Interactions of 5-deazapteridine Derivatives with Mycobacterium ...

Journal of Biomolecular Structure & Dynamics, ISSN 0739-1102 Volume 22, Issue Number 2, (2004) ©Adenine Press (2004)

Interactions of 5-deazapteridine Derivatives with Mycobacterium tuberculosis and with Human Dihydrofolate Reductases

Elaine F. F. da Cunha1* Teodorico de Castro Ramalho2 Ricardo Bicca de Alencastro1 Elaine Rose Maia3

http://www.jbsdonline.com Abstract There are major differences between the structures of human dihydrofolate reductase (hDHFR) and Mycobacterium tuberculosis dihydrofolate reductase (mtDHFR). These differences may allow us to design more selective mtDHFR inhibitors. In this paper we study the reactions of six different compounds derived from 5-deazapteridine with human and bacterial enzymes. Results suggest that the addition of hydrophobic groups to the aminophenyl ring would increase mtDHFR-inhibitor affinity and selectivity. Key words: Mycobacterium tuberculosis, DHFR, Simulation study, Docking.

1Instituto

de Química da Universidade

Federal do Rio de Janeiro – UFRJ Departamento de Química Orgânica Centro de Tecnologia-Bl A-Sala 609 Ilha do Fundão, Rio de Janeiro CEP 21949-900 - RJ – Brazil 2Departamento

de Química

Instituto Militar de Engenharia Introduction Mycobacterium tuberculosis (MT) is a leading cause of infectious disease in the world today (1). This outlook is aggravated by a growing number of MT infections in individuals who are immunocompromised as a result of HIV infections, particularly in developing countries, eastern Europe and Asia (2, 3). In addition, the emergence of multiple drug resistant (MDR) strains of MT is a serious threat to the control of this disease. A variety of drug therapies for the treatment of tubercular infections has been developed over the last 40 years. Current combination therapies include, among others, rifampicin, fluoroquinolones, isoniazid, ethambutol and streptomycin. Rifampicin inhibits nucleic acid synthesis by inhibiting the elongation of full-length transcripts from RNA polymerase (4). Fluoroquinolones inhibit DNA gyrase, a type II topoisomerase and topoisomerase IV, preventing negative supercoiling of DNA (5). Isoniazid, the oldest and most prescribed antitubercular drug, inhibits mycolic acid biosynthesis. It is a prodrug cleaved by the action of catalase-peroxidase (6). While the specific biochemical target of ethambutol is unknown, several studies have implicated this drug in the inhibition of cell wall biosynthesis (7). Streptomycin inhibits protein synthesis by affecting the accuracy and speed of mRNA translation (8). In our study, dihydrofolate reductase (DHFR) was selected as a target enzyme to combat M. tuberculosis. This enzyme catalyzes the NADPH-dependent reduction of dihydrofolate to tetrahydrofolate, thus compromising a variety of biochemical functions involving single-carbon transfers. The reduced form of the folate is a precursor of the cofactors necessary for the synthesis of thymidylate, purine nucleotides, methionine, serine, and glycine, required for DNA, RNA and protein synthesis. DHFR is the major target in the development of drugs against several diseases, e.g., cancer and bacterial and parasitic infections (9). Methotrexate (MTX) was the first DHFR inhibitor to be synthesized. However, it is toxic (mainly in the bone marrow) and presents resistance problems, factors which correlate to

Praça General Tibúrcio 80 Urca, Rio de Janeiro CEP 22290-270 - RJ – Brazil 3LEEM

Físico-Química

Instituto de Química Universidade de Brasília. Cidade Universitária Campus Darcy Ribeiro, Brasília CEP 70919-970 - DF – Brazil

*Fax:

+55-21-2562-7132 Email: [email protected]

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120 da Cunha et al.

its absorption and accumulation in the cell (10). Another problem with MTX is its low selectivity, which leads to the indiscriminate inhibition of parasite and host enzymes. A sequence alignment of hDHFR and mtDHFR indicated only ∼26% of sequence identity, and hence there were, at the onset of this study, good prospects for observing significant differences in crucial regions between mycobacterial and host enzymes. A structural comparison of hDFHR and mtDHFR reveals key differences in the active sites, which may lead to quite specific suggestions for the design of new selective inhibitors of mtDHFR (11). Suling et al. reported a set of 2,4-diamino-5-methyl-5-deazapteridine (DMDP) derivatives, with structures analogous to the trimetrexate/piritrexin class of antifolates, as potential antimycobacterial agents to be targeted for treatment of Mycobacterium tuberculosis (strain H37Rv) (12). Recently, they reported the synthesis and the 50% inhibitory concentrations evaluation (IC50) of a series of news potent and selective DMDP derivatives for Mycobacterium avium complex (MAC) and human DHFR (13). Molecular mechanics based methods involving docking studies and also molecular dynamics simulations are suitable tools to adjust ligands at target sites and to estimate interaction energy (affinity) (14). Nowadays, that is a well-established technique applied to numerous cases (15-17). In this study, we used molecular-mechanics–based methods, involving docking studies and molecular dynamics simulations, to study the binding orientations and predict binding affinities. Such studies have been carried out to understand the forms of interaction of six compounds, developed by Suling et al. (Figure 1), with two DHFR enzymes, the Mycobacterium tuberculosis and the human enzyme. Significant differences between the two active sites will be analyzed to propose structural changes in these compounds, with the aim of rendering them more selective and thereby better antituberculosis agents. CH3 NH2

CH3

N H2N

NH2 N H

N

OMe

N

CH3

N CH3

1

H2N

N H N

N

CH3 NH2

Figure 1: Structures of the 5-deazapteridine derivatives studied (DMDP). H2N

NH2 N H

N

OMe

CH3

N N

CH3 N H

N OMe H2N

3

N

N

OCH NH2

CH3 N H

N H2N

N

N

5

CH3

N OCH2CH3

OMe

4

OCH2CH3 NH2

CH3

2

H2N

N H N

N

OCH

6

Methods Molecular-modeling calculations were performed on a Silicon Graphics O2 workstation (Silicon Graphics Inc., USA). Model building, visualization and the docking manipulation of the structures were performed using the Accelrys’ software package Insight II 97.0 (18). Molecular mechanics and molecular dynamics calculations were carried out with DISCOVER 2.9.7 (19), using the CVFF force field (20).

System Relaxation Strategy Crystal coordinates of mtDHFR and hDHFR enzymes, the cofactor NADPH, inhibitors, and the crystallographic water molecules were taken from the Brookhaven Protein Data Bank (PDB codes: 1DF7 and 1OHJ respectively) (11,21). The mtDHFR enzyme is complexed with the methotrexate (MTX) inhibitor, and the hDHFR enzyme is complexed with the n-(4-carboxy-4-{4-[(2,4-diaminopteridin-6-ylmethyl)-amino]-benzoylamino}-butyl)-phthalamic acid (COP) inhibitor. The 1DF7 and 1OHJ systems were chosen because the crystallographic structures of the inhibitors are similar to the compounds in the data set used in this study, except for the substituents in the para-aminophenyl position. In order to relax these crystallographic systems, all hydrogen atoms were explicitly included in the system. Atomic coordinates were then minimized by the following protocol, to which constraints and restraints (22) were added in order to gain better control over relaxation. This protocol was defined by four successive steps: (i) to eliminate the initial strains, only hydrogen atoms were allowed to move, while the heavy atoms were kept fixed; (ii) for the next adjustments, the side-chains, the water molecules, the cofactor NADPh and the inhibitor were tethered restrained, keeping the mainchain atoms fixed; (iii) the tethering constant for the backbone atoms was gradually decreased; and (iv) strain was minimized until the system was completely relaxed. One hundred steepest descent minimization steps were carried out, followed by the conjugate gradient minimization method, until the derivatives were of the order of 5.0, 1.0, 0.1 and 0.05 kJ⋅mol-1⋅A-1, respectively. To prevent the evaporation of water molecules, all minimization steps were performed using a halfharmonic restraining potential of 0.5 kJ⋅mol-1⋅Å-2. Ligands Data Set A data set of six 2,4-diamino-5-methyl-5-deazapteridine derivatives (Figure 1) was taken from published results (12,13) and the activity against Mycobacterium tuberculosis H37Rv, Mycobacterium avium and human DHFR are reported in Table I. Table I Activity Data of DMDP Derivatives* against mtDHFR, MAC DHFR and hDHFR.

*

Compounds

MIC (mg/L) H37Rv

1 2 3 4 5 6

ND 6.25 6.25 12.5 > 12.5 ND

IC50 (nM) MAC DHFR Human DHFR 0.92 57 0.86 300 0.40 150 1.1 1,000 0.84 2,300 1.0 7,300

The compound structures (1 - 6) are shown in Figure 1.

Three-dimensional (3D) structures of each of the six compounds (Figure 1) were based on the structure of the methotrexate (MTX) co-crystallized with mtDHFR, retrieved from the Protein Data Bank (23). This structure is the conformation bound to the enzyme active site or the bioactive conformation of the MTX. The 3D models were built using the Insight II/Builder module, using the coordinates to the conformation bound structure of MTX. Subsequently, the overall geometry optimizations of the ligands were performed with the Discover program using the CVFF force field, as previously reported (19, 20). The minimizations were carried out by the conjugate gradient algorithm until the maximum derivative was less than 0.05 kJ⋅mol-1⋅A-1. Resultant conformations were submitted to the AM1 semi-empirical molecular orbital method (24), from the AMPAC/MOPAC 6.0 release, interfaced with Insight II, to re-calculate the partial atomic charge distribution. A strategy combining dynamics and energy minimization was defined to explore the conformational space of all compounds. A systematic search was performed using the ROTOR constraints

121 Interactions of DMDP Derivatives with mtDHFR & hDHFR

122 da Cunha et al.

provided in the Discover program by rotating the single dihedral bonds by 30º steps from 0º to 360º. After each rotation, the systems were held at equilibrium for 2 ps, at 300K, with a time step of 1fs. Then, the last fluctuations generated were minimized by 100 iterations using conjugate gradients until the maximum derivative were less than 0.5 kJ⋅mol-1⋅A-1. The low-energy conformers resulting from that loop strategy were minimized again to a convergence of 0.05 kJ⋅mol-1⋅A-1, and the structures were employed for docking analysis (25). The approach employed herein is a modified version of the procedure developed by Hagler, Scheraga and co-workers (22, 26-28). Docking and Molecular Dynamics Calculation Procedures The compounds were initially docked into the mtDHFR and hDHFR binding site and the Insight II Docking module performed the automatic pharmacophoric alignments of the compounds. The GRID-based method of calculating molecular interaction fields was then used to pre-compute and save the information necessary for force field scoring into a grid file. This scoring function approximates molecular mechanics interaction energies and consists of van der Waals and electrostatic components (29). Evaluation of docking results was based on steric and electrostatic complementarity (30). This may be called the best fit of the ligand to the enzyme pocket. At this point, three primary factors are known to influence the conformation of a ligand bonded to a protein: hydrogen bonding, binding energies and hydrophobic-hydrophobic interactions (30). It is possible to identify quantitatively the existence of hydrogen bonding in the docked conformation with the Docking tools. However, this program does not quantify the hydrophobic-hydrophobic interactions. It must be pointed out that in the docking methodology, the total intermolecular energy between two molecules (receptor/ligand) is computed by the non-bonded terms of a force field equation. The equation on the CVFF force field is shown below [Eq 1], where the first two terms are the 6-12 Lennard-Jones potentials and the last one is the Coulomb potential (23).

Einter =

ΣΣ [( r 12ij A ij

-

Bij rij6

) + (

qi qj εrij

)]

[1]

The DHFR-inhibitor complexes resulting from the docking calculation were solvated with five shells of water molecules. Harmonic radial forces (6.3 kJ⋅mol-1⋅Å-2) were applied to avoid evaporation. Prior to energy-minimization of the complexes, only the water molecules were energy-minimized and then submitted to a 100 ps of molecular dynamics at 300 K, in for the solvent to equilibrate around the enzyme-inhibitor structure. Then, conjugate gradient minimizations were performed until the rms gradient of the potential energy fell below 0.05 kJ⋅mol-1 Å-1. A cut-off of 10 Å was used to calculate non-bonded electrostatic interactions at every minimization step. An active approach was used for the following ligand-protein simulations. The enzyme was partitioned into one static subset region in which the atoms were not allowed to move (Sst), and another dynamic subset region composed of all amino acid residues having at least one atom within 12 Å of the center of the ligand (Sdyn). In order to avoid abrupt conformational changes, the system was equilibrated at 300 K, observing an equilibration phase during 50 ps. A dynamic trajectory was then collected over 500 ps. Atomic coordinates were saved every 2.2 ps. Finally, the structures resulting from the dynamics trajectory were selected on the basis of RMSD and re-optimized using the conjugate gradient method, until the maximum derivative was less than 0.05 kJ⋅mol-1⋅Å-1. Results Gokhale and Kulkarni (31) performed docking and molecular dynamics simulation of diaminoquinazoline derivatives with Candida albicans and human DHFR to

understand the basis for selectivity of these agents. The analysis showed that the fungal DHFR active site is flexible and can accommodate bulky groups on a thioaryl ring, and that the human enzyme active site was very rigid. Further, Kharkar and Kulkarni (32) recently reported a docking study between deazapteridine inhibitors and Mycobacterium avium complex dihydrofolate reductase. However, there are still many obscure points about the inhibitory potency of the Mycobacterium tuberculosis DHFR inhibitors and their selectivity with human DHFR. Therefore, considering the emergence of bacterial resistance, we applied docking and molecular dynamics techniques on a set of six compounds, with the human and bacterial DHFR enzymes.


Docking of 5-deazapteridines Ligands into the Binding Site on the Target Enzymes The initial position and conformation of the ligand in the active site can have an effect on the final model generated (33). Following a search of the conformational space of all the compounds by rotating the single bonds, the low-energy conformers were minimized and the calculated structures were employed for docking. Two conformations were selected for each compound, resulting in 12 structures for the dock study in the enzymes: 1A, 1B, 2A, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6A, and 6B. The conformations “A” and “B” are showing in Figure 2. R1 9 NH2

R2

CH3

N 3 H2N

10

6 12 N 13 14 H 1

N

N

16

NH2

11

5

R1

CH3 N H

N

7

8

H2N

N

N

Figure 2: Two conformations obtained for each inhibitor: A (left) and B (right).

R2

The six compounds, with two conformations each, were docked in the active site of the mtDHFR and hDHFR enzymes. DHFR enzyme has been shown to exhibit two modes of substrate (dihydrofalate) binding (34). So that, the orientations with the best intermolecular energy (van der Waals + electrostatic) were obtained and the accepted ligand/enzyme models were then subjected to molecular dynamics simulation. The interactions of the compounds in the “A” conformation with hDHFR and mtDHFR were energetically unfavorable. It was observed that an increase in the number of carbon atoms in the ortho position increases steric hindrance, because these substituents are oriented to the interior of the active site. In conformation “B,” the two substituents are oriented to the surface of the active site, thus reducing steric hindrance. Then, molecular dynamics simulation was performed only between 6 compounds in the conformation “B” and the enzymes. DHFRs-binding Sites The quality of the models generated from the MD simulations of the enzyme/ligand complexes was evaluated according to the following parameters: i) hydrogen bond interactions; ii) other interactions such as π-π and cation-π stacking; iii) hydrophobic interactions; iv) energy of molecular interactions between enzyme and inhibitor; v) rms deviations of the active sites residues between mtDHFR and hDHFR enzymes; and vi) orientation of the compounds within the active site. Compound 1 possesses two methyl groups at the R1 and R2 position on the aminophenyl ring and it is the most potent of six compounds for hDHFR, with an IC50 (nm) value of 57 (Table I). After dynamics simulations two hydrogen bonds with the human enzyme can be observed in the 2,4-aminopteridine ring (Table II and Figure 3): Ile7 forms a bond with N9 and Glu30 with N10. In the aminophenyl ring one hydrogen bond is observed between N12 and a water molecule. There are three residues, namely Leu23, Phe31, and Ile60, potentially capable of providing specific van der Waals interactions, stabilizing the complex between hDHFR and inhibitor 1.

124 da Cunha et al.

Figure 3: Six compounds docked into the active site of human DHFR. The residues shown are involved in H-bonding.

Figure 4: Six compounds docked into the active site of Mycobacterium tuberculosis DHFR. The residues shown are involved in H-bonding.

Figure 5: Solvent-accessible surface of the mtDHFR (left) and hDHFR (right) enzymes complexed with compound 6. The substituents (ortho- and meta-) are oriented to the surface of the active site. Red and blue colors correspond to negative and positive electrostatic potential values, respectively.

Table II Energies (kJ⋅mol-1) and distances (Å) values of the hydrogen bond data for compounds ( 1 - 6 ) interacting with human dihydrofolate reductase. Compounds Energy N9-Ile7 Distance N9-Ile7 Energy N10-Glu30

*

1 -23.49 2.07 -28.51

2 -23.78 1.95 -31.02

Distance

N10-Glu30

1.93

2.08

Energy Distance Energy Distance * Energy * Distance

N9-Val115 N9-Val115 N12-H2O N12-H2O O-H2O O-H2O

-5.02 2.46 -

-17.26 2.37 -8.07 2.29 -

3 -23.24 2.10 -25.29 -25.29 2.07 2.50 -8.07 2.29

4 -22.11 2.17 -34.53

5 -23.24 2.10 -32.02

6 -23.66 2.05 -29.34

1.87

1.91

1.88

-16.38 2.41 -

-16.43 2.35 -

-19.10 2.41 -8.40 2.39 -12.79 1.83

The ortho- or meta-oxygen of the aminophenyl ring.

Compound 1 interacts with the mtDHFR active site (Table III and Figure 4) through the 2,4-aminopteridine ring forming four hydrogen bonds: one between Ile5 and N9, another between N8 and a water molecule. Asp27 interacts with N10 and a water molecule, contributing with intermolecular energy of -22.69 kJ⋅mol-1 and -9.07 kJ⋅mol-1, respectively. In addition, the methyl groups of the aminophenyl ring accommodate in the active site around two residues, namely Gln28, and Pro51, probably forming hydrophobic interactions. Table III Energies (kJ⋅mol-1) and distances (Å) values of the hydrogen bond data for compounds (1 - 6 ) interacting with mtDHFR. Compounds Energy N9-Ile5 Distance N9-Ile5 Energy N10-Asp27 Distance N10-Asp27 Energy N8-H2O Distance N8-H2O Energy N10-H2O Distance N10-H2O Energy N12-H2O Distance N12-H2O * Energy O-H2O * Distance O-H2O *

1 -23.83 2.02 -22.69 1.98 -3.637 2.41 -9.07 2.22 -

2 -25.45 2.00 -19.60 2.02 -18.35 2.00

3 -25.75 1.95 -15.50 1.99 -9.49 2.20 -7.27 2.04 -

4 -25.83 1.96 -19.02 2.13 -17.68 2.44 -7.73 2.37 -8.53 2.49 -12.92 1.98

5 -26.04 1.94 -14.46 1.99 -4.43 2.40 -4.43 2.40 -

6 -23.74 1.97 -22.15 2.00 -7.06 2.25 -13.17 1.97

The ortho- or meta-oxygen of the aminophenyl ring.

Compound 2 possesses one methyl group at the R2 position and one methoxyl group at the R1 position on the aminophenyl ring, and after dynamics simulations three hydrogen bonds with the hDHFR can be observed in the 2,4-aminopteridine ring (Table II and Figure 3): Ile7 with N9, Glu30 with N10 and Val115 with N19. In the aminophenyl ring one hydrogen bond is observed between N12 and a water molecule. The addition of a methoxyl group indicated the loss of hydrophobic interactions with the residues of the hydrophobic pocket, resulting in lower binding affinity with hDHFR. In the case of mtDHFR, inhibitor 2 interacts with the active site (Table III and Figure 4) through the 2,4-aminopteridine ring, forming two hydrogen bonds: one between Ile5 and N9, and another between Asp27 and N10. In addition, a hydrogen bond was observed between the ortho-oxygen on the aminophenyl ring and one water molecule. Compound 3 is essentially similar to compound 2, the only change being the methyl group at the R2 position and one methoxyl group at the R1 position on the aminophenyl ring. However its IC50 value, for the hDHFR, is the half of the meet in compound 2 and its position in the active site is different. Three hydrogen bonds


126 da Cunha et al.

can be observed between the 2,4-aminopteridine ring and the human enzyme (Table II and Figure 3): one between Ile7 and N9, and two between Glu30 and N10. In the aminophenyl ring, one hydrogen bond is observed between ortho-oxygen and a water molecule. For mtDHFR, inhibitor 3 has the same MIC values as compound 2, and interacts with the active site (Table III and Figure 4) through the 2,4aminopteridine ring, forming three hydrogen bonds: one between Ile5 and N9, one between N10 and a water molecule, and another between Asp27 and N10. In addition, a hydrogen bond was observed between N12 and a water molecule. Compound 4, with one methoxyl group at the R1 and R2 positions on the aminophenyl ring, shows poor inhibitory potency for hDHFR (Table I). The interaction of compound 4 and the human enzyme, after dynamics simulations, results in three hydrogen bonds which can be observed in the 2,4-aminopteridine ring (Table II and Figure 3): Ile7 with N9, Glu30 with N10 and Val115 with N19. With mtDHFR, compound 4 interacts to form four hydrogen bonds (Table III and Figure 4): Ile5 with N9, Asp27 with N10, and N8 and N9 with water. In addition, a hydrogen bond was observed between N12 and a water molecule and another between ortho-oxygen and water molecule. Compound 5 shows one ethoxyl group at the R1 and R2 position on the aminophenyl ring. The incorporation of the bulk substituent decreases the potency for hDHRF. Compound 5 forms three hydrogen bonds with the human enzyme which can be observed in the 2,4-aminopteridine ring (Table II and Figure 3): Ile7 with N9, Glu30 with N10 and Val115 with N19. Interaction of compound 5 with mtDHFR shows four hydrogen bonds within the 2,4-aminopteridine ring (Table III and Figure 4): Ile5 with N9, Asp27 with N10, one water molecule with N10, and one water molecule with N8. Compound 6 possesses one propanoxyl group at the R1 position and other at the R2 position on the aminophenyl ring, Docking followed by molecular dynamics simulations results in an active site orientation which shows five hydrogen bonds with the human enzyme. The model shows three hydrogen bonds in the 2,4-aminopteridine ring (Table II and Figure 3): Ile7 with N9, Glu30 with N10 and Val115 with N19, and two in the aminophenyl ring: one water molecule with N12, and the other with the oxygen of the ortho- position. On the other hand, compound 6 interacts with the mtDHFR active site (Table III and Figure 4) through the 2,4-aminopteridine ring, forming three hydrogen bonds: one between Ile5 and N9, one between N10 and a water molecule, and another between Asp27 and N10. In addition, a hydrogen bond was observed between the ortho-oxygen atom of the aminophenyl ring and a water molecule. In addition, the same kinds of calculations were performed for MTX with both proteins. The 2,4-diaminopteridine ring of MTX is situated in a deep cleft and interacts strongly with both enzymes. After dynamics simulations, three hydrogen bonds with the human enzyme were observed in the 2,4-aminopteridine ring: Ile7 with N9, Glu30 with N10 and Val115 with N19. In the case of mtDHFR, MTX interacts forming four hydrogen bonds: Ile5 with N9, Asp27 with N10, and N8 and N9 with water. It confirmed that MTX interacts with both proteins through hydrophobic interactions. Discussion In the complexes formed between the six compounds and mtDHFR, the space occupied by the 2,4-deazapteridine ring (N1, N8 and C7, Figure 2) is accessible to the solvent (Figure 5). In contrast, in the complexes formed by the compounds and hDHFR, the space is well packed with three hydrophobic residues: Leu-22, Pro-26 and Phe-31, which restrict the accessibility of the solvent. In mtDHFR, these residues correspond to Leu-20, Arg-23 and Gln-38. Additional groups connected to N1, N8 or C7 may contribute with steric or chemical effects in the formation of the inhibitor/hDHFR complex.

It is evident from Table IV that the difference between intermolecular energies of mtDHFR and hDHFR enzymes increases from 1 to 6. The energy difference of intermolecular interaction between compound 1 and the human enzyme is very similar to that calculated for the bacterial enzyme. There are also no significant energetic differences in the interaction energy between inhibitor 2 and both enzymes. Except for compounds 5 and 6, the main contribution for this difference is apparently electrostatic in origin. It is notable that the variation of intermolecular energy between the two enzymes increases with the increase in the number of methyls in the inhibitor’s ortho- and/or meta- positions. This suggests that the addition of hydrophobic and bulky substituents to the aminophenyl ring should increase the affinity and selectivity of the mtDHFR inhibitors.


Table IV Interaction energies between inhibitors and mtDHFR and hDHFR enzymes in the studied orientation bounds. vdW energies (kJ⋅mol-1)

1 2 3 4 5 6 *

Electrostatic energies (kJ⋅mol-1)

rms (Å)

Intermolecular energies (kJ⋅mol-1)

hDHFR

mtDHFR

∆E*

hDHFR

mtDHFR

∆E

hDHFR

mtDHFR

∆E

hDHFR

mtDHFR

-335.60 -331.26 -337.20 -333.89 -382.76 -392.29

-331.22 -337.70 -343.93 -350.99 -375.57 -396.89

4.39 6.44 6.73 17.09 7.18 4.59

-53.42 -83.89 -60.27 -79.34 -45.69 -65.71

-57.73 -79.34 -68.26 -77.455 -70.05 -82.26

4.30 4.56 7.98 1.88 24.37 16.55

-389.03 -415.16 -397.48 -413.28 -428.49 -458.01

-388.95 -417.04 -412.19 -428.49 -445.63 -479.15

0.08 1.88 14.71 15.21 17.14 21.15

0.28 0.25 0.24 0.26 0.37 0.26

0.59 0.55 0.63 0.60 0.48 0.63

∆E is the energy difference of complexation among the six compounds and the hDHFR and mtDHFR enzymes.

The effect of the docked compound over the amino acid side chains contained on the dynamics subset (Sdyn) mentioned in Docking and Molecular Dynamics Calculation Procedures above was analyzed by calculating the rms superposition between the heavy atoms before and after dynamics calculations (Table IV). The results show that inhibitor interactions with the active site of hDHFR did not result in a significant displacement of residues with the rms varying between 0.24 and 0.36 Å. The effect was more pronounced for Ile7 and Glu30. On the other hand, for mtDHFR the displacement was larger, with the rms between 0.48 and 0.63 Å. The main differences were in Ile5 and Asp27. Summarizing, the nature of the active site residues and their effects in the orientation of the compounds had larger influences in mtDHFR than in hDHFR. The 6 compounds in the conformation “B” docking in the hDHFR enzyme have the dihedral angle of the inhibitor (C6-C11-N12-C13, Figure 2) varying between 51.45º and 56.29º (Figure 6) after dynamics simulation. This happens because the deazapteridine ring interacts with amino acid Phe34, probably forming π-π stacking interactions, with the distances varying from 3.63 Å to 4.56 Å, and the aminophenyl ring with amino acid Phe31, with the distances varying from 4.21 Å to 4.45 Å for Phe31 (the distance values were calculated between the pseudo-atoms positioned in the center of the rings). On the other hand, in mtDHFR, the same

Figure 6: The left figure shows the docking of the compound 6 to the active site of human, and, on the right, the Mycobacterium tuberculosis enzymes. The arrows indicate π-π stacking or cation-π interactions. The circle is indicating the dihedral angle C6-C11-N12C13 (see Figure 2).

128 da Cunha et al.

dihedral angle varies between 63.65º and 73.69º. This happens because the deazapteridine ring interacts with the amino acid Phe31, probably forming π-π stacking interactions, with the distances varying from 4.00 Å to 4.58 Å, and the aminophenyl ring interacts with Gln28, probably forming cation-π interactions, with the distances varying from 3.59 Å to 4.75 Å for Phe-34. In the ternary complex of MTX with mtDHFR, the side of the aminopterin ring facing loop is accessible to the solvent. There is a water molecule in a depression nearby. This water molecule interacts with Trp22, Asp27 and Gln28, which form a pocket in mtDHFR. In contrast, in hDHFR complexes containing folate or MTX, there are three residues – Leu20, Arg23 and Gln28 – adjacent to the N8 and C7 of the inhibitor, restricting the accessibility of MTX to the solvent, with the distance between the side-chains of Leu22 and Phe31 being about 3.8 Å. These two residues in the human enzyme correspond to residues Leu20 and Gln28 in mtDHFR, where the shortest distance between side-chain atoms is about 6.5 to 9.4 Å. Most of the active site residues interacting with the deazapteridines were the same as those interacting with the methotrexate. Our results confirm what is well know from literature (35, 36), that is, the arrangement of three or four H-bonds around the aminopteridine ring is highly conserved in known DHFR structures from various species and it is not a key point for selectivity. Turning now to the NADP position, one can see that the nicotinamide ring is almost parallel to the deazapteridine ring in all the complexes between the six compounds and hDHFR (Figure 6), probably forming π-π stacking interactions (the distances between the center of the rings vary from 4.19 to 4.64 Å). However, this is not observed in any of the mtDHFR inhibitor complexes (the distances between the center of the nicotinamide and deazapteridine rings vary between 5.80 and 6.12 Å), indicating probably that the compounds are not interacting with NADP as they did before the dynamics simulation. It is not clear at this point if this is relevant to the inhibition mechanism. Finally, minimal inhibitory concentration (MIC) of each ligand with the mtDHFR enzyme was compared on the basis of interaction energy of each ligand with the amino acid residues of the active site. According to Kumar & Kulkarni, the interactions of each ligand with the amino acid residues should govern the ability of that ligand to selectively inhibit the enzyme, while the differences in these interaction energies reflect the differences in the inhibitory potency and selectivity of the ligands (21). Thus, in Table I, we observed that compounds 2 and 3 have the same MIC values (6.0 mg/L), compound 4 has twice the value of previous compounds, and compound 5 has a MIC value greater than 12.5 mg/L for strain H37Rv. The MIC values increased with the increase in the number of carbons in the ortho- and meta- positions of the aminopteridine ring. Our calculations of interaction energies for Mycobacterium tuberculosis (Table IV) show a good agreement with these relationships. Exploring the fact that there is in mtDHFR larger accessible cavity to the solvent, we suggest that either the groups connected to N1, N8 or C7 or the bulky substituents in the ortho- or meta- positions of the aminophenyl ring will have the greatest potential impact on selectivity. This is reflected, for instance, in the MIC (Table I) and energy of complexation (Table IV) differences for inhibitor 6, whose aminophenyl ring-substitution possesses one propanoxyl group at R1 and R2 positions. In hDHFR, compounds that contain bulky substituents at R1 and R2 positions larger than a methyl group would probably produce significant steric clashes with the residues lining the hydrophobic pocket (Leu-22, Pro-26 and Phe-31), substantially modifying the docked ligand conformation and lowering the binding affinity, as in compound 4. Conclusions We combined docking and molecular dynamic studies to understand the inhibitorprotein interactions of a series of 2,4-diamino-5-methyl-5-deazapteridine deriva-

tives with the structures of human dihydrofolate reductase (hDHFR) and of Mycobacterium tuberculosis dihydrofolate reductase (mtDHFR). The six compounds are similar, with substituents in R1 and R2 positions varying with methyl, methoxyl, ethoxyl, and propanoxyl groups. Different terms were calculated and analyzed, such as intermolecular energy, hydrophobic and electrostatic interactions and hydrogen bonding interactions. We observed differences of great significance between the two active sites, which may be used to propose new selective inhibitors for mtDHFR. In mtDHFR, compounds 1, 4 and 5 interacted with the mtDHFR active site forming four hydrogen bonds, compounds 3 and 6 had three interactions and compound 2 only two interactions with 2,4-aminopteridine. In addition, a hydrogen bond was observed between N12 and a water molecule in inhibitors 3 and 4, and another hydrogen bond was observed involving the ortho-oxygens in compounds 2, 4 and 6 and water molecules. Two hydrogen bonds between compound 1 and the human enzyme were observed in the 2,4-aminopteridine ring, while in compound 2, 3, 4, and 5 there were three hydrogen bonds. The N12 nitrogen in inhibitors 1 and 2 interacted with a water molecule. In inhibitor 6, two hydrogen bonds with water molecules were observed, one with N12, and the other with the oxygen in the ortho- position. Finally, the differences in intermolecular energies of complexation for the six compounds with the hDHFR and the mtDHFR enzymes increased from compound 1 to 6, suggesting that the addition of hydrophobic and bulky substituents to the aminophenyl ring should increase the affinity and selectivity of the mtDHFR inhibitors. In addition, since the space occupied by the 2,4-deazapteridine ring is accessible to the solvent in mtDHFR but not in the hDHFR, then groups connected to N1, N8 or C7 atoms of the bacterial enzyme can increase the selectivity of this compound for Mycobacterium tuberculosis DHFR. Considering that the emergence of resistant strains of MT poses a serious threat to the control of this disease, we have selected mtDHFR as a possible target to combat MT. We strongly feel that this study can be helpful in understanding the molecular interactions and the structural factors responsible for selectivity of DHFR inhibitors. The selectivity of inhibitors plays an important role during the clinical application of these agents as antibacterials. Results of this selectivity analysis can be used to design of drugs with no inhibitory effect on the human enzyme and, consequently, fewer side effects on human host. The present study attempts to address this important question of DHFR selectivity and to contribute to the design of new and more selective drugs against Mycobacterium tuberculosis. Acknowledgments We are grateful to Brazilian agencies CAPES, FAPERJ, FUJB and PRONEX / CNPq nb. (661028/1998-4), for funding part of this work. We are especially grateful to Prof. Peter Bakuzis for his comments and English corrections on the manuscript. References and Footnotes 1. J. S. Blanchard. Annu. Rev. Biochem. 65, 215-241 (1996). 2. B. Roth and D. K. Stammers. The Design of Drugs to Macromolecular Targets. Ed., Beddell, C. R. John Wiley & Sons Ltd., New York (1992). 3. M. C. Raviglione, D. E. Zinder and A. Kochi. J. Am. Med. Assoc. 273, 220-245 (1995). 4. M. E. Levin and G. F. Hatfull. Molec. Microb. 8, 277-287 (1993). 5. J. C. Wang. J. Biol. Chem. 266, 6659-6667 (1991). 6. M. Nguyen, A. Quemard, H. Marrakchi, B. Jean and C. R. B. Meunier. Acad. Sci. Paris. Série Lic. Chimie/Chemistry 4, 35-47 (2001). 7. A. E. Belanger, G. S. Besra, M. E. Ford, K. Mikusová, J. T. Belisle, P. J. Brennan and J. M. Inamine. Proc. Nat. Acad. Sci. USA 93, 11919-11926 (1996). 8. R. Benvenist and J. Davies. Annu. Rev. Biochem. 42, 471-484 (1973). 9. S. C. Meyer, S. K. Majumder, and M. H. Cynamon. Antimicrob. Agents Chemother. 42, 1862-1869 (1995).


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Date Received: February 27, 2004

Communicated by the Editor Ramaswamy H Sarma