, LBC yellow; D121 (D), SBC yellow, LBC black; D116 (E), SBC blue, LBC pink; D120 (F), SBC blue, LBC green; and 2V61, EC tan, within the MAO B active site.



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MAO A and MAO B inhibition studies. The pharmacological potential of rationally designed and synthesized compounds D116-D128 (Table 11, Figure 9) to interrupt MAO A or MAO B catalyzed p-tyramine oxidation and consequent formation of H2O2 (Table 12) was evaluated by means of fluorimetric method in vitro. The results are presented in IC50 values (or in pIC50 = -logIC50 values for the purpose of activity prediction by 3-D QSAR) together with the selectivity index (SI hMAO B = [IC50(hMAO A)]/[IC50(hMAO B)]). Enzymatic assays revealed that all of the tested compounds were potent and selective hMAO inhibitors exerting activity in low nanomolar or picomolar concentrations. Thus, the introduction of 3-methyl-2-oxohexahydropyrimidine-4-carboxamide (D123) or 1-methyl-2-oxohexahydropyrimidine-4-carboxamide (D124) scaffolds at C3=C4 double bond of 2V61 coumarin core, along with the insert of common substituents (methoxy group at C6 and the extra m-CH3 within the m-chlorobenzyloxyl backbone), provided for the excellent activity of two designed compounds: D123 IC50 = 830 pM, and D124 IC50 = 970 pM, respectively (Table 12). As for D123 (Figure 9A), the most important factors that induced the remarkable activity in this comound were hydrogen bonds established between the 3-methyl-2-oxohexahydropyrimidine-4carboxamide lactam carbonyl and Tyr435, in the midst of the corresponding system amide group and Gln206, as well as among 6-methoxy group and Cys172. The activity was further enhanced by the N’’-CH3 and m-CH3, involved in the advantageous steric interactions with the recognition area and Ile316, respectively. The activity of D124 (Figure 9B) was reduced by 0.14 nM in comparison with the highest active designed inhibitor, considering that the D124 N’’-CH3 methyl group was transferred closer to Cys172 and the recognition site was deprived of the hydrophobic interactions. The activity of both compounds was accurately predicted by model 13 (AEP < 1): D123 pIC50 = 9.08, LOO SB predicted pIC50 = 9.06, K5FCV SB predicted pIC50 = 9.01, LOO


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LB predicted pIC50 = 9.00, K5FCV LB predicted pIC50 = 9.02; D124 pIC50 = 9.01, LOO SB predicted pIC50 = 8.87, K5FCV SB predicted pIC50 = 8.94, LOO LB predicted pIC50 = 8.99, K5FCV LB predicted pIC50 = 8.91. The high accuracy of prediction is most likely associated with the CA-AAP premise and may be considered as an ideal confirmation of successful design. The activity of remaining designed compounds was in nanomolar range (IC50 = 1.06 – 3.7 nM) and correctly predicted by model 13 as well.



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Table 12. Pharmacological Potential of Compounds D116-D128 and N probe Derived 3-D QSAR Model Structure-Based/Ligand-Based Predictive Ability Comp.

IC50a (nM)

SIb

Anti-MAO B activity prediction SB pred.c LB pred.c pIC50 MAO A MAO B LOOd K5FCVe LOOd K5FCVe *f 3.2±0.32 31.25 8.49 8.51 8.44 8.38 8.31 D116 * 3.7±0.17 37.03 8.43 8.38 8.36 8.26 8.23 D120 * 1.4±0.44 71.43 8.85 8.82 8.79 8.54 8.41 D121 * 0.83±0.11 120.08 9.08 9.06 9.01 9.00 9.02 D123 * 0.97±0.92 103.28 9.01 8.87 8.94 8.99 8.91 D124 * 1.06±0.92 93.33 8.97 8.76 8.66 8.64 8.56 D128 1.83 8.32 8.14 8.06 7.94 7.56 Pargyline 88.34±0.21 48.26±0.49 0.46 8.01 7.94 7.86 7.61 7.43 Clorgyline 43.07±0.92 92.17±0.49 g Comp. Ki Anti-MAO B activity prediction (nM) pKi SB pred.c LB pred.c d e MAO A MAO B LOO K5FCV LOOd K5FCVe *h 0.74±0.18 9.13 8.94 8.61 8.86 8.53 D116 * 0.83±0.12 9.08 8.32 8.26 8.12 8.10 D120 * 0.68±0.14 9.17 8.54 8.32 8.47 9.26 D121 * 0.25±0.09 9.60 9.31 8.82 9.22 9.04 D123 * 0.29±0.11 9.53 9.12 9.06 9.03 8.76 D124 * 0.31±0.09 9.51 8.76 8.68 8.63 8.55 D128 6.21 5.41 5.23 5.33 5.16 Pargyline 91.65±0.41 723.05±0.26 5.20 5.17 4.97 5.06 4.73 Clorgyline 54.36±0.27 624.82±0.83 a Values represent the mean ±SEM of five experiments (n = 5); b Selectivity index: MAO B selectivity ratios [IC50(MAO A)]/[IC50(MAO B)] for inhibitory effect of both designed compounds and reference inhibitors; c Predictions with with either LOO or K5FCV SA optimized models; d LOO, leave-one-out cross-validation; e K5FCV, 5-random-groups-out (K-5-Fold) cross-validation; f Inactive at 100 nM (highest concentration tested); g Values represent the mean ±SEM of five experiments (n = 5); h Inactive at 100 nM (highest concentration tested).

Upon determining the selectivity towards MAO B, the kinetic studies on MAO B inhibitory activity were performed for D116-D128 to reveal the mode of inhibition of designed compounds. Under specific conditions, hMAO B displayed the Michaelis constant (KM) of 22.86 nM and a maximum velocity (Vmax) in the control group of 2.54 (pmol p-tyramine)min-1 (µg protein-1). Having analyzed the Lineweaver-Burk plots of MAO B inhibition after the IC50 64 ACS Paragon Plus Environment

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concentration of D116-D128 had been added to p-tyramine/MAO B system, it was determined that all of the designed inhibitors are competitive MAO B inhibitors (Figure 10). The highest active compound D123 had an inhibition constant value equal to Ki = 0.25 nM (Figure 10A), confirming this hit as a very potent MAO B inhibitor. Slightly less effective was D124 (Figure 10B) with the inhibition constant value of Ki = 0.29 nM. The inhibition constant values of other evaluated compounds were lower than 0.9 nM.

Figure 10. Lineweaver-Burk plot of competitive inhibition of MAO B by compound D123 (A); D124 (B); D128 (C); D121 (D); D116 (E), D120 (F). The aptitude of derivatives D116-D128 to be transferred from blood stream towards the central nervous system was determined by measuring the partition coefficient (log P) as well as



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the passive blood-brain partitioning through the artificial membrane. The blood-brain partitioning ability was determined by collecting the data regarding the retention through the artificial membrane (R, %), effective permeability coefficient (Pe), along with the quantity of compound reaching the acceptor compartment (CA(t)/CD(0), (Supporting information Table S51). Hence, the n-octanol/water partition coefficient values for D116, D120, D121, D123, D124, and D128 ranged from 2.43 to 2.93, suggesting high hydrophobicity and their permeability through the membrane. Regarding the blood-brain barrier penetration, all of the compounds were retained within the artificial membrane in low percentage (R < 15%); the values of permeability coefficients through the hexadecane membrane (log Pe) were relatively high and in good correlation with log P, while a high percentage of compounds reached the acceptor compartment by crossing the membrane. In summary, D116-D128 would readily pass through blood-brain barrier if administered in vivo. CONCLUSIONS In this research, utilizing the procedures available in 3-D QSAutogrid/R protocol,45 SB 3D QSAR models were generated by using both irreversible inhibitors, which were converted to be non-covalent, and reversible co-crystallized inhibitors, in order to describe the MAO B inhibition. Notably, the models were built with the training set compiled of a variety of molecules (aminoindans, aromatic amines, aliphatic amines, aryloxybenzenes, coumarins, thiazolidine-2,4-diones, indoline-2,3-dione, 1,4-diphenyl-2-butene, terpene, and imidazolines), providing quantitative structure-activity relationships based on their wide-ranging molecular diversity. The models’ internal predictive capability was confirmed by applying several crossvalidation techniques, confirming no chance correlation. The best SA optimized model, derived from the interactions between the amide nitrogen (N) probe and inhibitors, outlined the 66 ACS Paragon Plus Environment

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coumarin-based inhibitor deposited at Protein Data Bank under the code name 2V61 as a lead for future design. By deriving complementary information from other probes, a unique SAR-based pharmacophoric model was created to summarize mandatory features for one powerful selective MAO B inhibitor: the compound should be built around the coumarin core which must bear C3 substituent capable of forming an H-bond with Gln206, C4 substituent adequate to create hydrophobic interactions and/or to accept H-bonding from Tyr435, and C7 scaffold, able to open the Ile199, to be attracted by Tyr326 via electrostatic interference, and to preserve contact with Phe103 with electrostatic or limited steric interactions. The SAR model provided a further solution in a rational drug design of new inhibitors. The model was extensively assessed for both alignment and predictive ability, and it was coupled with either SB or LB validated alignment methods. None of the assessed free-toacademia or open source docking/scoring functions was fully acceptable to perform extensive re/cross-docking of experimental or randomized reversible MAO B inhibitors. Based on the highest docking accuracy, DOCK was kept as the best algorithm for docking. In addition, the Ballon/ShaEP assembly performed the required re/cross-alignment with high accuracy and the so-called shaep-onlyshape method was used further on. The validation of the generated N probe 3-D QSAR model was limited to coumarinbased test sets since the most active reversible training set MAO B inhibitor is a member of the particular class. Thereafter, SB and LB procedures of choice, in combination with the N probe 3D QSAR resulting PLS-Coefficients and Actual Activity Contribution Fields, were used to survey the MAO B inhibitors (267 compounds in four test sets) available in literature. Consistent with the internal validation, robustness and lack of chance correlation (cross validations and Yscrambling), the external test sets proved the 3-D QSAR model to be endowed with a medium to



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high level of predictive ability. The medium level may be attributed to a limited number of compounds within the training set, the inevitable limitations of the docking program, or inaccurate activity data of the test set compounds. In addition, the chemical diversity of molecules within the training set made the model suitable for predicting the activity of any compound hitherto known in literature as a possible MAO B inhibitor. Therefore, the model set a course for lead optimization with the aim to discover new and more potent MAO B inhibitors. The gained knowledge was applied in designing 128 novel coumarin-based structures, generated either upon the optimization of 2V61 and similar crystals, or after the modification of SB/LBoutlined test set compounds. The designed molecules were assessed by the SB/LB approach, which provided 18 structures as candidates for synthesis and evaluation. Six of them were made and tested as selective MAO B inhibitors, whereby two compounds D123 (IC50 = 0.83 nM, Ki =0.25 nM) and D124 (IC50 = 0.97 nM, Ki =0.29 nM) distinguished themselves as candidates for preclinical and clinical trials as anti-Parkinson’s drugs. The synthesis and evaluation of the other generated compounds will be discussed in detail in future work.

ASSOCIATED CONTENT Supporting Information. This material is available free of charge via the Internet at http://pubs.acs.org

AUTHOR INFORMATION Corresponding authors: *E-mail: [email protected] *E-mail: [email protected]


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ORCID Alexandros Patsilinakos: 0000-0001-5961-3328 Adele Pirolli: 0000-0002-5550-6098 Manuela Sabatino: 0000-0001-9127-3925

Author Contributions

M.M. and R.R. designed the study, performed most of the experimental work and wrote the manuscript. A.P., A.P., and M.S. contributed to performing the in silico experimental work, the discussion of the results and writing. All authors have given the approval to the final version of the manuscript.

Notes The authors declare no competing financial interest.

ACKNOWLEGMENTS This scientific research was financed by the Ministry of Education, Science and Technological Development, Government of the Republic of Serbia (Grant No. III43004), and supported by two grants from Progetti di Ricerca di Università 2015, Sapienza Università di Roma (C26A15RT82 and C26A15J3BB). M. M. would like to express his gratitude to the Ministry of Education, Science and Technological Development, Government of the Republic of Serbia, for providing fellowship for post-doctoral studies at Sapienza Università di Roma, as well as to Professor Rino Ragno for his kind reception at the Rome Center for Molecular Design (Department of Drug Chemistry and Technologies, Sapienza University of Rome), where the experimental part of the research was conducted.



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DEDICATION M.M. would like to dedicate this paper to retired Professor Dr. Slavica Solujić who introduced him into the world of Medicinal Chemistry.

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