Artigo docking Zn BDC 2012

Metal Organic Frameworks for Drug Delivery andEnvironmental Remediation: A Molecular DockingApproach

Marcelo O. Rodrigues,y Marcos V. de Paula, Kaline A. Wanderley, Iane B. Vasconcelos,Severino Alves, Jr., and Thereza A. Soares*

Metal organic frameworks (MOFs) are promising materials

for the controlled release of drugs. Molecular docking

methods have been successfully applied to the fast

screening of lead compounds starting from the three-

dimensional structure of proteins. We apply molecular

docking calculations to MOFs as a fast approach to

distinguish between good and poor drug candidates for

incorporation. The approach can predict the binding

behavior of different guest molecules in agreement to

experimental measurements using X-ray powder diffraction,

thermogravimetry–differential thermal analysis, and UV–vis

spectroscopy techniques. It can also identify the overall

binding mode of tested compounds and estimate binding

affinity differences above the error of 2 kcal mol�1 or 8.4 kJ

mol�1 associated with the empirical scoring function used

in the calculations. This exploratory investigation indicates

that the molecular docking technique may be useful in the

fast screening of drug candidates for adsorption to

coordination polymers for controlled drug delivery and/or

environmental remediation. VC 2012 Wiley Periodicals, Inc.

DOI: 10.1002/qua.24211

Introduction

Metal-organic frameworks (MOFs) are a novel class of crystal-

line materials constructed by the means of self-assemblage of

metal ions or their clusters interconnected by multifunctional

organic ligands.[1–6] The wide interest in these materials has

been motivated by the remarkable number of potential frame-

work constructions as well as promising applications in gas

technology,[7,8] catalysis,[9,10] drug delivery,[11–13] luminescent

materials,[14] sensors,[15–18] and stationary phase for chroma-

tography.[19–21] In particular, MOFs containing Zn2þ cations

and 1,4-benzenedicarboxylate (1,4-BDC2�) residues as linkers

have been extensively studied through the pioneering contri-

butions of Yaghi and coworkers.[22–25] The compound 1,4-BDC

plays an important role in the three-dimensional arrangement

of MOFs for it bridges metal centers that can adopt different

coordination modes to form structures in 1D, 2D, and 3D (Fig.

1).[26–28] Depending on the resulting structure, the MOF skele-

ton can be flexible, combining a high cavity volume, regular

crystallinity, and the presence of tunable organic groups

within the framework. These properties allow an easy modula-

tion of the size of the layers, enabling a variety of specific

applications.[29] [Zn(BDC)(H2O)2]n stands out among 1,4-BDC-

derived MOFs due to the fact that its supramolecular structure

forms 1-D polymer chains interconnected by p–p stacking and

a series of hydrogen bond interactions, whose layer sizes can

in principle be modulated on adsorption of other molecules.

MOFs exhibit many desired characteristics as drug carriers,

including exceptionally high surface areas and large cavity

sizes for drug encapsulation, intrinsic biodegradability as a

result of relatively labile metal–ligand bonds, and versatile

functionality for postsynthetic grafting of drug molecules.[13,30]

These materials are made of hydrophilic and hydrophobic enti-

ties with manifold possibilities of pore sizes and connectivities

that can be adapted to the physical–chemical properties of

each drug. Despite its recognition as potential drug car-

riers,[11–13,31] little attention has been paid to the characteriza-

tion of MOF–drug interactions at the atomic resolution and its

impact on controlled drug liberation. In that regard, the ability

to predict the binding conformation and binding affinity of a

variety of guest molecules for a known framework is highly

desirable.

Computational simulations can offer a unique insight into

the nature of host–guest interactions at the atomic level.[32,33]

The molecular docking method allows the prediction of the

structure of multicomponent complexes. It has been tradition-

ally used to determine the structure of complexes between

protein and small molecules, or between two proteins with a

wide variety of computational programs publicly or commer-

cially available.[34–39] The method has two major components:

a search algorithm to sample the configurational and confor-

mational degrees of freedom and a scoring function to evalu-

ate the binding energies between the parts of the system. In

flexible docking, the search algorithm explores different posi-

tions for the guest molecule in the host framework using the

M. O. Rodrigues, M. V. de Paula, K. A. Wanderley, I. B. Vasconcelos, S. Alves, Jr.

and T. A. Soares

Departament de Fundamental Chemistry, Universidade Federal de Pernambuco,

Cidade Universit�aria, Recife 50740-560, Brazil

E-mail: [email protected]

yPresent address: Instituto de Quımica, Universidade de Brasılia, Brasılia, DF

70910-900, Brazil.

Contract grant sponsor: FACEPE, CNPq, and INCT-INAMI.

VC 2012 Wiley Periodicals, Inc.

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translational, rotational, and torsional degrees of freedom.

Scores are assigned to the sampled conformations based on

the intermolecular interaction energy, and each conformation

is ranked relative to other poses and other ligands.

A previous molecular docking study suggested that protein-

specific docking programs could predict the binding poses of

small guest molecules into porous crystalline materials based

on either strong interactions or shape-fitting.[40] These findings

imply that the molecular docking technique may be applied

to the virtual screening of potential candidates to immobiliza-

tion in host frameworks. However, some questions need to be

addressed before drug screening for incorporation in crystal-

line frameworks becomes reliably attainable: How dependent

is the performance of actual docking algorithms on the con-

cept of ‘‘molecular complementarity’’, a major feature in protein

recognition, but perhaps less effective for the comparatively

unspecific drug–MOF association? How accurately will scoring

functions designed to model biomolecule–ligand interactions

work? How important is the treatment of one copy versus

multiple copies of the drug for correct prediction of the struc-

ture of MOF–guest complexes? The answers to these questions

will require a systematic evaluation of the method and careful

comparison of calculated structures against the rather scant

crystallographic data for MOF complexes.

In this report, we investigate the potential of the molecular

docking method to distinguish between high- and low-affinity

MOF-binding drugs at different pH conditions. Molecular dock-

ing calculations were carried out to estimate the binding con-

formation and binding affinity of guest molecules (ibuprofen,

methylene blue, amoxicillin, and gentamicin) to the metal-or-

ganic framework [Zn(BDC)(H2O)2]n (Fig. 2). The adsorption (or

not) of these drugs onto the [Zn(BDC)(H2O)2]n framework was

characterized by the means of powder X-ray diffraction and

differential thermal analysis and used to validate the computa-

tional predictions against.

Methodology

Molecular docking calculations

The hybrid search method based on the Lamarckian genetic

algorithm (LGA) implemented in the suite of programs Auto-

Dock v4.0 was used in conjunction with the semiempirical free

energy force field for the molecular docking calculations.[34,41]

This energy function describes the energetics of the binding

process involving two molecules in an aqueous environment

using pair-wise terms to evaluate the interaction between the

two molecules coupled to an empirical method to estimate

the contribution of the surrounding solvent.[41] Hence, the free

energy of binding is given by (i) the difference between the

energy of the guest molecule and the crystalline framework in

a separated unbound state and (ii) the energy of the guest–

host complex. During the conformational search, the intramo-

lecular energetics of the transition from the unbound state to

the bound conformation is evaluated for each of the mole-

cules separately, and subsequently the intermolecular ener-

getics of bringing the two molecules together into the bound

complex is evaluated. The binding energy between two mole-

cules is given by

DG ¼ ðVdrug�drugbound � Vdrug�drug

unbound Þ þ ðVMOF�MOFbound � VMOF�MOF

unbound Þþ ðVdrug�MOF

bound � Vdrug�MOFunbound þ DSconfÞ (1)

Equation (1) includes four terms to describe the intramolec-

ular energies for the bound and unbound states of the guest

molecule and for the bound and unbound states of the host

framework, two terms to describe the change in

Figure 1. Dimensionalities adopted by crystalline framework structures con-

structed from 1,4-BDC and Zn2þ ions. (A) 1-D structure, (B) 2-D structure,

and (C) 3-D structure (MOF-5). The [Zn(BDC)(H2O)2]n in its 1-D structure con-

sists of approximately tetrahedral Zn2þ centers linked by a herringbone pat-

tern of 1,4-BDC molecules aligned approximately along the crystallographic

a/c diagonal. The lamellar structure is kept through offset p–p stacking and

hydrogen bond interactions between adjacent 1-D chains. [Color figure can

be viewed in the online issue, which is available at wileyonlinelibrary.com.]

FULL PAPERWWW.Q-CHEM.ORG

International Journal of Quantum Chemistry 2012, 112, 3346–3355 3347

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intermolecular energy between the bound and unbound

states and a one term to represent an estimate of the confor-

mational entropy lost upon binding.[34,41] Pair-wise atomic

interactions account for dispersion/repulsion, hydrogen bond-

ing, electrostatics, and desolvation as described[3]

V ¼ Wwdw

Xij

Aij

r12ij

� Bijr6ij

!þWhbond

Xij

EðtÞ Cijr12ij

� Dij

r10ij

!

þWcoul

Xij

qiqieðrijÞrij

þWsol

Xij

ðSiVj þ SjViÞeð�r2ij=2r2Þ (2)

where W corresponds to weighting constants optimized to cal-

ibrate the empirical free energy based on a set of experimen-

tally characterized protein complexes.[41] The 6/12 potential

describes dispersion/repulsion interactions with parameters A

and B taken from the Amber force field.[42] The 10/12 potential

is a directional H-bond term whose interaction directionality

E(t) is dependent on the angle t away from ideal bonding ge-

ometry. A screened Coulomb potential is used to describe

electrostatic interactions. The fourth term is a desolvation

potential based on the volume (V) of the atoms surrounding a

given atom, weighted by a solvation parameter (S) and an ex-

ponential term based on the distance weighting r. Rotatable

bonds included all torsional degrees of freedom.[34,41]

All docking simulations were performed using the crystalline

structure of the [Zn(BDC)(H2O)2]n framework [CCSD number

182/1299].[27] During the conformational search, the ligands

were fully flexible concerning its degrees of translation, orien-

tation, and conformation with respect to the [Zn(BDC)(H2O)2]nstructure, which was kept rigid. Each sampled conformation

was evaluated and ranked according to the empirical energy

function [Eq. (2)]. Grid maps with 126 � 126 � 126 points of

dimension and a grid spacing of 0.25 A were calculated using

AutoGrid4.[43] Charges for ligands and the [Zn(BDC)(H2O)2]

framework unite were assigned via a restrained hyperbolic fit

of the electrostatic potential[44] on the nuclei positions of each

atom after geometry optimizations at the HF-6-31G* level

using the NWCHEM software.[45] Formal charges were assigned

to the Zn2þ metal ions. van der

Waals parameters were taken

from the Amber force field

available with the distribution

of AutoDock v4.0.[42] Electro-

static interactions were eval-

uated with a screened Coulomb

potential.[46] A desolvation

potential was used which is

based on the volume of the

atoms surrounding a given

atom, weighted by a solvation

parameter and an exponential

term based on the distance,

with a distance weighting factor

of 3.5 A.[47]

The LGA was used to perform

the conformational search fol-

lowing the same protocol previ-

ously described in Refs. [48,49]. Briefly, LGA parameters were

defined as follows: the initial population of random individuals

had a size of 150 individuals with a maximum number of 2.5

� 106 energy evaluations and a maximum number of genera-

tions equal to 27,000. An elitism rate of 1 was applied to

ensure that the top individual always survives into the next

generation in conjunction with mutation and crossover rates

of 0.02 and 0.08, respectively. A maximum of 300 iterations

per local search was used. The probability of performing a

local search on an individual was 0.06 where the maximum

number of consecutive successes or failures before doubling

or halving the search step was 4. The number of docking sim-

ulations performed for the guest molecules reflected the re-

spective number of degrees of freedom, that is, 100 steps (ibu-

profen and methylene blue) and 200 steps (amoxicillin and

gentamicin). The final lowest energy structures were clustered

based on a RMS positional deviation of 2.0 A.

Experimental Details

The Ibuprofen loading was performed by introducing, under

stirring for 3 days, 100 mg of the dehydrated powder material

(dried overnight at 150�C in an oven) in a 10 mL solution of

ethanol containing 300 mg of Ibuprofen. After drug immobili-

zation, the remaining ethanol was removed at 100�C. Amoxi-

cillin and gentamicin were loaded in the MOF by introducing,

under stirring for 7 days, 100 mg of the [Zn(BDC)(H2O)2]n ma-

terial in 10 mL aqueous solution (Milli-Q water) containing 300

mg of the drug. Adsorption of methylene blue was performed

by stirring 100 mg of the [Zn(BDC)(H2O)2]n material in a 5 mL

aqueous solution (10�3 M) of the drug during 7 days. Subse-

quently, the suspensions were filtered, and the resultant solids

were characterized by X-ray powder diffraction (XRPD) and

thermal analysis Thermogravimetry–differential thermal analy-

sis (TG–DTA) curves were acquired through a SHIMADZU DTG-

60H instrument in the range from 25 to 900�C using an alu-

mina crucible with about 8.0 mg of samples, under dynamic

nitrogen atmosphere (50 mL min�1) and with a heating rate

Figure 2. Chemical structures of ibuprofen, methylene blue, amoxicillin, and gentamicin.



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of 10�C min�1. XRD analyses were performed at room temper-

ature, using a Bruker D8 Advanced with DaVinci design

equipped with a LynxEye Linear position sensitive detector

and a copper (Cu) sealed tube (kka1 ¼ 1.5404 A, kka2 ¼1.5444 A, Ia2/Ia1 ¼ 0.5). Intensity data were collected in step

scanning mode, in the range from 5 to 50� (2y), with a step

size of 0.01�, Soller slit with 2.5� of divergence, 0.5� scattering

slit, and 0.6-mm receiving slit. The Rietveld refinement[50] for

the drug–[Zn(BDC)(H2O)2]n complexes was performed with the

software GSAS/EXPGUI,[51,52] using the X-ray structure of the

uncomplexed MOF as initial atomic coordinates.[27,53] The pref-

erential orientation of the system was corrected using spheri-

cal harmonic model (sixth order) proposed by Jarvinen.[54] The

peak profile was adjusted by the Thompson–Cox–Hastings

function modified by Young and Desai (pV-TCHZ).[55] Surface

roughness correction was refined by the Pitschke function,

and the background was fitted by an eighth-degree shifted

Chebyshev polynomial function. The refined parameters in the

final runs were the scale factor, background and absorption

coefficients, spherical harmonic, unit-cell parameters, and pV-

TCHZ correction for asymmetric parameters.

Results and Discussion

Experimental characterization of the drug–[Zn(BDC)(H2O)2]ncomplexes

The immobilization of ibuprofen on the [Zn(BDC)(H2O)2]n was

ascertained by UV–vis spectroscopy (Fig. 3). This technique

was used to quantify the mass of ibuprofen adsorbed on the

[Zn(BDC)(H2O)2]n after 8 days. The calibration curve used for

that end shows that of 44.5% of ibuprofen was adsorbed on

the [Zn(BDC)(H2O)2]n (Fig. 3). Moreover, the incorporation of

ibuprofen into different 1,4-BDC-derived MOFs has been previ-

ously reported.[11,12,29,31] In the case of methylene blue, amoxi-

cillin, and gentamicin, evidence of drug incorporation was first

assessed by visual inspection of change in the coloration of

the MOF upon incorporation of the drug (Fig. 4). Hence, the

complex amoxicillin–[Zn(BDC)(H2O)2]n acquired a brown color,

whereas the methylene blue–[Zn(BDC)(H2O)2]n became blue.

The complex gentamicin–[Zn(BDC)(H2O)2]n did not exhibit any

alteration of color compared to the pure framework. The color-

less pattern characteristic of the [Zn(BDC)(H2O)2]n framework

remained unaltered during the 7 days, when it was kept

immersed in the gentamicin solution. Overall, these results

attest to the incorporation of amoxicillin and methylene blue

into the framework and suggest that gentamicin is not

Figure 3. Calibration curve obtained from the absorption spectrum in the

ultraviolet–visible (UV–vis) spectral region for the ibuprofen solution. The

red line represents the best linear fit profile.

Figure 4. XRD for (A) pure [Zn(BDC)(H2O)2]n (solid red line) and the amoxicil-

lin–[Zn(BDC)(H2O)2]n complex (black circles), (B) gentamicin–[Zn(BDC)(H2O)2]ncomplex, and (C) methylene blue–[Zn(BDC)(H2O)2]n complex. Rietveld refine-

ment of the XRD data was required for (B) and (C). The difference (green solid

line) and Braggs peaks (blue bars) arising from the Rietveld fit are shown. As

there is an unequivocal structural difference between pure [Zn(BDC)(H2O)2]nand the amoxicillin–[Zn(BDC)(H2O)2]n complex (A), the Rietveld refinement

was not performed for this system. [Color figure can be viewed in the online

issue, which is available at wileyonlinelibrary.com.]



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incorporated into the material (Fig. 4). To establish or clarify

these findings, these samples were characterized by XRPD.

Significant changes of peak positions and relative intensities

were further observed for the complex amoxicillin–

[Zn(BDC)(H2O)2]n compared with their dissociated forms (Fig.

4), indicating that the interaction of amoxicillin onto MOF is

followed by drug degradation. This observation has been fur-

ther verified by the means of mass spectrometry analysis (de

Paula, Soares and Alves, Jr., unpublished results). The molecular

docking calculations indicate that the Zn2þ cations on the

framework surface are the most favorable interaction site for

amoxicillin (Fig. 8). This is also consistent with previous experi-

mental studies reporting on the role of Zn2þ-containing mate-

rials in the degradation of antibiotic molecules (amoxicillin,

ampicillin, penicillin-G, and penicillin-V) in aqueous solution

under natural light exposition.[56,57] However, XRPD patterns

for the methylene blue– and gentamicin–[Zn(BDC)(H2O)2]ncomplexes exhibited a complete overlap of the peaks, prevent-

ing proper determination of the structure (Fig. 4).

Rietveld analyses of the two complexes were carried to allow

a better separation of the peaks. The Rietveld method creates a

virtual separation of these overlapping peaks, thereby allowing

a more accurate determination of the components of the

chemical mixtures (for details about the technique see Refs.

[58,59]). The Rietveld refinement of the XRPD data for methyl-

ene blue–[Zn(BDC)(H2O)2]n and gentamicin–[Zn(BDC)(H2O)2]ncomplexes did not produce the separation of overlapping peaks

as intended. In fact, no significant change in the lattice parame-

ters of the [Zn(BDC)(H2O)2]n crystal or the presence of peaks

corresponding to the drugs after the adsorption experiments

were observed. These observations indicate that

[Zn(BDC)(H2O)2]n has an unique crystalline phase in both sam-

ples. These findings are consistent with the observation that

there is no visual difference between the powder obtained

from the [Zn(BDC)(H2O)2]n pure and in presence of the gentami-

cin solution, further suggesting that gentamicin does not

absorb to the [Zn(BDC)(H2O)2]n framework. However, the color

alteration observed for the methylene blue–[Zn(BDC)(H2O)2]npowder indicates that the complex is formed. As the characteri-

zation by XRPD diffraction has been unsuccessful in directly

identifying the adsorbed methylene blue in the framework ma-

terial possibly due to low quantities and/or positional disorder

of the adsorbed drugs, TGA/DTA analyses were performed to

unambiguously assess the presence of methylene blue in the

[Zn(BDC)(H2O)2]n surface (Fig. 5). These analyses were also

extended for the complex gentamicin–[Zn(BDC)(H2O)2]n to fur-

ther substantiate the XRPD and Rietveld refinement analysis.

The gentamicin–[Zn(BDC)(H2O)2]n and the uncomplexed

[Zn(BDC)(H2O)2]n exhibited similar DTA curves with three well-

defined exothermic peaks between 410 and 550�C, which cor-

respond to the thermal decomposition of the material. The

similar thermal behavior is indicative anew that the adsorption

of gentamicin onto the [Zn(BDC)(H2O)2]n surface did not take

place. In the case of the methylene blue–[Zn(BDC)(H2O)2]ncomplex, an endothermic peak was observed in the same tem-

perature range attributed to desorption of methylene blue or

its fragments concomitant to the degradation of the

[Zn(BDC)(H2O)2]n structure (Fig. 5). These results provide evi-

dence that methylene blue adsorbs onto the [Zn(BDC)(H2O)2]nsurface, in line with the observed color change of the

[Zn(BDC)(H2O)2]n material upon adsorption of the drug (Fig. 4).

The adsorption of drug to the [Zn(BDC)(H2O)2]n framework

The guest molecules used in this study were chosen to repre-

sent drugs of wide medical relevance and different complex-

ities (i.e., degrees of freedom) in the context of the conforma-

tional search algorithm. The trial set of molecules was

comprised of ibuprofen, methylene blue, amoxicillin, and gen-

tamicin. Ibuprofen is a widely used nonsteroidal anti-inflamma-

tory that inhibits the enzyme cyclo-oxygenase. This enzyme

converts arachidonic acid to prostaglandin H2, a precursor of

several other prostaglandins mediators of pain, inflammation,

and fever.[60,61] Methylene blue has been used in many differ-

ent areas of clinical medicine, from dementia to cancer chemo-

therapy.[62] It is also a potent antimalarial chemotherapeutic

that acts via competitive inhibition of the glutathione reduc-

tase of Plasmodium falciparum.[63–66] The photosensitizing

potential of methylene blue has also been recognized ena-

bling its application as antimicrobial agents for blood disinfec-

tion.[62] Amoxicillin is a moderate-spectrum b-lactam antibiotic

that inhibits the synthesis of the bacterial cell wall by a broad

range of Gram-positive and some Gram-negative bacteria.[67]

Amoxicillin has also been successfully used in the eradication

of Helicobacter pylori, a Gram-negative bacterium that can

inhabit different regions of the stomach. Currently, the most

efficient eradication treatment of this bacterium consists of

triple therapy using a proton pump inhibitor or ranitidine bis-

muth citrate, combined with clarithromycin and amoxicil-

lin.[68,69] Gentamicin is an aminoglycoside antibiotic used for

broad-spectrum treatment of various infections.[70,71] It has

Figure 5. TG–DTA curves for the uncomplexed [Zn(BDC)(H2O)2]n (black

line), gentamicin–[Zn(BDC)(H2O)2]n (red line), and methylene blue–

[Zn(BDC)(H2O)2]n (blue line) complexes. Peaks and dips represent exother-

mic and endothermic processes, respectively. The latter is indicated by the

down arrow symbol. The distinctive dip at 500�C observed for the methyl-

ene blue–[Zn(BDC)(H2O)2]n complex (blue line) corresponds to the methyl-

ene blue dissociation event. [Color figure can be viewed in the online

issue, which is available at wileyonlinelibrary.com.]



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been the core of antibacterial therapy for serious Gram-nega-

tive infections. More recently, aminoglycoside antibiotics have

become important tools to study molecular recognition of

ribonucleic acid by exploiting their mechanism of action.[72]

Guest molecules were docked onto the [Zn(BDC)(H2O)2]n struc-

tures, and the lowest energy conformer was selected for each of

the compounds tested (Figs. 5–7). The lowest energy conformer is

defined as the conformation of the ligand with the most favorable

interaction energy as given by an empirical energy function[41] and

selected out of a about 106 sampled conformations.[34,48,49,73] For

the studied drug molecules, the lowest energy conformation cor-

responded also to the most populated cluster of structures based

on a RMSD of 2.0 A. Overall, only one predominant conformation

was identified for the docked molecules, but several clusters of

conformers were formed, because the same conformation could

bind to different regions of the crystalline structure of the frame-

work. The effect of pH on the binding of the guest molecules has

been taken into account through the representation of their pro-

tonation states. Ibuprofen can be unprotonated with a formal

charge of �1 or protonated with a formal charge of 0. Methylene

blue is cationic in nature and only

becomes dicationic at extremely

low pH ¼ 1.[74] Hence, it was rep-

resented as one single protona-

tion state with a formal charge of

þ1. The protonation states of

amoxicillin yielded formal charges

of �1 (protonated, acidic pH), 0

(zwitterionic, neutral pH), and þ1

(unprotonated, basic pH).

The [Zn(BDC)(H2O)2]n consists

of approximately tetrahedral Zn2þ

centers linked by a herringbone

pattern of 1,4-BDC spacers

aligned approximately along the

crystallographic a/c diagonal.[27]

The herringbone packing pro-

duces a molecular surface com-

posed of V-shaped clefts, so that

a guest molecule can bind either

inside the cleft or across it, some-

times bridging the its boundary-

setting crests. Throughout the

docking procedure, the host

framework atomic coordinates

were kept fixed, whereas the

guest conformation was fully flex-

ible. In this study, guest molecules

were chosen to represent either

simple or complex test cases from

the perspective of conformational

search and suitability of the scor-

ing function in predicting host–

guest interactions in MOFs. Sim-

pler molecules have a low num-

ber of degrees of freedom and

interact through a well-defined

pattern of molecular interactions. That is the case of compounds

such as ibuprofen and methylene blue. In contrast, complex mol-

ecules have a higher number of degrees of freedom and interact

through a less-defined pattern of molecular interactions. This

class is represented in our calculations by the b-lactam antibiotic

amoxicillin and the aminoglycoside antibiotic gentamicin.

Ibuprofen binds along one of the sides of the V-shaped cleft

in the [Zn(BDC)(H2O)2]n surface via electrostatic and p–p inter-

actions (Fig. 6). It binds in parallel to cleft wall, spanning its

full bottom-up length. Electrostatic interactions occur between

the ibuprofen carboxylic group and Zn2þ cation in the MOF

surface, whereas p–p stacking takes place between the ben-

zene rings of host and guest (Fig. 6). Protonated and unproto-

nated states of the ibuprofen molecule led to the same bind-

ing conformation. However, the estimated binding affinity (Kd)

of the unprotonated ibuprofen for the [Zn(BDC)(H2O)2]n sur-

face is in the femtomolar (10�15 M) range whereas the proto-

nated form is the micromolar (10�6 M). It has been previously

shown that ibuprofen is incorporated with remarkably high

load and slow release to MOFs made of 1,4-BDC and iron or

Figure 6. Lowest energy conformers of ibuprofen in the protonated (blue) and unprotonated (green) states.

The respective lowest energy conformers are representative of the most populated conformational cluster. A

total of 4.05 � 106 conformations were sampled for the each of the protonated and deprotonated states of ibu-

profen. (A) Longitudinal and (B) top views of the complex predicted structure. [Color figure can be viewed in

the online issue, which is available at wileyonlinelibrary.com.]



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chromium metals.[12,29] It was also asserted that the amount of

adsorbed ibuprofen depended on the relative affinity between

the drug and the porous surface.[29]

Methylene blue was expected to bind to [Zn(BDC)(H2O)2]n in an

analogous manner to ibuprofen, that is, through p–p interactions

between its conjugated ring and the benzene ring of the BDC

molecule in the framework. However, this molecule has a formal

charge of þ1, resulting in a strong electrostatic repulsion when in

contact with the Zn2þ populated surface of the framework. As a

proof of concept, docking calculations were performed for the col-

orless reduced form of the methylene blue (leucomethylene blue),

whose formal charge is 0. The leucomethylene blue binds along

the longitudinal axis of the V-shaped cleft in the [Zn(BDC)(H2O)2]nsurface through p–p interactions between its conjugated ring and

the benzene ring of the BDC molecule of the host framework (Fig.

7). It binds to the [Zn(BDC)(H2O)2]n surface with a binding affinity

of the order of millimolar (10�3 M). In contrast with the docking

calculations, the experimental characterization of the complex

shows that the colorless crystal of the [Zn(BDC)(H2O)2]n becomes

blue upon the adsorption of the methylene blue (Fig. 4). The

amount of adsorbed drug

increases with increasing pH,

indicating that electrostatic

interactions govern the binding

of methylene blue and

[Zn(BDC)(H2O)2]n. A similar pat-

tern has been previously

observed for the adsorption of

methylene blue to the posi-

tively charged iron-containing

MOF253.[75] As for [Zn(BDC)

(H2O)2]n, the adsorbed amount

of methylene blue into MOF253

increased with increasing pH.

However, in the pH range of

the our measurements, that is,

pH 5–12, the protonation state

of the methylene blue does not

change, because it becomes

dicationic only at pH 1.[74] There-

fore, the pH-dependent increase

of methylene blue adsorption is

likely to result from a decrease

of the concentration of Hþ in

the aqueous milieu and not from

changes in the protonation state

of the drug. In such cases, the

molecular docking approach is

intrinsically unsuitable due to its

implicit treatment of the solvent.

Due to its larger molecular

size, gentamicin and amoxicil-

lin do not fit entirely inside

the V-shaped cleft. The dock-

ing calculations show that

gentamicin does not bind to

[Zn(BDC)(H2O)2]n framework

due to strong electrostatic repulsion. This finding corroborates

the XRPD and TGA/DTA analyses (Figs. 4 and 5). Amoxicillin

binds to the [Zn(BDC)(H2O)2]n surface through electrostatic

interactions involving oxygen atoms (Fig. 8). The binding affin-

ity of amoxicillin for the [Zn(BDC)(H2O)2]n surface increases as

more deprotonated oxygen sites becomes available, that is,

with increasing pH. In acidic conditions, amoxicillin binds to a

single Zn2þ cation through the lactam carbonyl and the car-

boxyl groups (Fig. 8A), whereas in neutral conditions, it inter-

acts with two Zn2þ cations through the amide carbonyl and

carboxyl groups (Fig. 8B). In basic conditions, the lowest

energy conformer binds to three Zn2þ cations via the carboxyl,

amide carbonyl, and the deprotonated hydroxyl groups (Fig.

8C). However, the predominant conformer in basic conditions

interacts with two Zn2þ cations through the carboxyl and the

deprotonated hydroxyl groups and has a binding energy of

about 2.51 kJ mol�1 less favorably that the lowest energy con-

former. It is not possible distinguish energetically between

these two conformers given that the error associated with the

potential energy function is ca. 8.4 kJ mol�1.[41] It is likely that

Figure 7. Lowest energy conformer of leucomethylene blue, the colorless reduced form of the methylene blue.

The lowest energy conformer is representative of the most populated conformational cluster. A total of 4.05 � 106

conformations were sampled. (A) Longitudinal and (B) top views of the predicted structure of the complex. [Color

figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]



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both conformers occur in solution. Remarkably, the binding af-

finity of the different protonation states of amoxicillin for the

[Zn(BDC)(H2O)2]n surface differs in orders of magnitude (Table

1). The estimated dissociation constant for the binding of

amoxicillin on the host surface is 10�21M in basic pH, 10�9 M

in acidic pH, and 10�3 M in neutral pH (Table 1).

The XRPD analysis of amoxicillin–[Zn(BDC)(H2O)2]n complex pro-

vides evidence for the strong association of amoxicillin to the

framework surface (Fig. 4). It further shows that amoxicillin under-

goes degradation upon adsorption onto [Zn(BDC)(H2O)2]n. Based

on the docking predictions, amoxicillin binds to the Zn2þ cations

on the framework surface, suggesting that the metal is involved

in the chemical degradation of the drug. Such assessment is

aligned with previous reports on the degradation of b-lactam

antibiotics (amoxicillin, ampicillin, penicillin-G, and penicillin-V) by

different Zn2þ-containing materials in aqueous solution.[56,57]

Moreover, the Zn2þ cation participates in the enzymatic cleavage

of the b-lactam ring by b-lactamases.[76,77] In these enzymes, the

Zn2þ cation acts as a Lewis acid by co-ordination to the b-lactam

carbonyl oxygen to facilitate nucleophilic attack and stabilize the

negative charge developed on this oxygen in the tetrahedral in-

termediate anion.[76] Therefore, [Zn(BDC)(H2O)2]n appears to be a

promising platform for removal and degradation of b-lactam anti-

biotics. Mass spectrometry is currently being applied to identify

the reaction products of the degradation reaction in our group.

This report suggest that the molecular docking procedure

can differentiate between drugs that does adsorb onto the

[Zn(BDC)(H2O)2]n framework and those that does not. The pro-

cedure can also identify the drug binding conformation and

rank different protonation states of a given drug based on

their respective binding affinities. However, the molecular

docking predictions failed in the case of the methylene blue.

The adsorption of this positively charged molecule onto a pos-

itively charged surface appears to be strongly governed by sol-

vent effects (water dissociation, Hþ concentration). In such

cases, molecular docking techniques are meant to fail, because

its computational robustness relies on the implicit treatment

of the solvent among other approximations. The correct rank-

ing of these compounds was possible, because the estimated

binding affinity differences for the different classes of guest

molecules or different protonation states of the same com-

pound are well above the error of 8.4 kJ mol�1 associated

with the empirical scoring function (Table 1, Figs. 3–7).[41] It is

well known that actual docking scoring functions have a lim-

ited performance in estimating absolute binding energies.[78]

Nevertheless, these functions can correctly rank guest mole-

cules based on binding affinities for experimental testing.[79]

Most empirical energy functions implemented in molecular

docking programs have been developed for predicting binding

affinities between drugs and biomolecules. It is encouraging

that these functions can be readily applied to novel materials

such as MOFs with a fairly successful prediction rate. Of

course, the natural direction from here is the development of

empirical energy functions specific for the class of host–guest

systems described in this report.

Figure 8. Lowest energy conformers from molecular docking calculations of

the (A) protonated (low pH), (B) zwitterionic (neutral pH), and (C) deprotonated

(basic pH) states of amoxicillin. Predominant conformations out of a total of 5.4

� 106 sampled conformations for each protonation state. The lowest energy

binding conformer is also representative of the most populated conformational

cluster for the protonated and zwitterionic states of amoxicillin. Representation

pictures the p–p interactions between aromatic groups and electrostatic inter-

actions between functional groups in the guest–host complex. [Color figure

can be viewed in the online issue, which is available at wileyonlinelibrary.com.]

Table 1. Estimated binding energies and dissociation constants from molecular docking calculations.

CompoundConformer occurrence[a] (%) Binding energy[b] (kJ mol�1) Estimated Kd (M)

Protonation state[c] P U Z P U Z P U Z

Ibuprofen 100 100 n/a �21.06 �77.79 n/a 10�6 10�15 n/a

Methylene blue 97 100 n/a �14.28 �14.28 n/a 10�3 10�3 n/a

Amoxicillin 29 100 69 �4.36 �107.06 �44.59 10�3 10�21 10�9

[a] Occurrence of the predominant conformation among sampled structures. [b] Binding energy corresponding to the lowest energy conformer. [c] Pro-

tonation states are protonated (P), unprotonated (U), and zwiterionic (Z). The protonated state corresponds to acidic pH, the unprotonated state to ba-

sic pH, and the zwiterionic form to neutral pH.



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Conclusions

This work reported on the use of the molecular docking meth-

odology for the identification of candidate drug molecules

with high affinities for the [Zn(BDC)(H2O)2]n framework. If suc-

cessful, such approach can offer a view of host–guest interac-

tions at the atomistic level not easily accessible by experimen-

tal means. It is also a very robust approach for the screening

of large databanks containing thousands of compounds or

biomedical relevance. Evaluated systems were comprised of

the [Zn(BDC)(H2O)2]n framework and four guest molecules ibu-

profen, methylene blue, amoxicillin, and gentamicin. These

compounds were chosen to represent drugs of wide medical

use and different complexities in terms of their number of

degrees of freedom. To validate the theoretical estimates, the

experimental characterization of the host–guest complexes

was performed by the means of XRD and differential thermal

analysis. The proposed molecular docking procedure was able

to identify the overall binding mode of three out of four

tested compounds as well as to estimate binding affinity dif-

ferences for the different classes of guest molecules which are

well above the error of 8.4 kJ mol�1 associated with the em-

pirical scoring function.[41] Mostly notably, the procedure was

also able to distinguish between bad (gentamicin) and good

(ibuprofen, methylene blue, and amoxicillin) drug candidates

for immobilization in the [Zn(BDC)(H2O)2]n framework as vali-

dated via experimental measurements. Hence, the proposed

approach has the potential to offer a fast and affordable alter-

native prior to experiments for identification of the binding

preferences of a host system for a set of guest molecules. We

have taken an initial step toward the evaluation of the

approach applied to metal-organic frameworks.

Acknowledgments

Experimental measurements were performed by M.V. P., K.A.W., and

I.B.V. Interpretation of measurements were performed by M.V. P. and

S.A.-Jr. Computational simulations and related analyses were per-

formed by T.A.S.

Keywords: host–guest systems � crystalline nanosurfaces �AutoDock � molecular modeling � b-lactam � Zn21-catalysed

degradation � environmental remediation

How to cite this article: MO. Rodrigues, MV. de Paula, KA.

Wanderley, IB. Vasconcelos, S. Alves, Jr., TA. Soares, Int. J.

Quantum Chem. 2012, 112, 3346–3355. DOI: 10.1002/qua.24211

Additional Supporting Information may be found in the

online version of this article.

[1] C. Janiak, Dalton Trans. 2003, 2781, doi: 10.1039/B305705B.

[2] S. Kitagawa, R. Kitaura, S. Noro, Angew. Chem. Int. Ed. Engl. 2004, 43,

2334.

[3] U. Mueller, M. Schubert, F. Teich, H. Puetter, K. Schierle-Arndt, J. Pastre,

J. Mat. Chem. 2006, 16, 626.

[4] A. Ramanan, M. S. Whittingham, Cryst. Growth Des. 2006, 6, 2419.

[5] J. L. C. Rowsell, O. M. Yaghi, Microporous Mesoporous Mater. 2004,

73, 3.

[6] O. M. Yaghi, M. O’Keeffe, N. W. Ockwig, H. K. Chae, M. Eddaoudi, J.

Kim, Nature 2003, 423, 705.

[7] M. Hartmann, S. Kunz, D. Himsl, O. Tangermann, S. Ernst, A. Wagener,

Langmuir 2008, 24, 8634.

[8] Z. Zhao, Z. Li, Y. S. Lin, Ind. Eng. Chem. Res. 2009, 48, 10015.

[9] Y. Liu, W. Xuan, Y. Cui, Adv. Mater. 2010, 22, 4112.

[10] A. M. Shultz, O. K. Farha, J. T. Hupp, Nguyen, S. T. J. Am. Chem. Soc.

2009, 131, 4204.

[11] P. Horcajada, T. Chalati, C. Serre, B. Gillet, C. Sebrie, T. Baati, J. F.

Eubank, D. Heurtaux, P. Clayette, C. Kreuz, J. S. Chang, Y. K. Hwang, V.

Marsaud, P. N. Bories, L. Cynober, S. Gil, G. Ferey, P. Couvreur, R. Gref,

Nature Mat. 2010, 9, 172.

[12] P. Horcajada, C. Serre, M. Vallet-Regi, M. Sebban, F. Taulelle, G. Ferey,

Angew. Chem. Int. Ed. Engl. 2006, 45, 5974.

[13] R. C. Huxford, J. Della Rocca, W. Lin, Curr. Opin. Chem. Biol. 2010, 14,

262.

[14] M. O. Rodrigues, F. A. Paz, R. O. Freire, G. F. de Sa, A. Galembeck, M. C.

Montenegro, A. N. Araujo, S. Alves, J. Phys. Chem. B 2009, 113, 12181.

[15] B. Chen, X. Zhao, A. Putkham, K. Hong, E. B. Lobkovsky, E. J. Hurtado,

A. J. Fletcher, K. M. Thomas, J. Am. Chem. Soc. 2008, 130, 6411.

[16] B. L. Chen, Y. Yang, F. Zapata, G. N. Lin, G. D. Qian, E. B. Lobkovsky,

Adv. Mater. 2007, 19, 1693.

[17] Y. F. Chen, R. Babarao, S. I. Sandler, J. W. Jiang, Langmuir 2010, 26,

8743.

[18] L. E. Kreno, J. T. Hupp, R. P. Van Duyne, Anal. Chem. 2010, 82, 8042.

[19] A. Aquino, K. A. Wanderley, O. Paiva-Santos, G. F. de Sa, R. Alexandre,

S. A. Junior, S. Navickiene, Talanta 2010, 83, 631.

[20] A. S. Barreto, R. L. da Silva, S. C. Dos Santos Silva, M. O. Rodrigues, C.

A. de Simone, G. F. de Sa, S. A. Junior, S. Navickiene, M. E. de Mes-

quita, J. Sep. Sci. 2010, 33, 3811.

[21] P. H. de Carvalho, A. S. Barreto, M. O. Rodrigues, M. Prata, P. B. Alves,

M. E. de Mesquita, S.Alves, Jr., S. Navickiene, J. Sep. Sci. 2009, 32, 2132.

[22] D. Britt, D. Tranchemontagne, O. M. Yaghi, Proc. Natl. Acad. Sci. U.S.A.

2008, 105, 11623.

[23] M. Eddaoudi, J. Kim, N. Rosi, D. Vodak, J. Wachter, M. O’Keeffe, O. M.

Yaghi, Science 2002, 295, 469.

[24] M. Eddaoudi, H. L. Li, O. M. Yaghi, J. Am. Chem. Soc. 2000, 122, 1391.

[25] D. J. Tranchemontagne, J. R. Hunt, O. M. Yaghi, Tetrahedron 2008, 64,

8553.

[26] M. Edgar, R. Mitchell, A. M. Z. Slawin, P. Lightfoot, P. A. Wright, Chem.

Eur. J. 2001, 7, 5168.

[27] G. Guilera, J. W. Steed, Chem. Comm. 1999, 16, 1563.

[28] H. Li, M. Eddaoudi, M. O’Keeffe, O. M. Yaghi, Nature 1999, 402, 276.

[29] P. Horcajada, C. Serre, G. Maurin, N. A. Ramsahye, F. Balas, M. Vallet-

Regi, M. Sebban, F. Taulelle, G. Ferey, J. Am. Chem. Soc. 2008, 130,

6774.

[30] S. M. Cohen, Curr. Opin. Chem. Biol. 2007, 11, 115.

[31] P. Horcajada, R. Gref, T. Baati, P. K. Allan, G. Maurin, P. Couvreur, G.

Ferey, R. E. Morris, C. Serre, Chem. Rev. 2012, 112, 1232.

[32] W. van Gunsteren, D. Bakowies, R. Burgi, I. Chandrhhasekhar, M. Chris-

ten, X. Daura, P. Gee, A. Glattli, T. Hansson, C. Oostenbrink, C. Peter, J.

Pitera, L. Schuler, T. Soares, H. B. Yu, Chimia 2001, 55, 856.

[33] W. F. van Gunsteren, J. Dolenc, A. E. Mark, Curr. Opin. Struct. Biol. 2008,

18, 149.

[34] G. M. Morris, R. Huey, W. Lindstrom, M. F. Sanner, R. K. Belew, D. S.

Goodsell, A. J. Olson, J. Comp. Chem. 2009, 30, 2785.

[35] R. A. Friesner, J. L. Banks, R. B. Murphy, T. A. Halgren, J. J. Klicic, D. T.

Mainz, M. P. Repasky, E. H. Knoll, M. Shelley, J. K. Perry, D. E. Shaw, P.

Francis, P. S. Shenkin, J. Med. Chem. 2004, 47, 1739.

[36] T. A. Halgren, R. B. Murphy, R. A. Friesner, H. S. Beard, L. L. Frye, W. T.

Pollard, J. L. Banks, J. Med. Chem. 2004, 47, 1750.

[37] A. N. Jain, J. Comput. Aided Mol. Des. 2007, 21, 281.

[38] B. Kramer, M. Rarey, T. Lengauer, Proteins 1999, 37, 228.

[39] B. K. Shoichet, D. L. Bodian, I. D. Kuntz, J. Comp. Chem. 1992, 13, 380.

[40] O. Korb, P. A. Wood, Chem. Commun. 46, 3318.

[41] R. Huey, G. M. Morris, A. J. Olson, D. S. Goodsell, J. Comp. Chem. 2007,

28, 1145.

[42] S. J. Weiner, P. A. Kollman, D. A. Case, U. C. Singh, C. Ghio, G. Alagona,

S. Profeta, P. K. Weiner, J. Am. Chem. Soc. 1984, 106, 765.



http://q-chem.org/




[43] P. J. Goodford, J. Med. Chem. 1985, 28, 849.

[44] W. D. Cornell, P. Cieplak, C. I. Bayly, P. A. Kollman, J. Am. Chem. Soc.

1993, 115, 9620.

[45] M. Valiev, E. J. Bylaska, N. Govind, K. Kowalski, T. P. Straatsma, H. J. J.

van Dam, D. Wang, J. Nieplocha, E. Apra, T. L. Windus, W. A. de Jong,

Comput. Phys. Commun. 2010, 181, 1477.

[46] E. L. Mehler, T. Solmajer, Protein Eng. Des. Sel. 1991, 4, 903910.

[47] P. F. W. Stouten, C. Fr€ommel, H. Nakamura, C. Sander, Mol. Simulat.

1993, 10, 97.

[48] T. A. Soares, D. S. Goodsell, J. M. Briggs, R. Ferreira, A. J. Olson, Biopoly-

mers 1999, 50, 319.

[49] T. Soares, D. Goodsell, R. Ferreira, A. J. Olson, J. M. Briggs, J. Mol.

Recogn. 2000, 13, 146.

[50] H. M. Rietveld, J. Appl. Crystallogr. 1969, 2, 65.

[51] A. C. Larson, R. B. Von Dreele, General Structure Analysis System

(GSAS). Los Alamos National Laboratory: Los Alamos, 2004, p.224.

[52] B. H. Toby, J. Appl. Crystallogr. 2001, 34, 210.

[53] L. R. Hoffman, D. A. D’Argenio, M. J. MacCoss, Z. Zhang, R. A. Jones, S.

I. Miller, Nature 2005, 436, 1171.

[54] M. Jarvinen, J. Appl. Crystallogr. 1993, 26, 525.

[55] R. A. Young, P. Desai, Archiwun Nauki o Materialach 1989, 10, 71.

[56] E. S. Elmolla, M. Chaudhuri, J. Harzard. Mater. 2010, 173, 445.

[57] R. Nosrati, A. Olad, R. Maramifar, Environ. Sci. Poll. Res. Intl. 2012. [Epub

ahead of print].

[58] R. E. Dinnebier, S. J. Billinge, Powder Diffraction; Royal Society of

Chemistry: Cambridge, 2008.

[59] L. B. McCusker, R. B. Von Dreele, D. E. Cox, D. Loueur, P. Scardi, J. Appl.

Crystallogr. 1999, 32, 36.

[60] M. Busson, J. Int. Med. Res. 1986, 14, 53.

[61] E. R. Southey, K. Soares-Weiser, J. Kleijnen, Curr. Med. Res. Opin. 2009,

25, 2207.

[62] M. Wainwright, K. B. Crossley, J. Chemother. 2002, 14, 431.

[63] M. Bountogo, A. Zoungrana, B. Coulibaly, C. Klose, U. Mansmann, F. P.

Mockenhaupt, J. Burhenne, G. Mikus, I. Walter-Sack, R. H. Schirmer, A.

Sie, P. Meissner, O. Muller, Trop. Med. Int. Health. 2010, 15, 713.

[64] F. Gut, W. Schiek, W. E. Haefeli, I. Walter-Sack, J. Burhenne, Eur. J.

Pharm. Biopharm. 2008, 69, 582.

[65] R. Pastrana-Mena, R. R. Dinglasan, B. Franke-Fayard, J. Vega-Rodriguez,

M. Fuentes-Caraballo, A. Baerga-Ortiz, I. Coppens, M. Jacobs-Lorena, C.

J. Janse, A. E. Serrano, J. Biol. Chem. 2010, 285, 27045.

[66] A. Zoungrana, B. Coulibaly, A. Sie, I. Walter-Sack, F. P. Mockenhaupt, B.

Kouyate, R. H. Schirmer, C. Klose, U. Mansmann, P. Meissner, O. Muller,

PLoS One 2008, 3, e1630.

[67] S. M. Drawz, R. A. Bonomo, Clin. Microbiol. Rev. 2010, 23, 160.

[68] P. Malfertheiner, F. Megraud, C. O’Morain, F. Bazzoli, E. El-Omar, D. Gra-

ham, R. Hunt, T. Rokkas, N. Vakil, E. J. Kuipers, Gut 2007, 56, 772.

[69] P. Malfertheiner, F. Megraud, C. O’Morain, A. P. Hungin, R. Jones, A.

Axon, D. Y. Graham, G. Tytgat, Aliment Pharmacol. Ther. 2002, 16, 167.

[70] A. Forge, J. Schacht, Audiol. Neurootol. 2000, 5, 3.

[71] C. G. Kumar, M. Himabindu, A. Jetty, Crit. Rev. Biotechnol. 2008, 28,

173.

[72] T. Hermann, Cell. Mol. Life Sci. 2007, 64, 1841.

[73] T. A. Soares, R. D. Lins, T. P. Straatsma, J. M. Briggs, Biopolymers 2002,

65, 313.

[74] M. Wainwright, L. Amaral, Trop. Med. Int. Health. 2005, 10, 501.

[75] E. Haque, J. W. Jun, S. H. Jhung, J. Hazardous Mater. 2011, 185, 507.

[76] M. I. Page, A. Badarau, Bioinorg. Chem. Appl. 2008, 576297, doi:

10.1155/2008/576297.

[77] Z. Wang, W. Fast, A. M. Valentine, S. J. Benkovic, Curr. Opin. Chem. Biol.

1999, 3, 614.

[78] D. L. Mobley, K. A. Dill, Structure 2009, 17, 489.

[79] N. Brooijmans, I. D. Kuntz, Annu. Rev. Biophys. Biomol. Struct. 2003, 32,

335.

Received: 18 February 2012Revised: 14 May 2012Accepted: 18 February 2012Published online on 8 June 2012



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Artigo docking Zn BDC 2012

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