Chiral Recognition of Amino Acids by Use of a FluorescentResorcinarene

GERALD I. RICHARD, HADI M. MARWANI, SHAN JIANG, SAYO O. FAKAYODE,MARK LOWRY, ROBERT M. STRONGIN,* and ISIAH M. WARNER*Department of Chemistry, Louisiana State University, Baton Rouge, Louisiana 70803 (G.I.R., H.M.M., S.J., M.L., I.M.W.); Department of

Chemistry, King Abdulaziz University, Jeddah, Saudi Arabia 21589 (H.M.M.); Department of Chemistry, Winston-Salem State University,

Winston-Salem, North Carolina 27110 (S.O.F.); and Department of Chemistry, Portland State University, Portland, Oregon 97207 (R.M.S.)

The spectroscopic properties of a chiral boronic acid based resorcinarene

macrocycle employed for chiral analysis were investigated. Specifically,

the emission and excitation characteristics of tetraarylboronate resorci-

narene macrocycle (TBRM) and its quantum yield were evaluated. The

chiral selector TBRM was investigated as a chiral reagent for the

enantiomeric discrimination of amino acids using steady-state fluores-

cence spectroscopy. Chiral recognition of amino acids in the presence of

the macrocycle was based on diastereomeric complexes. Results demon-

strated that TBRM had better chiral discrimination ability for lysine as

compared to the other amino acids. Partial least squares regression

modeling (PLS-1) of spectral data for macrocycle-lysine guest–host

complexes was used to correlate the changes in the fluorescence emission

for a set of calibration samples consisting of TBRM in the presence of

varying enantiomeric compositions of lysine. In addition, validation

studies were performed using an independently prepared set of samples

with different enantiomeric compositions of lysine. The results of

multivariate regression modeling indicated good prediction ability of

lysine, which was confirmed by a root mean square percent relative error

(RMS%RE) of 5.8%.

Index Headings: Chiral analysis; Fluorescent sensors; Chemometrics;

Partial least squares regression modeling; PLS-1.

INTRODUCTION

Chiral recognition has an important influence on biologicalsystems due to the many naturally occurring chiral moleculessuch as carbohydrates, amino acids, proteins, hormones,enzymes, and DNA.1–6 In fact, several biological functionsare based on chiral interactions. For example, since humanodor receptors are chiral, humans are able to recognize that (R)-limonene smells like lemons and (S)-limonene smells likeoranges. Likewise, D-asparagine tastes sweet and L-asparaginetastes bitter. While it is generally accepted that biologicalsystems are primarily composed of L-amino acids and D-carbohydrates, the presence of D-amino acids at low levels hasbeen reported in humans.7–10 In addition, D-amino acids maypossibly have an antagonistic biological function.

The true importance of chirality to human functions wasrealized in the 1960s when the chiral drug thalidomide wasprescribed to pregnant women to alleviate morning sickness. Itwas later discovered that the (R)-enantiomer produced thedesired therapeutic effect, while the (S)-enantiomer resulted insevere birth defects.11 As a result, there has been a proliferationof studies of enantiomeric selectivity in biological systems, anda concomitant series of analytical methods have been applied.The most commonly used analytical methods involve chro-matographic techniques such as high-performance liquid

chromatography (HPLC),12 gas chromatography (GC),13 andcapillary electrophoresis (CE).14 However, spectroscopictechniques such as UV-visible,15 nuclear magnetic resonance(NMR),16 and mass spectroscopy17 have shown considerablepromise in studying chiral recognition ability for differentapplications. Fluorescence spectroscopy is one of the mostpowerful and widely used methods in studying the enantio-selectivity of several biological systems because of itsrelatively high sensitivity, selectivity, and versatility.18–21

Consequently, enantioselective fluorescent sensors have at-tracted considerable interest for use in potential bioanalyticalapplications.22–24

When enantioselective studies are performed in an achiralenvironment, chiral discrimination is not observed. Therefore,the use of a chiral selector for chiral recognition of enantiomersis required. For instance, some commonly used fluorescentchiral selectors for spectroscopic investigations of chiralmolecules include fluorescent-labeled cyclodextrins,22 crownethers,23 and calixarenes.24 Previously, a chiral pinanediol-derived boronate resorcinarene macrocycle was developed byLewis et al.25 Resorcinarenes are cyclic aromatic tetramerssynthesized in a single step from an acid catalyzed condensa-tion reaction of resorcinol and an aldehyde.26 Lewis et al.,27 Heet al.,28 Davis et al.,29 and Zhang et al.30 have previouslyreported the use of boronic acid based resorcinarene macrocy-cles for achiral and chiral carbohydrate recognition andchemical sensing using UV-visible spectroscopic methods.Consequently, these macrocycles could be useful in investi-gating the fluorescence detection and enantiodiscrimination ofamino acids.

Amino acids, which are structurally similar to one another,but have different side chains, are among the most importantmolecules in biological systems because they are alsoconstituents of peptides, proteins, and DNA. Most amino acidsexhibit transparency in the UV-visible region. In fact, theoptical detection of amino acids has been the focus of severalresearch studies. However, a majority of fluorescent chiralrecognition studies involving amino acids have been performedwith amino acid derivatives.31–33 Therefore, fluorescenceenantioselective recognition of unmodified amino acids wouldbe an attractive approach for monitoring diseases andunderstanding protein biological functions. In addition, manyapproaches have recently demonstrated that the enantiomericcomposition of chiral molecules can be determined bymultivariate regression modeling of fluorescence emissionspectral data.18,34–36 These results from spectral data measure-ments have demonstrated that multivariate regression modelingis a reliable method for evaluating the chiral recognition abilityof chiral molecules.

This study reports the fluorescent spectral properties of

Received 7 December 2007; accepted 22 February 2008.* Authors to whom correspondence should be sent. E-mail: iwarner@lsu.edu and strongin@pdx.edu.

476 Volume 62, Number 5, 2008 APPLIED SPECTROSCOPY0003-7028/08/6205-0476$2.00/0

� 2008 Society for Applied Spectroscopy

tetraarylboronate resorcinarene macrocycle (TBRM) andinvestigates the enantiomeric selectivity of unmodified aminoacids with TBRM employed as a chiral selector. In addition,the chiral recognition ability of TBRM with selected aminoacids as well as determination of enantiomeric composition oflysine was evaluated by use of steady-state fluorescencespectroscopy and multivariate regression analysis.

EXPERIMENTAL

Materials. D- and L-enantiomers of alanine, arginine,asparagine, aspartate, glutamine, glutamate, isoleucine, leucine,lysine, methionine, proline, serine, threonine, and valine wereobtained from Sigma-Aldrich (St. Louis, MO) and used asreceived. Spectroscopic grade (�99.9%) acetonitrile, dimethylsulfoxide, ethyl acetate, methanol, and tetrahydrofuran werealso purchased from Sigma Aldrich (St. Louis, MO) and usedas received. Resorcinol, 4-formylphenylboronic acid, ethanol,hydrochloric acid, dimethyl formamide, sodium sulfate, and1R, 2R, 3S, and 5R-(-)-pinanediol were obtained from SigmaChemical Company (Milwaukee, WI). The purity of allanalytes and reagents was 99% or higher.

Tetraarylboronate Resorcinarene Macrocycle Synthesis.The chiral (-)-pinanediol tetraarylboronate resorcinarene (72%yield) macrocycle (Fig. 1) was synthesized according to theprocedure described by Lewis et al.25 The structure of thesynthesized TBRM was confirmed by NMR.

Sample Preparation. Stock solutions of TBRM inspectroscopic grade methanol and each amino acid in carbonatebuffer (pH 7.2) were prepared. For the polarity study, anindividual standard solution of the chiral macrocycle (7 lM)was prepared in different solvents. In addition, a series ofdifferent standard solutions of TBRM were prepared inmethanol for determining the fluorescence quantum yield ofthe chiral macrocycle. For the enantiomeric selectivity ofamino acids, mixtures of chiral macrocycle (7 lM) and aminoacid (1 mM) were also prepared in methanol (90%) and water(10%). All standard solutions were made in triplicate. Forchemometrics experiments, a stock solution containing 50 lMof TBRM was prepared in methanol. Twenty mM stocksolutions of each lysine enantiomer were prepared in carbonatebuffer (pH 7.2). From the stock solutions, the final concentra-tion of all calibration and validation solutions contained fixedmacrocycle (7 lM) and amino acid (2 mM) enantiomerconcentrations. The enantiomeric composition of the calibra-tion samples ranged from 0.1 to 1.0 mole fraction while thevalidation samples ranged from 0.05 to 0.95 mole fraction.

Data Analysis. Multivariate data analysis was achieved

using the UnscramblerTM (version 9.1; CAMO, Inc., Corvalis,OR). Partial least squares regression models (PLS-1) weredeveloped from the spectral data of guest–host complexesusing full cross-validation. The models were validated withindependently prepared test solutions containing known moleratios of lysine.

Instrumentation. Steady-state fluorescence measurementswere performed in triplicate at room temperature using a SpexFluorolog-3 spectrofluorometer (model FL3-22TAU3, JobinYvon, Edison, NJ) equipped with a 450 W xenon lamp, doublegrating excitation and emission monochromators, and anR928P photomultiplier tube detector. Excitation and emissionspectra were acquired using a 10 mm quartz fluorometer cellwith excitation bandpass set at 1 nm and emission bandpass at4 nm. The excitation and emission wavelengths weremonitored at 290 and 435 nm, respectively, for the chiralmacrocycle. In addition, absorbance measurements were madein triplicate at room temperature using a UV-3101 PC (UV-VISNIR) scanning spectrophotometer (Shimadzu, Columbia, MD)equipped with a deuterium halogen lamp. The absorbancespectra were recorded in 10 mm quartz cells. Both fluorescenceand absorption spectra were blank subtracted before proceed-ing with the data analysis.

RESULTS AND DISCUSSION

TBRM Spectroscopic Characteristics. Fluorescence spec-troscopic properties, including emission and excitation charac-teristics and quantum yield of TBRM, were investigated. Theinfluence of different solvents on the chiral macrocycle’smicroenvironment was evaluated. Different spectroscopicgrade solvents, including acetonitrile, dimethyl sulfoxide(DMSO), ethyl acetate, methanol, and tetrahydrofuran, wereused in order to study the fluorescence characteristics ofTBRM. Significant shifts in fluorescence spectra of the chiralmacrocycle in the presence of each solvent were observed atthe maximum emission wavelength. The TBRM maximumemission wavelengths in tetrahydrofuran, ethyl acetate, aceto-nitrile, DMSO, and methanol were 325, 350, 390, 410, and 435nm, respectively. In general, these marked shifts in themaximum emission wavelength are indicative of non-covalentinteractions between the solvent molecules and the chiralmacrocycle.

The quantum yield of the TBRM was determined in order toevaluate its fluorescence efficiency. For determination of theTBRM fluorescence quantum yield, L-tryptophan (L-Trp) wasselected as a standard sample since L-Trp absorbs near theexcitation wavelength (kmax ex ¼ 290 nm) of the chiralmacrocycle. As a result, it is expected that the same number ofphotons will be absorbed by both samples. Dilute solutions ofTBRM (1–10 lM) and L-Trp (4–22 lM) were prepared inmethanol and water, respectively. The fluorescence of thestandard and unknown were recorded using the same standardand unknown solutions used for absorbance measurements.

The quantum yield of the chiral macrocycle was calculatedusing the following equation:37

Ux ¼ UstdðGradx=GradstdÞðg2x=g

2stdÞ ð1Þ

where the x and std subscripts denote the chiral macrocycle(unknown) and L-Trp (used as the standard), respectively, U isthe quantum yield, Grad is the slope from the plot of integratedfluorescence intensity versus absorbance, and g is the

FIG. 1. Chemical structure of tetraarylboronate resorcinarene macrocycle(TBRM).

APPLIED SPECTROSCOPY 477

refractive index. The quantum yield of L-Trp has beenpreviously reported in water as 0.14.38 The refractive indicesfor water and methanol are 1.32 and 1.33, respectively. UsingEq. 1, the calculated quantum yield of TBRM at a maximumemission wavelength of 435 nm was 0.10, indicative of arelatively highly fluorescence efficient macrocycle.

Amino Acid Chiral Recognition. The enantiomericselectivity of the chiral macrocycle towards different aminoacids was examined. Spectral differences of TBRM (7 lM)with the investigated amino acids (1 mM) were obtained asillustrated in Fig. 2. Fluorescence intensity (DI) was acquiredby subtracting the emission peak of TBRM from that of theTBRM and amino acid mixture at 435 nm. In general, it wasobserved that the fluorescence intensity of the macrocycle washigher in the presence of L-amino acid enantiomers ascompared to D-amino acids. This is indicative of the higherenantiomeric selectivity of the TBRM towards L-amino acidsin contrast to D-amino acids. However, the opposite trend wasobserved in the fluorescence intensity for TBRM with L-arginine, L-glutamate, and L-proline. Interestingly, the differ-ence in the fluorescence intensity of the macrocycle in thepresence of D- and L-lysine was the highest compared to all theinvestigated amino acids.

Various degrees of enantioselectivity for amino acids wereobserved, as illustrated in Fig. 2. The chiral recognition ofamino acids is based on the formation of diastereomericcomplexes between the enantiomer and the chiral macrocycle,as indicated by differences in the fluorescence emission ofTBRM with amino acid enantiomers. It is observed that therewere significant differences in fluorescence intensity of TBRMwith alanine, leucine, and lysine enantiomers as compared toless evident changes for arginine, aspartate, and asparagineenantiomers. Moreover, all amino acids used in this study donot absorb in the same region as the chiral macrocycle, furthersupporting the idea that the changes in fluorescence intensitywere due to interactions between the macrocycle and aminoacid enantiomers.

As noted above, the enantiomeric selectivity of themacrocycle was the highest towards L-lysine (L-Lys) ascompared to all other amino acids investigated. This highchiral recognition ability of TBRM for lysine can be attributedto the excitation spectra of the macrocycle with lysine in whichthere is not only a peak at 290 nm but also a peak at 350 nm(Fig. 3A), suggesting complex formation with TBRM andlysine. At 290 nm, the excitation intensity of TBRM isdecreased in the presence of D-Lys, while it is increased in the

presence of L-Lys. However, at 350 nm the excitation intensityof TBRM with L-Lys is higher than that of TBRM with D-Lys.An excitation peak around 350 nm was not observed in thespectrum of TBRM with any other amino acids investigated.The peak at 350 nm was investigated by collecting theemission at an excitation of 350 nm (Figure 3B). As a result, nofluorescence emission was observed for the macrocycle in theabsence of amino acid at this excitation wavelength. For themacrocycle and D-Lys (or L-Lys) mixture, however, anemission spectrum with a maximum fluorescence intensity at420 nm was observed. In addition, the emission intensity of themacrocycle with L-Lys was higher than that with D-Lys.

Determination of Enantiomeric Composition. Accuratedetermination of the enantiomeric composition of amino acidsis important, particularly for the pharmaceutical industry,because D-amino acids have been detected at low concentra-tions in humans, which may have an antagonistic biologicalfunction. In addition, deficiencies in L-lysine concentrationshave been reported to result in mental retardation andhyperammonemia.39,40 However, an increase in L-lysineconcentrations can prevent the recurrence of herpes simplexinfections.41

In this phase of our study, fluorescence steady-state-basedmeasurements were used to determine the enantiomericcomposition of chiral molecules. This investigation wasperformed for screening purposes and as a proof of principalfor this fluorescence approach. Relatively high concentrationsof amino acids were employed. However, it is expected that theconcentrations could be significantly reduced upon furtheroptimization. As previously noted, the enantiomeric selectivity

FIG. 2. Fluorescence study of the interaction of a 7 lM TBRM with aminoacids (1 mM) monitored at 435 nm emission wavelength.

FIG. 3. (A) Excitation spectra monitored at 435 nm emission wavelength and(B) emission spectra monitored at 350 nm excitation wavelength of TBRM (7lM) in the absence and presence of D-lysine (1 mM) and L-lysine (1 mM).

478 Volume 62, Number 5, 2008

of the chiral macrocycle was highest with lysine as comparedto other amino acids. To further understand the chiralrecognition ability of the chiral macrocycle towards lysine,determination of the enantiomeric composition was investigat-ed for lysine using partial least squares regression modeling(PLS-1). In fact, multivariate regression modeling allows quickand accurate screening of chiral molecules, and it has beenwidely used for correlating spectral variations with knowncompositional changes.18,34,35,42 According to procedures fromprevious studies, PLS-1 regression modeling was used tocorrelate changes in spectral data of guest–host complexes withenantiomeric composition of samples.

Figure 4A illustrates the emission spectra of differentmixtures containing a fixed concentration of the macrocycle(7 lM) and lysine (2 mM) with varying the enantiomericcomposition of lysine. The samples have a maximum emissionat 420 nm. Spectral variations of the macrocycle were observedwith different enantiomeric compositions of lysine. Again, this

finding is indicative of the formation of diastereomers in thedifferent samples. In addition, this is further confirmed by amean-centered plot (Fig. 4B) obtained by averaging thespectral data of the mixtures (Fig. 4A) and subsequentlysubtracting the average spectrum from each individual samplespectrum on a wavelength-by-wavelength basis.18 Mean-centered plots offer better insight into the spectral variationsof TBRM that occurred with different enantiomeric composi-tions of lysine. It is also interesting to note that the spectral dataof samples with enantiomeric compositions of L-Lys greaterthan 0.5 mol fractions are above the plot’s origin, while thosewith mol fractions less than 0.5 are below the origin, asillustrated in Fig. 4B.

The above findings are also supported by multivariateregression methods. From PLS-1, the correlation coefficient(0.9932), the slope (1.0158), and the offset (0.0168) wereobtained from the calibration regression modeling. A perfectmodel would have a correlation coefficient of 1, a slope of 1,

FIG. 4. (A) Fluorescence spectra of 7 lM macrocycle with 2 mM lysine containing different enantiomeric compositions monitored at 350 nm excitationwavelength. The mol fraction used for L-lysine was: (1) 0.0, (2) 0.1, (3) 0.2, (4) 0.3, (5) 0.4, (6) 0.5, (7) 0.6, (8) 0.7, (9) 0.8, (10) 0.9, and (11) 1.0. (B) Mean-centeredplot of 11 solutions containing the TBRM and lysine. Same mol fractions as in panel (A).

APPLIED SPECTROSCOPY 479

and an offset of 0. The results of the actual mole ratio,predicted mole ratio, and percent relative error obtained fromthe independent test solutions of D-lysine and L-lysine in thevalidation study are summarized in Table I. Root mean squarepercent relative error (RMS%RE) was used to evaluate theoverall prediction ability of the calibration model for theenantiomeric composition determination of lysine. The rootmean square percent relative error is given by the followingequation:

RMS%RE ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

X

ð%REiÞ2

n

s

ð2Þ

where %REi is the percent relative error determined from theknown and predicted values for the ith sample in the validationset, and n is the number of validation samples in the set. FromTable I, it is observed that the prediction of D-lysine(RMS%RE, 5.8%) was better than that for L-lysine(RMS%RE, 12%).

CONCLUSION

The results from the present study demonstrated a successfulinvestigation of a chiral resorcinarene macrocycle and itsselective chiral interactions with amino acid enantiomers. Thechiral macrocycle had a quantum yield of 0.10, indicatingrelatively good fluorescence efficiency. In addition, the chiralrecognition ability of the macrocycle was examined with 14pairs of amino acid enantiomers. Examination of the resultssuggests that the enantiomeric selectivity of the chiralmacrocycle towards lysine was highest as compared to otheramino acids. Moreover, the macrocycle has potential applica-tions for rapid screening of amino acids and can be used as anoff-and-on sensor for D- and L-lysine.

ACKNOWLEDGMENTS

Isiah M. Warner acknowledges the National Science Foundation, theNational Institutes of Health, and the Philip W. West Endowment for theirsupport. Robert M. Strongin also acknowledges the National Institutes ofHealth for support via grant EB002044.

1. B. Buszewski, M. Jezierska-Switala, and S. Kowalska, J. Chromatogr. B,Anal. Technol. Biomed. Life Sci. 792, 279 (2003).

2. K. Imai and Y. Zasshi, J. Pharm. Soc. Jpn. 123, 901 (2003).

3. H. Onouchi, T. Hasegawa, D. Kashiwagi, H. Ishiguro, K. Maeda, and E.Yashima, J. Polym. Sci. Part A: Polym. Chem. 44, 5039 (2006).

4. C. Pena, I. Alfonso, B. Tooth, N. H. Voelcker, and V. Gotor, J. Org. Chem.72, 1924 (2007).

5. M. Ravikumar, S. Prabhakar, and M. Vairamani, Chem. Commun., 392(2007).

6. J. J. Sheng, A. Saxena, and M. W. Duffel, Drug Metab. Dispos. 32, 559(2004).

7. I. Kaneko, N. Yamada, Y. Sakuraba, M. Kamenosono, and S. Tutumi, J.Neurochem. 65, 2585 (1995).

8. J. T. Powell, N. Vine, and M. Crossman, Atherosclerosis 97, 201 (1992).9. R. Shapira and C. H. J. Chou, Biochem. Biophys. Res. Commun. 146,

1342 (1987).10. D. W. Armagrong, M. Gasper, S. H. Lee, J. Zukowski, and N. Ercal,

Chirality 5, 375 (1933).11. W. Lenz, Teratology 38, 203 (1988).12. I. Slama, C. Dufresne, E. Jourdan, F. Fahrat, A. Villet, A. Ravel, C.

Grosset, and E. Peyrin, Anal. Chem. 74, 5205 (2002).13. M. J. Paik, Y. Lee, J. Goto, and K. R. Kim, J. Chromatogr., B: Anal.

Technol. Biomed. Life Sci. 803, 257 (2004).14. H. Matsunaga, Y. Sadakane, and J. Haginaka, Electrophoresis 24, 2442

(2003).15. K. Luo, H. Y. Jiang, J. S. You, Q. X. Xiang, S. J. Guo, J. B. Lan, and R. G.

Xie, Lett. Org. Chem. 3, 363 (2006).16. E. Yashima, C. Yamamoto, and Y. Okamoto, J. Am. Chem. Soc. 118,

4036 (1996).17. M. Sawada, Y. Takai, H. Yamada, J. Nishida, T. Kaneda, R. Arakawa, M.

Okamoto, K. Hirose, T. Tanaka, and K. Naemura, J. Chem. Soc.-PerkinTrans. 2, 701 (1998).

18. S. O. Fakayode, A. A. Williams, M. A. Busch, K. W. Busch, and I. M.Warner, J. Fluorescence 16, 659 (2006).

19. O. Rusin, O. Alpturk, M. He, J. O. Escobedo, S. Jiang, F. Dawan, K. Lian,M. E. McCarroll, I. M. Warner, and R. M. Strongin, J. Fluorescence 14,611 (2004).

20. O. Rusin, V. Kral, J. O. Escobedo, and R. M. Strongin, Org. Lett. 6, 1373(2004).

21. L. Zhu, Z. L. Zhong, and E. V. Anslyn, J. Am. Chem. Soc. 127, 4260(2005).

22. Y. Liu, Y. Song, Y. Chen, X. Q. Li, F. Ding, and R. Q. Zhong, Chem.-Eur.J. 10, 3685 (2004).

23. A. Moghimi, B. Maddah, A. Yari, M. Shamsipur, M. Boostani, M. F.Rastegar, and A. R. Ghaderi, J. Mol. Struct. 752, 68 (2005).

24. J. S. Kim and D. T. Quang, Chem. Rev. 107, 3780 (2007).25. P. T. Lewis, C. J. Davis, M. C. Saraiva, W. D. Treleaven, T. D. McCarley,

and R. M. Strongin, J. Org. Chem. 62, 6110 (1997).26. P. Timmerman, W. Verboom, and D. N. Reinhoudt, Tetrahedron 52, 2663

(1996).27. P. T. Lewis, C. J. Davis, L. A. Cabell, M. He, M. W. Read, M. E.

McCarroll, and R. M. Strongin, Org. Lett. 2, 589 (2000).28. M. He, R. J. Johnson, J. O. Escobedo, P. A. Beck, K. K. Kim, N. N. St.

Luce, C. J. Davis, P. T. Lewis, F. R. Fronczek, B. J. Melancon, A. A. Mrse,W. D. Treleaven, and R. M. Strongin, J. Am. Chem. Soc. 124, 5000(2002).

29. C. J. Davis, P. T. Lewis, M. E. McCarroll, M. W. Read, R. Cueto, and R.M. Strongin, Org. Lett. 1, 331 (1999).

30. T. Z. Zhang and E. V. Anslyn, Org. Lett. 8, 1649 (2006).31. J. Lin, Z.-B. Li, H.-C. Zhang, and L. Pu, Tetrahedron Lett. 45, 103 (2004).32. W. L. Wong, K. H. Huang, P. F. Teng, C. S. Lee, and H. L. Kwong, Chem.

Commun., 384 (2004).33. C. Wolf, S. L. Liu, and B. C. Reinhardt, Chem. Commun., 4242 (2006).34. S. O. Fakayode, M. A. Busch, D. J. Bellert, and K. W. Busch, Analyst

(Cambridge, U.K.) 130, 233 (2005).35. S. O. Fakayode, M. A. Busch, and K. W. Busch, Talanta 68, 1574 (2006).36. C. D. Tran and D. Oliveira, Anal. Biochem. 356, 51 (2006).37. A. T. R. Williams, S. A. Winfield, and J. N. Miller, Analyst (Cambridge,

U.K.) 108, 1067 (1983).38. E. P. Kirby and R. F. Steiner, J. Phys. Chem. 74, 4480 (1970).39. A. E. Awrich, W. J. Stackhouse, J. E. Cantrell, J. H. Patterson, and D.

Rudman, J. Pediatrics 87, 731 (1975).40. O. Simell, J. Perheentupa, J. Rapola, J. K. Visakorpi, and L. E. Eskelin,

Am. J. Med. 59, 229 (1975).41. N. W. Flodin, J. Am. Coll. Nutrition 16, 7 (1997).42. S. O. Fakayode, I. M. Swamidoss, M. A. Busch, and K. W. Busch, Talanta

65, 838 (2005).

TABLE I. Actual and predicted mol fraction of D-lysine and L-lysine forsolutions containing 2 mM lysine and 7 lM macrocycle.

Actual molfraction

D-lysinepredicted

mol fraction

Relativeerror(%)

Actual molfraction

L-lysinepredicted

mol fraction

Relativeerror(%)

0.050 0.049 1.76 0.950 0.951 �0.1100.150 0.157 �4.67 0.850 0.843 0.8200.250 0.230 8.00 0.750 0.770 �2.670.350 0.308 12.0 0.650 0.692 �6.460.450 0.448 0.410 0.550 0.552 �0.3600.550 0.540 1.82 0.450 0.460 �2.220.650 0.589 9.38 0.350 0.411 �17.40.750 0.773 �3.07 0.250 0.227 9.200.850 0.851 �0.120 0.150 0.149 0.6700.950 0.996 �1.68 0.050 0.034 31.6

RMS%RE 5.80 RMS%RE 12.0

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