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6Biomimetic Molecular Recognition Elements for ChemicalSensingJustyn Jaworski

6.1Introduction

6.1.1Overview

In this chapter, we focus on chemically receptive materials that are capable ofselectively binding to specific molecular targets. These materials are biomimeticin nature by way of their adherence to the laws that govern analogous chemicalreceptors found in biological systems. In this chapter, we discuss these lawsand how they apply to the invention of selective biomimetic surface coatings forchemical-sensing systems. Throughout this discussion, specific attention is madeto the theory of molecular recognition as well as categorical spotlights of significantrecent developments in molecular imprinting, supramolecular analytical chemistry,and bioreceptor coating research. In this survey, the achievements, trends, andprospects for improved biomimetic molecular recognition are highlighted.

Within the context of chemical sensing, a material that mimics biological func-tion is highly desired, as biological selectivity is unsurpassed. The main conceptsin achieving selectivity are explored in this chapter by first providing the founda-tional background theory for noncovalent molecular interactions. Subsequent caseexamples in this chapter draw attention to biomimetic materials for chemical sensorcoatings. By learning from the principles provided by nature’s chemical receptors,researchers have imitated those ideas to design selective molecular recognitionelements to allow binding of specific target analytes to the surface of chemical sen-sors. This use of biomimetic receptive materials has led to the rapid increase in thedevelopment of genomics and proteomics screening tools and selective analyticalsystems. In addition, the last decade has seen dramatic improvements in our abilityto selectively detect small-molecule compounds including pesticides, explosives,and other chemicals that may harmfully impact our health through water andfood stocks. While chemical sensing remains a major application of chemicallyreceptive biomimetic materials, it is important to keep in mind the broad impactthat biomimetic molecular recognition has on the world around us. Research in

Biomimetic Approaches for Biomaterials Development, First Edition. Edited by Joao F. Mano. 2012 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2012 by Wiley-VCH Verlag GmbH & Co. KGaA.

118 6 Biomimetic Molecular Recognition Elements for Chemical Sensing

catalysis, separation, and even therapeutics relies heavily on the same principles,and work in these fields is impacted greatly by increasing our understanding ofthe mechanisms of biomimetic recognition. It is the goal of this chapter to providethe core understanding of these principles and to present the current status of thefield in terms of recent advances, perceived limitations, and future prospects forimproving biomimetic surfaces for selective molecular recognition.

6.1.2Biological Chemoreception

Biological molecular recognition is ubiquitous in our daily lives and is a vital part ofmaintaining the functions necessary for sustaining life. Among the various aspectsof molecular recognition, two well-studied biological systems of particular interestto selective biomimetic materials research are the immune response system andthe olfactory/gustatory system.

In the immune system, antibodies provide exceptionally selective binding ca-pabilities in order to counteract the presence of foreign objects, also known asantigens, in the body. The antibodies, produced by plasma cells, are either secretedin order to search for antigens or bound to the membrane of B cells in order to actas cell-based infection sensors. While the structure of most antibodies is generallysimilar, the light chain ends of the large ‘‘Y’’-shaped protein complex contain avariable domain that is responsible for molecular recognition of the antigen andis termed the paratope. The paratope is highly variable allowing for millions ofdifferent amino acid sequences at this region. Slight changes in the sequence ofthe paratope will produce a unique structure that can serve as a receptor (analogousto a lock) for a specific antigen (analogous to a key). The diversity of antibodiesfor specific antigens can therefore be extremely vast, thereby allowing the immunesystem to respond effectively by binding to an unknown antigen entering thebody. This concept of a biological lock and key proposed by Emil Fischer over 100years ago to describe the binding of natural receptors [1] has been a fundamentalstarting point for chemical sensor coating technologies, as the ability to mimic theextraordinary selectivity achievable by antibodies is a highly sought after goal toeliminate the occurrence of false-positive signals, which plague most engineeredsensing platforms.

Another biomimetic approach to achieving surfaces capable of molecular recogni-tion is borrowed from our body’s own chemical-sensing systems of smell (olfactorysystem) and taste (gustatory system). The key components of these systems areG-protein-coupled receptors that are bound to the membrane of cells and activatethe opening of ion channels to a certain degree upon analyte binding, dependingon the extent of cyclic AMP formation induced by the extent of analyte binding.In this respect, the olfactory and gustatory system are presently believed to achieverecognition of molecules in a different way than that of the immune system. Whilethe immune system has a vast number of potential receptors for different antigens,the olfactory systems possess approximately 1000 different receptors (of which notall are functional). In a set of paradigm-shifting experiments by Buck and Axel,

6.1 Introduction 119

each odorant cell was found to possess unique G-protein-coupled odorant receptorsthat demonstrated the ability to be activated to different degrees by interaction withdifferent target odorants. As opposed to the idea that one unique receptor existsfor a unique odorant molecule, findings have demonstrated that a single receptorcan bind to multiple different odorants, thereby creating the concept of a complexcross-reactive array. This combinatorial interaction code offers an explanation forour ability to sense thousands of diverse smells despite having a far smaller num-ber of different receptors. Currently, there is no theory that completely explainsolfaction; however, it is widely believed that different chemical features of odorantchemicals are perceived by different receptors according to the characteristic shapesof the molecules, termed shape theory, or due to the molecular vibrational frequencyof the odorant as outlined in the vibration theory [2]. The resulting interactionsbetween an odorant and several different receptors occur to varying degrees, whichcreate a unique odor ‘‘fingerprint’’ that is processed as a specified smell attributedto the chemical. Importantly, this cross-reactive nature of the olfactory systemhas inspired researchers to implement pattern-recognition-based approaches tobiomimetic chemical-sensing system with great success.

6.1.3Host–Guest Interactions

In order to make use of the evolutionary advantage of nature, researchers havetried to identify and imitate the underlying mechanisms of biological molecularrecognition. In the field of host–guest chemistry, particular importance is givento creating complementarity between the host (receptor) and the guest (analyte)in terms of size, shape, and functionality in order to maintain selectivity inmolecular recognition. The pioneering works by Pedersen, Lehn, and Cram inthe development and understanding of synthetic receptors led to their receipt ofthe 1987 Nobel Prize for chemistry. The impact of their works led to the use ofspecific control of receptor structures to limit their association with different targetanalytes via noncovalent interactions. This form of molecular complementarity isanalogous to the original mechanism proposed for biological recognition akin tolock and key binding, which has become widely accepted as an effective meansof molecular recognition. Exceptions and distinctions to this theory have resultedin dividing the mechanism into several model systems. The following sectionshighlight the different aspects of three such model systems: lock and key, inducedfit, and preexisting equilibrium (Figure 6.1). The divergence among these modelsarises when the receptor is entirely rigid, conformable between two states, orconsiderably flexible, respectively.

6.1.3.1 Lock and KeyTo understand molecular recognition in the case of lock and key binding asproposed by Emil Fisher in 1894, we must take the case of a purely rigid receptorand target. In terms of selectivity, the predefined binding pocket may accommodateonly specific shapes and functionalities of guest molecules. In addition to shape


L

L

L L L

L

L

L

Induced fit

Lock and key

Preexistingequilibrium

Figure 6.1 Model mechanisms for receptor-based recognition of ligands, L.

and functionality, the size of the receptor site essentially restricts the mismatch oflarger competing molecules. However, competing analytes possessing molecularsimilarity to the guest but with smaller sizes than the intended target maystill exhibit binding to the predefined receptor site. In practice, this biomimeticmechanism is widely used in the design of macrocycle systems for molecularsensing and separation. Moreover, the constrained nature of the macrocycles hasproven an enhancement in binding stability due to the preorganized rigidity ofthe receptor framework. This is attributed to both the lack of entropic loss thatwould be exhibited if using flexible or unconstrained receptors and the avidityeffect requiring multiple binding sites to simultaneously break in order to releasethe guest.

6.1.3.2 Induced FitFormerly proposed by Daniel Koshland in 1958 as applicable to certainenzyme–substrate interactions, induced fit proposes a mechanism for increasingthe stability of correct host–guest interactions in conformable systems. Illustra-tively, consider a system in which the binding site is slightly larger than the size ofthe intended target. Initially, weak binding interactions occur between the host siteand the guest molecule that stimulate the host to undergo a small conformationalchange resulting in the formation of stronger molecular recognition events withthe correct target guest. The energy cost required for the host site to conformincreases quickly for greater bending, thereby requiring only small mismatchdifference to effectively enhance the selectivity [3]. If a smaller competing moleculeenters the host cavity, then the amount of binding required to fully interact is largerthan that required for the intended target molecule and hence more energeticallyunfavorable. Stabilization of intramolecular and intermolecular bonds helps toensure the specificity of the host–guest system. The selectivity enhancement ofthis approach is used widely throughout biological systems in which induced fitdeformations have been observed to be as large as tens of angstroms. Severalbiomimetic molecular recognition systems have made use of this biologically

6.1 Introduction 121

inspired mechanism by utilizing elements that induce conformational changes forenhanced binding to achieve molecular tweezers, switches, and receptor systems.

6.1.3.3 Preexisting Equilibrium ModelThe work of Foote and Milstein challenged the assumption that the antibody’sbinding site has a single structure. In their pivotal experiments, they supportedthe existence of equilibrium between several antibody conformations, in whichthe guest molecule bound preferentially to a single structural isomer [4]. Thepreexisting equilibrium model (PEEM), ultimately based on the energy landscapeand molecular dynamics theory of protein folding and binding, proposes that thenatural state of a receptor exhibits a variety of different binding site conformations[5–7]. Throughout these conformational fluctuations, a guest molecule will bindto an active conformation of the host thereby biasing the equilibrium towardthe bound conformation [8]. Work by James et al. [9, 10] demonstrated thatdifferent conformations of unbound antibody can bind to structurally differentguest molecules, in which one binding mode exhibits a shallow groove, whileanother form displays a deep hole. This type of conformational diversity results incross-reactivity, which attributes to multispecificity or, in other cases, nonspecificinteractions. In another example, researchers examined an antibody capable ofbinding various sets of DNA [11] to find that the binding site is fundamentallyunstructured in the absence of the guest but after binding the host site becomesstructurally ordered. It is believed that the conformational flexibility allows thehost to dramatically modify the size and shape of the binding site to enhancecomplementarity to other guest molecules, thereby accounting for multispecificity[12]. Schultz and coworkers [13] have found that mutations in such flexibledomains can result in a rigid binding site with optimal guest complementaritycapable of exhibiting much higher-affinity binding (30 000 times) than their flexiblecounterparts. In addition, they found that locking the antibody into the fixedconfirmation resulted in the introduction of structural features that interfered withnonguest interactions thereby rendering increased specificity.

6.1.4Biomimetic Surfaces for Molecular Recognition

It may not be surprising that in living systems, most molecular recognition eventstake place at interfacial environments [14]. On the basis of the biological reactionmechanisms described above, researchers have explored a number of biomimeticmaterial designs in order to achieve effective sensor coatings. Perhaps, the oldestengineering of molecular recognition is based on the lock and key mechanismthrough the use of molecular imprinting. As discussed later, this field utilizespolymeric surfaces with complementary features to target guests in order toachieve surfaces capable of binding targets with selectivity among enantiomers.Supramolecular chemistry has also provided foundational understanding of thelock and key mechanism of binding through the design and analysis of structurallyconstrained macrocyclic receptors for target-specific binding.


Recently, supramolecular chemists have also applied preexisting equilibriumto analogous binders with promiscuous cross-reactivity, in which a complex canstill be formed even if the guest molecule does not perfectly match the host site.While the quality of molecular recognition in terms of selectivity is compromised,researchers take advantage of the multispecific binding in order to create chemicalsensor coatings, which are capable of recognition of distinct binding patterns ina biomimetic manner, as described previously. In such cases, a flexible recog-nition element, which does not exhibit perfect structural matching, may provideadvantages over rigid host receptors in order to increase the diversity of specificity.Ultimately, these biomimetic systems depend on the flexibility and molecularsimilarity of the host and guest, respectively.

As for the induced fit model of binding, researchers are actively pursuing thedesign of receptive materials that exploit the free energy barrier imposed by theconformational change to create a threshold for discriminating target binders fromnontarget binders. This biomimetic concept utilizes structural mismatches betweenthe host and guest to enhance recognition specificity among a noisy backgroundof competing chemicals [15]. As this is highly applicable to sensor coating design,researchers have made use of biopolymer receptors engineered to contain keystructural mismatches. These mismatches can reduce the initial interaction ofboth the correct and incorrect targets; however, the inhibitory effect on the rate ofincorrect target binding is much more significant. As a result, these biomimeticreceptor systems have demonstrated astonishing selectivity as sensor coatings.

In addition to designing selectivity of the receptive elements using the strategiesmentioned above, other aspects must be engineered into biomimetic surfacesfor sensing purposes. During the past several decades, extensive research onmolecular recognition at interfaces has been performed using monolayer andlipid assemblies to identify the critical surface effects to be considered in orderto maintain efficiency of the selectivity elements [14]. Biological systems haveoptimized their molecular recognition formats over eons of selection and evolution.To accommodate sufficient space in biological systems, for instance, B cells areknown to clear the presence of surface groups in the area of thousands ofangstroms surrounding attached antibodies [16]. Biomimetic sensor coatings mustalso be designed to minimize the occurrence of competition or steric hindranceassociated with surface saturation of receptive motifs. Surface attachment itself is animportant consideration that can often become problematic, as it depends highly onthe material of the sensing platform. Depending on the transduction mechanism,spacer sequences may be placed between the sensing surface and the receptivemotif to deter surface fouling effects or to increase hydration [17]. Sufficient spacingbetween the active site and the surface can also ensure effective presentation of thereceptive motif. The functional site of active receptors must be properly displayedin order to achieve useful molecular recognition. Antibodies, if randomly attachedto a sensing surface, will have a fraction of the recognition sites inaccessible. Inorder to accommodate exposure of the active binding site, attachment approachesto control the directionality of surface receptors generally utilize site-specificchemical modifications (i.e., biotin) or functionalization by affinity tag binding.

6.2 Theory of Molecular Recognition 123

As an example, researchers have also utilized the natural Fc binder, protein A, toanchor antibodies to a sensing surface in an orientation that provides full exposureof the antigen binding site [18]. Maximizing the accessibility of the recognitionsite by controlling host site orientation is often overlooked, although it is a majorconsideration for the effective design of biomimetic sensing surfaces.

6.2Theory of Molecular Recognition

6.2.1Foundation of Molecular Recognition

As we now know, molecular recognition is an essential component of biologicalsystems. While often referred to as a binding event, this is a bit of an oversim-plification. One of the pioneers of host–guest chemistry, Jean-Marie Lehn, hasdefined molecular recognition as involving both binding and selection throughstructurally well-defined intermolecular interactions [19]. Thus, complementarityin size, shape, and functionality akin to that described in the lock and key modelis fundamental to achieving molecular recognition [20]. Throughout biological andbiomimetic systems, receptive sites often present themselves as a concave surfaceladen with acids, bases, amino acids, metal ions, and so on to offer noncovalentinteractions in a structurally controlled space to receive the complementary molec-ular guest [21]. From a steric viewpoint, a complementary guest implies that thevan der Waals radii of the guest molecule’s atoms cannot go beyond that of the hostand correspondingly should serve to fill the host cavity as densely as possible [22].Although complete structural complementarity may not be required for molecularrecognition [23], this section considers such molecular recognition system in orderto begin to explore the elements and thermodynamics of the binding event as wellas the means of analyzing and quantifying the host–guest molecular interaction.

6.2.2Noncovalent Interactions

Effective functional complementarity between a recognition site and a guestmolecule relies on the occurrence of favorable noncovalent interactions. Formsof noncovalent interactions include hydrogen bonding, hydrophobic forces, metalcoordination, van der Waals forces, aromatic interactions, and electrostatic forces.Given a target molecule, there exist a large number of possible configurations ofhost receptors that can facilitate recognition through these interactions. Spatialoptimization, as mentioned, can be designed to accommodate maximization ofattractive dispersion forces. However, even if surface complementarity is low,effective molecular recognition elements can still be designed through electrostaticcomplementarity and hydrophobic complementarity. The presence of complemen-tarity of the molecular electrostatic potential relies in essence on maximization


of the ionic and polar interactions, such as hydrogen bonds, for a given targetmolecule. Long-range electrostatic forces, such as coulomb forces, may lead tostrong accumulative binding and can contribute extensively to the overall bindingenergy [24]. It is noteworthy that the neighboring charges of individual contactatoms in a host–guest complex need not necessarily show complementarity be-cause from a coulombic aspect, it is the property of the entire independent hostand guest systems that is most dominant [25]. As such, the potential in a host cavitywill benefit from being opposite in sign to that attributed to a guest molecule.While significant, these coulomb forces can diminish other important interactionsresponsible for precise molecular recognition [24]. Such interactions may includemetal coordination, aromatic interactions, and hydrogen bonding. Metal bindingcan subtly change or constrain the confirmation of a host site and even confermetal ion specificity and affinity. The existence of aromatic interactions is everpresent in biologic recognition systems, as pi–pi stacking is a major stabilizingfactor among binding events, particularly in those involving DNA and RNA. As forhydrogen bonding, it is essentially electrostatic in nature and is a highly importantinteraction necessary for tuning the selectivity of the recognition site.

The hydrogen bond is effectively an attractive interaction between a hydrogenacceptor and a hydrogen atom from a functional motif that is more electronegativethan hydrogen, such as fluorine, oxygen, and nitrogen. Atoms, groups of atoms,anions, pi bonds, metal complexes, and even pi electrons from aromatic ringsmay act as hydrogen bond acceptors [26]. The strength of the interaction dependson the nature of the bonding components, for example, weak polar interactionsmay involve pi-system acceptor groups and CH donor groups [27]. In addition,the interaction has directional preference and usually becomes stronger as thebond angle approaches linear. As a result, researchers can manipulate the locationand charge of the hydrogen bonding sites to change the capabilities of syntheticreceptors to function in different solvents [28].

Aromatic components can offer a variety of bonding interactions, including theformation of hydrogen bonds via their pi system as described above, pi–pi interac-tions, and cation–pi interactions, which act as major forces to stabilize host–guestassociations. Aromatic groups are capable of forming T-shaped arrangementsallowing edge-to-face interactions as well as parallel-displaced arrangements allow-ing aromatic pi–pi stacking interactions [29]. High edge-to-face attraction is foundwhen partial positively charged H atoms interact with aromatic rings enhanced bypi-electron density. Aromatic partners may also form charge-transfer complexesunder parallel alignments. These pi–pi stacking interactions are affected by theelectrostatics of component atoms in addition to the geometric alignment of molec-ular dipoles. These contributing factors determining how the pi systems interactand thus affect the strength of the pi–pi interaction based on the stacking ge-ometry. Perhaps the strongest interaction arising from aromatics occurs betweena cation in conjunction with the aromatic face of the pi system [30]. Cation–piinteractions, which have been show to contribute to ion selectivity in potassiumchannels [31], depend critically on the aromatic system and charge of the cation. Amore positively charged cation will interact more strongly with the electron-rich pi


system resulting in a stronger molecular interaction. As such, the nature of the aro-matic components is important in determining the strength of the interaction, withstronger cation–pi binding occurring when electron-donating groups are present.In a related manner, one may consider the formation of anion–pi interactions inaromatic systems if strong electron-withdrawing components are placed within thepi system [32]. While biological substrates and cofactors have predominantly beenanionic, researchers have recently explored this area to implement the recognitionof specific anions for applications in chemical-sensing materials [33]. In contrast tothe true aryl pi system interaction seen with cations, anion binding is consideredto be primarily due to local dipoles induced by the added substituents and not dueto the attraction with the aromatic pi system [34].

Finally, hydrophobic complementarity, which relates to the tendency of nonpolargroups to associate in an aqueous medium, also plays a critical role in molecularrecognition. In general, this phenomenon results in the minimization of contactbetween nonpolar and polar regions. The burial of charged groups in a nonpolarrecognition site, for instance, is highly unfavorable. This manifestation of the‘‘similis simili gaudet’’ principle offers that association of similar species (i.e.,ionic, polar, or nonpolar components) contributes to a more stable system ascompared to association of dissimilar components [35]. Hydrophobic interactionsby themselves may be viewed as amorphous and nonselective; however, they can beused in conjunction with spatial restriction to confer stability and limited specificity.With the support of polar interactions, which have optimal distance and directionalrequirements, highly selective molecular recognition is achieved [36] (Table 6.1).

6.2.3Thermodynamics of the Molecular Recognition Event

Molecular recognition obeys the same rules as any spontaneous process andtherefore will occur given a negative change in free binding energy (�G). One mayhope to engineer a host receptor for a given target molecule such that the free

Table 6.1 Strengths of representative bonds and interactions in molecular recognitionprocesses.

Magnitude of interaction energies

Type of noncovalent interaction Strength of interaction (kJ mol–1)Hydrogen bonding 10–65Coulomb interaction 250π–π <50Cation–π 5–80Ion–dipole 50–200Dipole–dipole 5–50van der Waals <5

Source: Adapted from Hoeben et al. [37].


energy of binding is favorable; however, this remains a difficult task. To attemptsuch an achievement, it is important to first demystify the free energy of bindingby looking at the components of enthalpy (�H) and entropy (−T�S). These twocomponents are inherently related to the structure of the host and guest as wellas the solvation effects of the system, thereby making the design of a molecularrecognition element a daunting task [38]. One design factor that is widely acceptedis the burial of a nonpolar site within the recognition complex, as this favorablefree energy contribution of desolvation is conventionally attributable to a gain inthe entropy due to the randomized release of solvent and/or ions [36]. While thisexists, nonpolar groups also directly provide an enthalpic driving force for bindingby a nonclassical hydrophobic effect [29]. The favorable enthalpy term arisesduring complexation from at least two factors. The first enthalpic contributiondue to nonpolar burial is caused by an increase in solvent cohesive interactions(hydrogen bonding) between water molecules on release of the surface-solvatingwater molecules into the bulk. This favorable outcome is directly a factor of nonpolarsurfaces participating in fewer strong hydrogen bonds. The second aspect of thisreplacement is that nonpolar components will have less favorable interactionswith water as compared to after complexation in which attractive dispersion forcesbetween nonpolar components at the guest–host interface will promote stabilitydue to similar polarizabilities [29]. When we consider polar binding sites at thesurface of the host and guest molecule, there may exist a negative contributionto the binding enthalpy attributable to desolvation of these polar binding sites.In such circumstances, it may be advantageous to design hydrogen bond donorsites with close proximity to the host recognition site in order to spatially limitits solvation capabilities [28]. Yet another alternative strategy may be through thedesign of CH/pi hydrogen binding interactions in the host–guest system, as thisis expected to work well in a variety of solvent systems [39].

Solvation effects as well as structural effects are often interconnected phenom-ena when considering the thermodynamic aspects of molecular recognition. Forinstance, a host–guest complex akin to the lock and key model provides significantstructural complementarity, which is more effective than a loose complex in termsof maximizing the strongly distance-dependent attractive van der Waals interactions[40]. Conversely, a cavity larger than the guest molecule will provide significantdesolvation entropy on binding. In both circumstances, an additional entropicterm must be accounted for that is associated with the initial state of the host andguest relative to the freezing of bond rotations as well as loss in translational androtational freedom of the interaction component on complexation. This inherententropic cost is hard to avoid but may be minimized by the use of constrainedrecognition sites. Conversely, positively cooperative interactions exist throughoutbiology; for instance, in the streptavidin–biotin complex, where structural tighten-ing, which unfavorably decreases conformational entropy, results in the formationof additional interactions with shorter distances, which is enthalpically favorable,thereby providing a net benefit to the molecular recognition event [41]. Alternatively,a partially bound state model also exists to explain such net losses in free energyon complexation, in which the balance shifts from an entropically favorable state


to a less mobile enthalpically favorable state. This model is proposed to accountfor mechanisms of enthalpic chelate effect and enthalpy–entropy compensationfound in certain biomolecular recognition events [42].

6.2.4Putting a Figure of Merit on Molecular Recognition

Quantification of molecular recognition relies on identifying the preferential affin-ity of a host receptor for a particular guest molecule as compared to nonintendedcompeting guest molecules. This affinity, generally quantified as the bindingconstant, relates the ratio of unbound and bound molecules at a state of equilib-rium between binding and dissociation. Particularly for bimolecular association,the ratio of host–guest complex concentration to the product of host and guestconcentrations provides the association constant, Ka, while the inverse representsthe dissociation constant, Kd, which is commonly used to represent the affinity.Because the change in Gibbs free energy is related to the equilibrium associa-tion constant, �G = RTlnKa, the thermodynamic parameters of the associationevent may be calculated. In order to measure the binding constant for such abimolecular recognition event, one must find a way to measure the amount ofcomplex formed over a range of initial guest concentrations. Experimentally, itis possible to identify the host–guest complex concentration as a function of theinitial concentration by various quantitative techniques including UV–vis and fluo-rescence spectroscopy, nuclear magnetic resonance spectroscopy, surface plasmonresonance (SPR), Raman spectroscopy, and isothermal titration calorimetry. Us-ing approximations and measuring the equilibrium concentration of host–guestcomplex formed over a range of starting guest concentrations, a binding isothermplot can be obtained in order to identify Ka of the molecular recognition event.Commonly considered the gold standard in accurate measurement of the trueequilibrium data, isothermal titration calorimetry (ITC) directly determines theheat flow on host–guest complexation with which we can measure the enthalpy,binding affinity, and the binding stoichiometry of the interacting system. Thisdata is subsequently used to calculate the entropy and Gibbs free energy of thesystem. Generally, this approach works well for recognition events with bindingconstants in the range of millimolar to nanomolar. Other techniques, includingSPR detection systems, measure the kinetic rates of association and dissociationfrom which the binding constant can be determined; however, these techniquesare arguably less reliable. In general, though, thermodynamic values are inher-ently sensitive to preparation conditions such as salt concentrations and buffer.For instance, weak solvation of hydrogen bonding sites in certain nonpolar or-ganic solvent systems can cause strong enthalpic driving force for binding, whichmay not be realistic to a biomimetic chemical-sensing surface. As such, subtleexperimental variations can make obtaining consistent thermodynamic valuesof enthalpy and entropy a challenge. In contrast, observing the binding con-stant and hence the net overall free energy change remains a more dependable


strategy to quantify the molecular recognition event without deconvoluting theassociated structural changes, long-range interactions, and desolvation–solvationconsiderations.

6.2.5Multiple Interactions: Avidity and Cooperativity

The above-mentioned case of a simple biomolecular recognition event providesa relatively easy scenario of a single host interacting with a single guest. Inmany biological systems, particularly protein–protein interactions, a host–guestinteraction may occur via more than one recognition event. For instance, if boththe host and guest sites exist as a dimer, the initial binding event brings thehost and guest in close proximity to facilitate the subsequent incidence of bindingat the other site. This occurrence of multivalent binding provides an additionalstabilizing component to increasing the host–guest interaction by lowering the offrate of the complete system. This effect, known as avidity, is often used to enhancethe performance of biomimetic receptive surfaces for chemical sensing and isa natural component of antibody-based recognition. As compared to the case ofdissociation from a monovalent interaction, multivalent antibodies are less likelyto diffuse away from an antigen when a single site dissociates, thereby makingit more likely for the bond to reassociate. The multiple simultaneous interactionsprovided by avidity thereby provide a strengthening mechanism for molecularinteractions that affect the affinity of the entire system [43]. Importantly, theaffinity of the entire system cannot quantitatively be determined as just a collectionof the individual monovalent interactions, as there must exist a linker betweenmultimeric binding sites. As such, extensive design considerations must be takeninto account to optimize the linker in terms of rigidity, length, orientation, andproximity between binding sites, depending on the characteristics of the system[44].

Another phenomenon that may occur when more than one binding site existson a receptor is cooperativity, which can arise from conformational changes orlong-range interactions as a result of an initial binding event [15]. Specifically,if there is an increase in the binding affinity of subsequent guest molecules asthe result of prior guest binding, then the receptor exhibits positive cooperativity.Conversely, if the binding of the first guest molecules causes a decrease in affinityfor the incoming guests, then it results in negative cooperativity. Assessing theeffectiveness of these systems requires a more complex formulation than thatpreviously described for the case of simple bimolecular interactions; however,receptor–target models accounting for components of multivalency and cooper-ativity have been proposed [15, 45]. The use of positive cooperative recognitionin chemical sensing has been explored as a viable molecular recognition designstrategy, and it is appearing with increased prevalence in the biomimetic receptorcommunity for the creation of high-affinity interactions via binding-induced strainand folding [46, 47].

6.3 Molecularly Imprinted Polymers 129

6.3Molecularly Imprinted Polymers

6.3.1A Brief History of Molecular Imprinting

From our prior discussion, we see that biology is replete with molecular recog-nition events based on host–guest complementarity in size, shape, and chemicalfunctionality. Biomimetic recognition systems may utilize these same principlesin the creation of synthetic chemical receptors, which may have an increased sta-bility than their biomolecular counterparts. The use of molecular-imprinting-basedbiomimetic sensors thereby provides an attractive alternative. The concept ofmolecular imprinting has existed for many years as a technique exploited throughsilica matrices, as identified by the Soviet chemist M.V. Polyakov in 1931 [48].Four decades later, the molecular imprinting technique was implemented in or-ganic polymers to create surfaces with predetermined target selectivity [49, 50]. Ingeneral, predetermined target template molecules are present during the castingstage and removed to provide an imprinted cavity in the molecularly imprintedpolymer (MIP). The MIP then contains a recognition site available for subsequentinteraction with the target molecule [51]. The use of molecular imprinting receivedincreased attention in the area of biomimetic receptive surfaces after the creationof theophylline-recognizing MIPs that showed strong binding and cross-reactivityprofiles similar to those of antibodies [52]. The ability to use molecular imprintingfor selective molecular recognitions on a synthetic surface continues to be of inter-est in chemical sensor coating research as selectivity remains the key component inattaining effective detection systems. In addition, the rigidity of the polymer matrixand tolerance to extreme environments allow these surfaces to remain stable inpractical scenarios.

6.3.2Strategies for the Formation of Molecularly Imprinted Polymers

MIPs can be created with high-affinity binding sites for a target molecule by twodifferent strategies: self-assembly and preorganization (Figure 6.2). Generally, thesestrategies utilize covalent/noncovalent preassociation complexes formed betweena template molecule and the proper monomers to provide complementary size,shape, and chemical functionality [53]. In the self-assembly approach of molecularimprinting, intermolecular noncovalent interactions are formed between monomerprecursors and the template molecule. The choice of monomer is important, asthis dictates the complex host–guest binding interactions that will be present inthe final MIP. On polymerization, the shape and distance of these interactions arestructurally constrained depending on the extent of cross-linking. The templateis then extracted and the shape of the complementary site is maintained in thepolymer matrix, with the arrangement of the functional groups optimized toprovide a recognition site for the intended target. In the preorganized molecular


Self-assembledtemplate

Preorganizedtemplate synthesis

Polymerization

Template removed byChemical cleavage

Templateextraction

Figure 6.2 General strategy for the formation of molecular imprinted polymers by eitherusing a template capable of self-assembling with the polymerizable monomers or synthesiz-ing a polymerizable template.

imprinting approach, monomers are linked to the template molecule via cleavablecovalent bonds before polymerizing the polymer matrix. After polymerization, thetemplate molecule is rigidly locked into the MIP. Cleavage of the bonds holdingthe template allows its extraction from the polymer matrix, resulting in a polymericcavity with recognition site complementarity. Combinations of these strategiesutilize a covalent template–monomer complex for imprinting, but on cleavage andremoval, an entirely noncovalent interaction is used for binding [54]. This approachprovides a more homogeneous recognition site with faster binding kinetics thanthe conventional covalent approach [55].

6.3.3Polymer Matrix Design

After deciding on a self-assembly or preorganization approach, the polymeric sys-tem must be selected. The choice of monomers and solvent are critical designparameters in achieving an effective MIP. It has become a general rule that themolecular recognition capability of an MIP is best when operating in the same


solvent as that used during polymerization [56]. The use of acrylic and vinylicmonomers offers a variety of functional motifs for noncovalent template interac-tion, and the monomers are thus widely implemented when designing a polymermatrix based on the self-assembly strategy [57]. In the preorganization approach,there exists an intrinsic limitation in the type of template target molecule that canbe imprinted. Since the process requires covalent attachment, the template must beprefabricated into a polymerizable derivative of the intended target molecule. Clas-sically, this preorganization approach has utilized template–monomer complexesformed by carboxylic ester linkages, boronate esters, ether linkages, imines, and ke-tal bonds, among others [53, 58, 59]. Despite the limitation in the functional groupsthat can be imprinted using the preorganization approach, effective MIP designshave demonstrated highly selective molecular recognition capabilities [60]. Thishas been attributed to the distinct bonding between the template and monomerduring polymerization, which results in a higher uniformity of the recognitionsites as compared to the self-assembly approach [61]. The self-assembly approach,however, is more mimetic of antibody-based recognition, in that noncovalent bondsdominate the recognition event.

In addition to having an appropriately functional monomer, it has become ageneral rule that the molecular recognition capability of an MIP is best when oper-ating in the same solvent used during polymerization [56]. The solvent’s purposeis to both dissolve the components and generate a highly porous structure to allowtransport of the template and later provide access to the recognition site. Fromthe Flory–Huggins solution theory, a thermodynamically good solvent will haveincreased polymer–solvent interactions, which may create more porous polymericstructures with higher specific surface areas as compared to a thermodynamicallypoor solvent [62]. In addition, the solvent must not hinder the formation of the neces-sary template–monomer interactions. Nonpolar solvents are generally used in non-covalent imprinting to promote the formation of template–monomer complexes.Water, often believed to prohibit the formation of template–monomer interactions,is usually replaced with perfluorocarbons [63]. Typical solvents include acetonitrile,chloroform, and toluene with a monomer/solvent ratio of 3 : 4 by volume [64].Aside from the monomer and solvent, researchers have used other components toimprove the design of the polymer matrix. Particularly, researchers have createdcleavable sacrificial spacers, which are covalently linked between the monomersand template molecule, to reduce steric hindrance in the recognition site. Withthese elements in place, the formation of a structurally defined stable polymericframework can be achieved through effective cross-linking and template removalto create a biomimetic receptive MIP or as it is often referred to a ‘‘plastic antibody.’’

6.3.4Cross-Linking and Polymerization Approaches

The most commonly used polymerization method for the formation of MIPs is thefree radical vinyl polymerization [65]. As mentioned above, there are a wide varietyof monomers available for this polymerization scheme with diverse chemical


structures and functionality. In addition, these reactions have the added benefitof occurring under mild reaction conditions and are not easily susceptible toimpurities. One important caveat is that different vinyl groups may be incorporatedat different rates in the polymeric system depending on the functionality. Analternative, yet still successful, approach of step-growth polymerization has beenused by Dickert et al. to make polyurethane-based MIPs with biomimetic recogni-tion capabilities toward sensing a variety of templates including vapor targets [66],oils [67], and even whole cells [68, 69]. Among the polymerization systems, oneparameter that seems most pertinent is the ratios of monomer, cross-linker, andtemplate, which is typically 8 : 40 : 1 respectively [64]. Often in noncovalent MIPs,the types of monomer implemented and their ratios are varied using automationin order to optimize the recognition sites formed in the MIP [70, 71]. Importantlythere also remains an optimum cross-linking density for achieving selectivity,which will depend implicitly on the template as well as the matrix design strategy[72]. For example, attempts to make MIPs for larger biomolecules such as proteinsand whole cells have been found to be more successful if using a lower cross-linkingdensity as compared to MIPs for small molecules [64]. In principle, the type anddensity of cross-linker used is important in determining the stability of the bindingsite as well as the mechanical properties and porosity of the entire polymer matrix.

Another key point in the formation of MIPs is the use of initiators for free radicalpolymerization. In such systems using noncovalent MIP formation, polymerizationstrategies have been developed that allow the initiator concentration and temper-ature to be extremely low since hydrogen bonds forming the template–monomercomplex are thermally less stable [72]. In such instances, photochemically activeinitiators are favored since they operate well at low temperatures. In contrast, ifthe template molecule is photosensitive, then a thermally triggered initiator maybe preferred.

6.3.5Template Extraction

After the formation of a constrained recognition domain by polymerization aroundthe template molecule, removal of the template is necessary for use in ensuingmolecular recognition. The molecular recognition sites are created in the polymercavity by properly located functional groups that provide complementary interac-tions with the intended target molecule [73]. For removal of template from MIPsformed by preorganization, the process may require harsh conditions for templatedesorption to break the reversible covalent bonds. Covalent-based imprinting istherefore generally more successful if the bonds used between the functionalgroup and template are cleavable under relatively mild conditions [54]. By chemicalcleavage of the supporting covalent bonds, the template is released from the MIPand the corresponding functional groups located in the recognition site cavity arefree for future interactions [54]. In some circumstances, the embedded cavity isformed by a combination of the covalent and noncovalent approaches. In this


approach, sacrificial spacer groups, used to improve subsequent noncovalent bind-ing geometries, are cleaved and eliminated during template extraction [65]. In thecase of MIPs formed by purely noncovalent interactions, the template is removedfrom the embedded recognition site in a much easier manner. Generally, this isaccomplished by several extraction cycles using a simple solvent system such asmethanol/acetic acid followed by methanol and afterward drying under vacuum[64].

In some instances, it has been found that target molecules unintentionallyremain in the MIP after the initial extraction step despite thorough washing.Larger templates are usually problematic, as they can easily become trappedin the polymeric matrices. A biomimetic endeavor to overcome this problememploys a fragment or the key epitope of the target analyte as the templatestructure [74, 75]. This approach is parallel to the immune system in whichbiomolecular recognition events focus on recognizing key features of the antigenrather than the entire surface of a protein. Another difficulty related to the templateextraction concern may take place during the analytical use of the MIP. Aftertarget exposure, removal of the target from the recognition site may be problematicregardless of continuous washing. To compensate for this predicament, it hasproven effective to use template molecules that are analogous to the intended targetanalyte; however, this enhancement in sensitivity may be at the cost of selectivity[76].

6.3.6Limitations and Areas for Improvement

While MIPs provide the advantages of being made from chemically and physicallystable materials and allowing tailor-made recognition sites with high specificity,there are still areas for improvement. The practical use of MIPs as biomimeticreceptive surfaces may require exposure to aqueous buffer. As these polymersare originally imprinted in nonpolar solvents, aqueous environments can alterthe selectivity of the recognition sites, hence limiting the use to nonpolar organicsolvents [64]. To alleviate this problem, researchers have proposed the use of metalcomplexes and covalent imprinting techniques in polar solvents to promote theformation of strong bonds between the polymer and template target molecule[72, 77]. Another inherent problem with MIP is the randomized placement ofthe template molecule through the system during the polymerization process.After extraction of arbitrarily oriented templates, a heterogeneous populationof binding sites exists with varying affinities for the intended target molecule.Unfortunately, the percentage of high-affinity sites in a noncovalently imprintedpolymer is usually less than 1% of the binding sites [64]. There is evidence thatcovalent imprinting can provide a more homogeneous population of bindingsites [61]. Improvements in these areas are still thriving areas of research, soare the biomimetic recognition capabilities of MIP-sensing surfaces to detectlarge biomolecules, which will prove valuable in advancing medical diagnosticsystems.


6.4Supramolecular Chemistry

6.4.1Introduction

Supramolecular chemistry has been fundamental in increasing our understand-ing of molecular recognition. This section focuses on biomimetic supramolecularchemistry approaches to the creation of receptive analytical systems. Ever since theworks of Pedersen, Lehn, and Cram opened the arena for biomimetic host–guestsystems, receptive materials have taken on a number of functional forms includingclathrates, cryptophanes, and cyclotriveratrylenes As there is immense space fordiscussion on this topic, we limit the focus of this section to select examples ofmetalloporphyrins, crown ethers, and calixarenes in chemical-sensing surfaces. Inthis area, a large focus has been on anion recognition. The first such system wasshown to operate via macrobicyclic amines through the combination of electro-static interactions and directional hydrogen bonding [78]. Before that, the use ofmacrocyclic systems had seen a recent development as Charles Pederson foundthat crown ethers were able to spontaneously complex with alkali metals [79]. Thecation solvation properties of crown ethers were then elucidated by a variety ofpolyether rings in terms of size and functionality. Asymmetric host moleculeswere developed soon after by Cram and Cram [80] as a new biomimetic class ofcrown ethers, which offered the capability of distinguishing between enantiomersby charge and shape complementarity. The work of Lehn and coworkers [81–83]also reflected on the importance of multivalent binding and constrained shapesin achieving molecular recognition. Particularly, their synthesis and analysis ofcagelike cryptands demonstrated improved binding and selectivity over conven-tional crown ethers, thereby helping to open the area of supramolecular chemistryfor the creation of concave receptor cavities that may be lined with binding sitesintended for a specific guest [81, 84]. More recent work incorporating the useof hydrogen bonding components for chiral recognition proved to expand theseprevious approaches [85, 86]. Since its inception, supramolecular-chemistry-basedrecognition systems have grown in their selectivity and complexity. As an example,synthetic systems have been demonstrated to be complementary in topology anddimensions to hydrogen sulfate, with the cavity operating through an interlockedreceptor mechanism [87]. These elaborate systems continue to lead the field of ana-lytical supramolecular chemistry to improved recognition materials for biomimeticsensing surface.

6.4.2Macrocyclic Effect

An important component of the recognition capabilities of supramolecular chem-ical systems, in addition to the ability to control shape, size, and chemicalfunctionality, is exploitation of the macrocyclic effect. Macrocyclic host systems,

6.4 Supramolecular Chemistry 135

cyclic molecules capable of multiple guest interaction, have enhanced complex for-mation stability over a linear multivalent counterpart. Because the interaction sitesare prefixed into a preferred conformation, there is less configurational entropy formacrocyclic receptors to lose on binding. Also, the preconstrained macrocycle willnot exhibit an enthalpy loss due to intramolecular repulsion that can occur in thelinear case. Given the size of the guest and host coinciding, an additional benefitto macrocyclic constraint can be realized through the use of a proper solvent.Macrocycles are not as heavily solvated as compared to free linear analogs; hence,on complexation, they will have a more favorable solvation enthalpy contribution.This can also be applied to multimacrocycles, which explains the stronger complexformation for cryptates over conventional crown ether formations, as mentionedearlier.

6.4.3Chelate Effect

The use of chelation has obvious advantages in supramolecular systems. Inchelation, formation of the first bond between a host and guest capable of multipleinteractions increases the local effective concentration of the nearby associationsites. As a result, subsequent bond formation is more likely to occur in contrastto a monovalent binding event. Dissociation of a host–guest complex requires thedissociation of several bonds in a multivalent recognition event. On dissociationof the first bond, there remain points of attachment that prevent the immediaterelease of the guest, which is the source of the chelate effect, resulting in very fastbond reformation. Correspondingly, a macrocycle is still a chelate so it can benefitfrom this effect, though, to an even greater extent than a linear receptor. Thedissociation of a complex is generally initiated by breaking intermolecular bondsone by one beginning from a particular end group. In a macrocyclic host–guestcomplex, the structure does not have an equivalent end group, resulting in verystable complexes with extremely low dissociation rates. Creating such systems byoptimizing the flexibility and topology of the host–guest interaction allows for thecreation of highly stable molecular recognition events.

6.4.4Preorganization, Rational Design, and Modeling

The ability of synthetic receptor hosts to be tailor-made with well-defined shapes,sizes, and chemical functionalities is the key link between supramolecular chem-istry and molecular recognition. Initially, it was the coordination features of manysupramolecular chemistry building blocks that facilitated their use in the selec-tive recognition of metal ions [88]. Preorganization of host structures around acoordination sphere provided effectively stable receptors with well-defined recog-nition site geometries for select target ions; however, too much preorganizationmay prove to make it difficult to initially form the complex [89, 90]. Since theaddition of metal ions can cause different binding modes to occur, which in turn


can affect the selectivity, rational design by controlling structural properties alonecould be problematic. Although it may be appealing to design a host system solelybased on rational size, shape, and chemical complementarity to a guest analyte,using a rational design approach in combination with computational modeling is amore effective means of creating new host systems [91]. Using modeling methods,researchers aim to estimate the binding capability as well as analyte selectivityof their designed receptor. One of the first quantitative studies between a rigidneutral cavity of a host receptor and neutral guest molecules revealed the expectedoutcome, as predicted by molecular dynamic simulations, to be different from thatobtained by experimental comparison. After improving the van der Waals parame-ters from their standard force field model, the experimental binding behavior waspredictable by the computational model. This reflects the importance of empiricalevidence for proper improvement in parameterization of host–guest systems [92].Depending on the level of detail desired, host–guest complex stabilization energiesmay be predicted via molecular modeling by force field methods such as MM3 [93].While not definitive, modeling allows researchers to assess the feasibility of analyteincorporation or at least obtain a better understanding of host and guest geometriesin relation to complex formation [94]. While the geometries and individual inter-actions of the binding event are important, predicting molecular recognition hasadded complexity due to cooperative effects, in which the strength of interactions ischanged by the formation of other contacts [92]. In addition, solvent effects are oftenthe key driving forces in complex formation; however, it has been noted that theircontribution to binding may be accounted for by using a polarizable model. Inno-vative processes are continually arising for receptor–target modeling, such as theuse of the shared-electron number (SEN) method for calculation of hydrogen bondenergies [95]. Some agree that the complexity of host–guest molecular recognitionis best to be broken down into basic components, which can be assigned enthalpiesand entropies through a thermochemical model that can be confirmed empirically.Overall, the various modeling methods available are refining our understandingof natural and synthetic receptors and are widely being used in the design andmodification of beta-cyclodextrins and supramolecular recognition elements forchemical-sensing applications.

6.4.5Templating Effect

While supramolecular chemistry is diverse in its repertoire of host–guest systems,macrocycles represent a major synthetic component of biomimetic recognitionsystems. Researchers often utilize smaller, linear molecules to synthesize macro-cyclic host systems. Creating a closed ring structure classically involves similarmechanisms as polymerization and requires extremely high dilutions to favor theintramolecular reaction. Cyclization was later improved by the template effect inwhich transition metals could be used to position reactive sites into a specific geom-etry. The choice of an appropriate linear molecule and transition metal template canallow for steric control and formation of a preorganized macrocyclic product. The


properties of the resulting host system will depend greatly on the coordination andshould therefore be carefully considered when designing a receptive host system.Crown ethers represent a class of host that has been templated by alkali metals,while porphyrins do not require a metal templating ion. Calixarenes, another classof oligomeric macrocycle, are typically formed via a catalyzed phenol-formaldehydecondensation. The reaction conditions and starting materials in each of these casesmust be optimized to produce the desired cyclic structures.

6.4.6Effective Supramolecular Receptors for Biomimetic Sensing

6.4.6.1 CalixarenesOwing to their ease of modification with respect to shape and size, calixarenes arean important scaffold for designing a host system to selectively recognize specificguest molecules. They are typically shaped in a cuplike configuration characterizedby a well-defined upper rim, lower rim, and central annulus (Figure 6.3) [96]. Thisshape enables calixarenes to act as effective receptors as a result of their naturalcavities. Favorable host cavities are essentially formed by cyclic intramolecularhydrogen bonding of the hydroxyl groups to create the cup or cone shape of thecalixarene [97]. Researchers have found it possible to control the host selectivityto ion or small-molecule binding by functional modification of the upper andlower rims [96]. This can effectively be used to lock the calixarene into a specificconfirmation. Alternatively, it has been observed that the cavity can expand forlarge guests or shrink for smaller ones depending on the amount of plasticity in theupper-rim or lower-rim components [98]. As such, an optimal shape may conformon binding to certain guest molecules, representative of an induced fit bindingmechanism [99].

Using force field calculations to model the recognition event, researchers havebeen able to design calixarene receptors with strong interactions to specific targets,including halogenated or aromatic hydrocarbons, based on host/guest inclusionprinciples [99]. Such polyaromatic macrocycles are also highly effective receptorsfor organometallic anions by charge pairing interaction via hydrogen bondingresidues or by preorganization of the upper-rim charge [100]. Changing the upper-and lower-rim charges is known to effectively change the ion selectivity of the host

Upper rim

Annulus

Lower rim

R1

OR2 OR2 OR2R2O

R1R1 R1

Figure 6.3 Basic structure of calix[4]arene in which the upper and lower rims may befunctionalized.


system. For instance, calixarenes with oxygen donor atoms on the upper rim turnedoutward have demonstrated selectivity for binding of alkali ions [96]. In some cases,the upper-rim components may shield particular analytes from forming an endocomplex. The appearance of aromatic groups on the periphery of the calixarenecan also result in CH-pi stacking interactions that lead to exo-complex formationin which the guest binds outside the cavity [101], which may be the case seen in thebinding exhibited by certain saccharide targets [102]. Another variable parameterused to control selectivity in the calixarene system is deciding the number ofaromatic units in the cyclic oligomer. As an example, calix[4]arenes, tetramershaving four phenolic units, show selectivity for sodium ions, while larger cavities,such as calix[6]arene, have higher-affinity binding for larger metal cations such aspotassium and rubidium [96].

While aromatic monomers may be designed as identical units, interestingfunctional modifications of individual monomers and synthesis of low-symmetrycalixarenes have opened the way to a range of selective receptor materials. In anexample of induced fit binding, calix[6]arene cores have been functionalized withalternating amino arms that project inward after selective target binding, thusclosing the calixarene [98]. This particular case reflects the important contributionof having a flexible and polarized hydrophobic scaffold for selective molecularrecognition. Depending on the choice of functional group, modifications can instillchromogenic properties in the recognition material as has been demonstrated byShinkai and others [103, 104]. Using calixarenes with multiple different functionalgroups incorporated on the lower rim has shown that the photophysical propertiesof these systems can be easily influenced by different target binding modes.Aside from chromogenic sensors, these biomimetic receptor materials have shownsuccessful incorporation into a variety of sensor platforms, including quartz crystalmicrobalance and surface acoustic wave oscillator base sensors, as the selectivecoatings [97]. The shape and chiral selective capabilities of calixarene-based coatingshave shown to offer a solution to the selectivity problem of several sensingplatforms. As another recent example, researchers have demonstrated that chiralcalix[4]arenes functionalized with aminonaphthol moieties were capable of selectivechiral recognition of mandelic acid, a useful precursor to various drugs [105]. Asfuture biomimetic calixarene systems emerge and are refined, we will continue towitness improvements in our chemical-sensing capability as well as our analyticalsystems for assessing enantiomeric purity of pharmaceuticals.

6.4.6.2 MetalloporphyrinsResearchers have explored an enormous variety of metalloporphyrin systems overthe years in order to better understand and mimic the biological efficiency of avariety of biochemical functions found in natural systems [106]. Porphyrin is a formof heterocyclic macrocycle with open coordination sites available for axial ligation.Owing to the highly conjugated nature of porphyrin, unique spectral propertiesof these systems have often been exploited in biomimetic chemical recognitionsystems [107]. Researchers have found the arrangement and combination ofhydrogen acceptor (N) sites, hydrogen donor (OH), and Lewis acid (Zn) sites


in metalloporphyrins to be the influential factors in determining their targetpreference [108]. The selectivity of these systems is not as high as other molecularrecognition approaches; however, Suslick et al. have utilized the shape-selectivefeatures of metalloporphyrins in their colorimetric sensor arrays. Their arraysare arranged such that the targets interact with the various host sites on thesurface in a cross-reactive manner; that is, the host receptor molecules are nothighly selective for a single analyte, rather the analyte binds with various hostsdepending on their corresponding electronic and shape characteristics. Specifically,cross-reactive arrays have used various metal-incorporated tetraphenylporphyrinsto provide discrimination based on coordination [109]. The steric selectivity in thesesystems arise from the incorporation of large-, medium-, and small-sized silylethergroups on both faces of porphyrin to differentiate analytes based on size and shape[109]. Binding and coordination via the porphyrin’s metal center result in largespectral shifts, which can be detected by alterations in the colorimetric propertiesof the array. In contrast to a quantized on-off response, these systems operate todistinguish analytes based on their polarizability, ligation affinities, and associatedphotophysical color changes that occur on binding [110].

As mentioned previously, molecular recognition in these cross-reactive systemsrelies on weak interactions and does not occur through a highly selective host–guestrecognition event. Instead, the recognition occurs by the analyte creating a uniquedistributed response pattern across the various hosts in the array, which can beassessed as the analyte’s signature [111]. This biomimetic approach is akin tothat of the olfactory and gustatory systems, which we utilize for chemical sensingof the environments around us. Interestingly, the importance of Lewis acids innatural chemical sensing has also been supported, as there are indications thatthe olfactory receptors are metalloproteins [112]. In essence, the Lewis acidity ofthe metals in metalloporphyrins makes these hosts useful for the recognition ofanalytes with Lewis acid/base capabilities, while the presence of bulky side groupsand hydrogen bonding components can restrict, to some extent, the selectivityof the interaction event [113]. This inherent lack of complete selectivity is thefundamental aspect of utilizing these synthetic receptors in biomimetic arrays.Through the use of pattern recognition and the ability of the individual receptorsto interact differently depending on the analyte, these systems have proven usefulin chemical sensing at discriminating a large variety of complex analytes arisingfrom food [114], beverages [115, 116], bacteria [117], and even explosives [118]. Inthe future, cross-reactive metalloporphyrins may be used in a variety of diagnosticsettings due to the wide chemical diversity capable of being displayed by thesebiomimetic receptor systems.

6.4.7Recent Improvement

While supramolecular receptive materials work well for small-molecule systems,synthetic receptors are claimed to be no match for the selective recognition ofmedium- and large-complex analytes achievable with antibodies and aptamers


[111]. Particularly, the use of cross-reactive arrays based on the pattern recogni-tion/differential receptor paradigm has difficulty in discriminating complex proteintargets. Biological systems utilize binding models similar to the induced fit mecha-nism, which we have previously discussed, and as such, supramolecular chemistrymust mimic this process in order to achieve the same level of molecular recogni-tion. Recently, a new generation of macrocyclic host has emerged that can mimicthe induced fit mechanisms of biology. In the recently developed systems, knownas heterocalixaromatics, the aromatic rings are linked by different heteroatomsincluding oxygen and nitrogen, whose bond angles and lengths can vary resultingin finely tuned cavities, rather than the conventional methylene bridge [119]. Forexample, azacalix[4]pyridine can adopt a different electronic configuration to alterits cavity conformation for providing the maximum and most efficient interactionwith its guest species [119]. Impressive structural features such as this continueto improve the molecular recognition capabilities of supramolecular chemical sys-tems, although constructing biologically analogous synthetic receptors still remainsdifficult due to the complex atomic networks needed to form large binding siteswith precisely positioned binding functionality [120].

6.5Biomolecular Materials

6.5.1Introduction

The third class of biomimetic receptive material is derived directly from the naturalbuilding blocks of the nature of biological systems. In terms of chemical sensing,biological selectivity remains unrivaled. Accordingly, biomolecular materials offera unique advantage in terms of molecular selectivity due to their inherent diversityarising from sequence heterogeneity. Conceivably, the most well-known example ofmolecular recognition in biology was discovered by James Watson and Francis Crickin 1953. Specifically, they found the hydrogen bonding patterns between purinesand pyrimidines of certain types of DNA, referred to as base pairing, that provideda framework for controlling the hybridization between complementary strands ofnucleotides. Because the molecular recognition events are spatially constrained tothe limits imposed for stable helix formation, effective binding can occur morereliably when a high fidelity of sequence complementarity is maintained. If the facilebase pairing requirements are not met, the resulting complex exists with a certaindegree of instability, which may likely cause dissociation or inhibit the initialstrand hybridization. Using this inherent sequence specificity, researchers cancreate custom host–guest systems with precise molecular recognition capabilities.Already, the biomimetics community has created an abundance of biomolecularmaterials exploiting the canonical Watson–Crick base pairing for use as receptivecoatings. As discussed later, DNA chip technologies have made particularly effectiveuse of these principles for genomic screening and analysis. Also highlighted are

6.5 Biomolecular Materials 141

the recent achievements using engineered nucleic acid systems that have openedthe way for a new class of biomimetic materials capable of selective chemical andbiomolecular detection.

In addition to nucleic acids, the heterogeneity of amino acid sequences hasallowed a large repertoire of diverse receptors and enzymes to evolve with exquisitespecificity for unique target ligands and substrates. One of the strongest proteinrecognition events, which is widely used throughout the biotechnology industry,is the complex formation between biotin and streptavidin, having a dissociationconstant of approximately 10−14 M [121]. The protein exists as a homotetramer,each with binding domains for the small-molecule biotin. Multiple hydrogenbonding and van der Waals interactions facilitate high-affinity binding in additionto structural closure of surface loops to bury the biotin in the interior of theprotein through quaternary changes [122]. This system is often used to capture andimmobilize proteins, DNA, or other molecules onto beads and surfaces for variousbiological assays, selections, or purifications. Biomimetic attempts to harness theactive site constituent of streptavidin have yielded several peptide variants, whichfunction as stand-alone recognition elements for biotin, with similar selectivity.

A key strategy of the biomolecular materials approach to molecular recognitionmakes use of sequence rearrangements of biopolymers composed of naturalbuilding blocks, such as nucleic acids and amino acids, to create a diverse collectionof candidate receptors and further imitate the evolutionary process. Using cleverlydesigned combinatorial libraries and directed evolution strategies, researchers haveaimed to recapitulate the highly specific molecular recognition events obtainedthrough native biological systems in order to obtain biomimetic receptors for theirown targets of interest. This section discusses the in vivo, in vitro, and even in silicoselection strategies used to obtain biomimetic receptors derived from biomolecularmaterials with target selectivity comparable to that achieved by nature.

6.5.2Native Biomolecules

Over 10 million polypeptide sequences have been identified (as of January 2011),and a small percentage of these have their three-dimensional structures solved[123]. Among this collection of known proteins, several forms of natural receptorshave been discovered, which offer unparalleled selectivity for their target ligand. Itis therefore attractive to consider the use of native biomolecules (i.e., polypeptides,polynucleotides, and polysaccharides) as true biomimetic molecular recognitionelements for sensor coating applications. The following sections highlight someof the key natural recognition elements that have been utilized for sensor coatingapplications.

6.5.2.1 PolypeptidesRanging from short chains of six amino acid residues to nearly 37 000 aminoacid containing proteins, polypeptides offer diverse functional and structuralcomponents that are used to carry out the biological processes of life through


selective molecular recognition. Research on sensing applications has focused ontwo broad uses of natural polypeptides known for their substrate specificity (catalyticrecognition and affinity-based recognition). In the context of this chapter, we focuson biomimetic molecular recognition through affinity-based means, with the mostprominent examples being antibodies and receptor proteins. These proteins offerhigh affinity and target specificity improved over a long natural evolutionaryprocess. In addition, the diversity of existing receptors is enormous, with a numberof useful targets including toxins, metabolites, and other relevant chemicals [124].Aside from existing receptor–ligand pairs, antibodies for a specific target can beobtained from the immune system of healthy animals by introducing the antigen(target chemical or protein) into the blood stream. The healthy immune responsewill activate B lymphocytes to produce antibodies against the target antigen. Thesecells are harvested from the animal and fused to an immortal cell line to allowfor continual production of the antibody of interest. Researchers have utilizedthis approach to produce thousands of different antibodies with specificity for arange of biological and chemical targets, including dyes, explosives, carcinogens,and pesticides for sensor applications [125–127]. Effective immobilization of themolecular recognition elements is critical for ensuring the efficacy of the sensingsurface. For example, burial of the recognition domain because of randomlyoriented immobilization may render the selectivity element inaccessible to targetbinding. In addition, excessive linkages between the surface and the recognitionelement may result in conformational changes of the polypeptide that will alterthe shape of the recognition domain. A caveat of many natural receptors is thatthey are membrane proteins requiring a membrane framework, for example,Langmuir–Blodgett monolayer films, in order to create a recognition surface forsensing purposes; however, antibodies do not have such requirements and henceare more widely used for sensing applications [128]. These membrane receptors canhost a variety of different targets of biological importance and are generally classifiedinto ion channel receptors, G-protein-linked receptors, and single transmembranereceptors, among others. As future receptor protein structures become resolved,we may gain a better understanding of the recognition site selectivity imposedby these biopolymers through directly observing the amino acids responsible forbinding with the target.

6.5.2.2 CarbohydratesComplex carbohydrates exist on the surface of eukaryotic cells and play importantroles in molecular recognition for cell–cell recognition, adhesion, and activation.Researchers have identified that polysaccharides can even specifically interact withpolynucleotides [129]. Understanding polysaccharides and how their structuresconfer selectivity is still an eventful topic of study. For instance, microbes utilizeglycoconjugates (carbohydrate-functionalized biomolecules) on the surface of hostcells and tissues for infecting them, as in the case for influenza virus binding tosialic acid, but they also may utilize similar carbohydrate structures in their ownmicrobial surface to mimic the host in order to escape immune defense [130].Unlike the phosphate or peptide bond of other biopolymers, polysaccharides utilize


several different glycosidic linkages between various different monosaccharides toallow a significant diversity in their structures. As an example, gangliosides are asialic-acid-containing glycoconjugate with great heterogeneity and diversity in thestructures of their carbohydrate chains for participating in cell–cell recognition,adhesion, and signal transduction [131]. Because of their capability to be usedin the molecular recognition of certain protein targets, glycoconjugates havebeen incorporated into chemical sensor platforms as selective sensing surfaces forcholera toxin and influenza virus [132, 133]. By incorporating these glycoconjugatesinto colorimetric sensors based on conjugated liposomes and lipid membranefilm, the work by Charych and coworkers effectively demonstrated the ability toprobe selective molecular interactions by mimicking the receptive strategy usedby cellular surfaces. Because of the ease of incorporation into such systems,natural carbohydrate-based receptors offer significant benefits to the de novodesign of artificial receptors. The actively explored research area of glycobiologywill undoubtedly provide a future insight into the binding capability, affinity, andselectivity of these native receptors, as we continue to develop a better understandingof their critical molecular recognition roles in signal transduction and other vitallife processes.

6.5.2.3 OligonucleotidesThe most widely utilized native biopolymers for selective molecular recognitionhave been the oligonucleotides, DNA and RNA. Single-stranded DNA (ssDNA)and RNA offer the unique advantage of selective molecular recognition achiev-able through linear sequence complementarity. As such, researchers have beenable to take advantage of this simplistic binding paradigm in order to createoligonucleotide-embedded materials and surfaces capable of selective biomimeticrecognition of target strands with some of the highest specificities of any molecularrecognition system. The high selectivity and affinity of these natural componentshas become further popularized by the creation of DNA microarray technologies,most notably those developed by Affymetrix, which encode a significant repertoireof genes from a given species’ genome. Because of the high specificity of com-plementary base pairing, a copy of ssDNA can provide quantitative detection of asingle complementary strand in the background of an extremely complex mixture[134, 135]. In short, DNA microarrays offer a molecular recognition surface com-posed of oligonucleotide segments, representing all known genetic elements of anorganism, which can be exposed to a sample of cDNA to estimate genome-widetranscription profiles of the sample’s host.

While these high-throughput systems offer the advantage of collecting a largeamount of information about gene expression levels, other oligonucleotide-basedrecognition elements composed of RNA have been utilized by researchers for adifferent but just as interesting purpose. Natural noncoding RNA, which servesto control biochemical pathways, has been found to have exquisite molecularrecognition capabilities for various metabolites. These so-called riboswitches canundergo a conformational change to bind with high affinity to ligands includingvitamin B12, thiamine pyrophosphate, and flavin mononucleotide [136]. In addition,


the selectivity of these molecular recognition elements are inherently superb; forexample, thiamine pyrophosphate sensor riboswitches are found to have 1000-foldpreferential binding to target over the unphosphorylated precursor metabolite [137].Owing to these advantageous properties, natural oligonucleotide-based receptorsare often exploited in biomimetic sensing materials. As discussed in the nextsection, biopolymers including RNA can also be engineered computationallyand by in vitro screening to achieve high-affinity receptive motifs with designedselectivity for a desired target of interest.

6.5.3Engineered Biomolecules

While native biopolymers, such as G-protein-coupled receptors, antibodies, andRNA riboswitches, offer high-affinity molecular recognition elements, the abilityto tailor-make a biomimetic molecular recognition element with predeterminedspecificity is an attractive prospect for chemical-sensing applications. The followingsection discusses the strategies for engineering biomolecular materials for use asselective chemical-sensing components. Particularly, selection technologies forDNA and RNA aptamers, directed evolution of recombinant antibody mimics, andthe computational design of receptive biomolecules are focused on.

6.5.3.1 In vitro Selection of RNA/DNA AptamersBefore riboswitches were discovered to play a critical cellular role via metaboliteinteraction, research found that DNA or RNA could be engineered to formaptamers, a selective molecular recognition motif capable of high-affinity bindingof targets including small chemical targets as well as proteins [138, 139]. In 1990,the laboratories of Szostak and Gold found that large libraries of approximately1010 random sequence RNA molecules could be screened through in vitro selection,often referred to as SELEX , to identify selective molecular recognition elements withunexpectedly high affinity for predestined target ligands. Since then, DNA and RNAaptamers have been identified by systematic evolution of ligands by exponentialenrichment for targets ranging from simple ions, small-molecule metabolites,and peptides to large proteins, organelles, and viruses with library sizes up to1015 randomized oligonucleotides [140]. For the most part, published bindingconstants for such aptamers are generally in the low nanomolar to picomolarrange [141]. This is accomplished by nucleic acid aptamers offering numeroushydrogen bond acceptors and donors to facilitate the molecular recognition of theirtarget. The recognition process encompasses shape complementarity, stacking andelectrostatic interactions, and other aspects of intermolecular interaction, whichwere discussed thoroughly in previous sections. In addition, the flexibility ofpolynucleotides allows for the formation of recognition cavities for the selectiveenclosure of small-molecule targets. While DNA is intrinsically more chemicallystable than RNA, there is inherently no difference in the capability of forminghighly selective aptamers with either polynucleotide. It should be noted thoughthat an ssDNA aptamer sequence if converted into the RNA equivalent typically


T7 promoter

5’

Constantsequence

Randomsequence

Constantsequence

3’

ssDNA library

dsDNA pool

PCR Sequence analysis tosearch for consensus

Aptameridentified

In vitrotranscription

RNApool

PCR

cDNA

Reversetranscription

Target binding EnrichedRNA

Partition Non binding RNA

Figure 6.4 Outline of a typical scheme for in vitro selection of aptamer RNA by cyclicscreening.

will not have the same molecular recognition capabilities due to the role ofthe 2′-OH contributing to interactions that stabilize the aptamer and complex[140]. Thus, deciding between either RNA or DNA screening strategies is thefirst step in selecting high-affinity nucleic-acid-based receptors. Subsequently, acyclic procedure is performed in which RNA- or DNA-based aptamers, capable ofbinding to a given target, are selected from pools of variant sequences followed byamplification of those sequences that bind well to the target (Figure 6.4).

As outlined, the SELEX strategy can utilize randomized RNA or DNA sequencesof 20–80 nucleotides long, which can be flanked by a constant region of knownsequence for the purposes of amplification, transcription, or reverse transcription.After a few cycles of selection and amplification, dominant selective binding se-quences tend to populate the observed sequence space given that the initial diversityof the library was sufficiently large to contain at least one such binding sequence.A variety of selection strategies can be applied to remove unwanted low-affinitybinding sequences, such as washing strength, time, and buffer conditions. Otherselection pressures can include a short target exposure time for binding, whichwill preferentially enhance the proportion of sequences with fast on-rates. Theresults of this approach yield a consensus sequence motif, generally purine rich,with dominant binding capabilities for the intended target facilitated by a properlyformed H-bonding surface, sometimes via irregular oligonucleotide topologiesand noncanonical base pairing [140]. Hence, the self-annealing properties of theaptamer allow for three-dimensional structures that specifically recognize the in-tended target molecule with high affinity. By providing a means for identifyinga molecular recognition element for a target of interest, this screening strategy


offers several benefits to the diagnostic and sensor community [142]. In additionto their widespread use as highly selective recognition elements, these systemshave also demonstrated that binding-induced conformational changes in the struc-ture of these nucleic-acid-based receptors can be utilized for transduction of thebinding signal. Such engineered RNA aptamers have been modularly designed torecognize small-molecule targets and consequently undergo strand displacementto act as a reporter on binding to a number of chemicals of interest [143, 144].This offers a smart approach, as the precise target binding sites and correspondingconformational changes are generally unknown beforehand.

In contrast to the use of antibodies as biomimetic molecular recognition elementsfor sensing chemicals, aptamers can offer several advantages. First of all, aptamersare considerably smaller that makes them easier to synthesize and enables them toaccess areas that might otherwise be inaccessible to the bulkier structure of the na-tive antibody [145]. This qualification can also facilitate a sensor surface to achievehigher functional coating densities when using aptamers as compared to anti-bodies. In addition, SELEX screening can be performed using nonphysiologicalconditions and be used to screen otherwise toxic targets. Despite these advan-tages, nucleic-acid-based aptamers have drawbacks as well. While aptamers have along shelf life, they do remain highly susceptible to enzymatic degradation and arequickly destroyed in biological fluids [141]. In addition, not every target may be effec-tively screened by SELEX, particularly those with hydrophobic or negatively chargedsurfaces, thereby leaving room for improvement in this strategy’s future [146].

In summary, oligonucleotide library sequence screening allows for key aptamersto be selectively bound to a target, amplified, and enriched by way of eliminationof nonbinding sequences to yield useful recognition elements. Their ease ofchemical modification and immobilization without loss of function is expected tobring about the replacement of antibody-based assays with aptamer-based sensingmaterials [141]. The value of these nucleic-acid-based aptamers lies in their flexiblenature, simple synthesis, high selectivity, and high affinity, thereby allowing theirimplementation in a variety of biosensor platforms with successes rivaling those ofantibody-based recognition elements [128, 147, 148].

6.5.3.2 Evolutionary Screened PeptidesIn an approach akin to the previously mentioned SELEX strategy, amino acidlibraries may also be screened for selective binding sequences. One may considerusing a similar combinatorial chemistry approach to synthesize large peptidelibraries, which may prove useful in some limited applications. Unfortunately,combinatorial chemical synthesis of peptide libraries does not provide the samelink between genotype and phenotype, as was seen in the previous section usingoligonucleotide libraries, thus making it difficult or impossible to identify singlepeptide molecules capable of target binding and subsequent amplification. As such,peptides are best screened using an evolutionary selection method. Herein, webriefly discuss a few approaches to identifying biomimetic molecular recognitionelement through the evolutionary screening of peptides, generally referred to asdisplay techniques.


The potential of rapid evolutionary screening of peptide libraries was firstrealized through the seminal work of George Smith in 1985. Therein, he revealedthat peptide sequences could be displayed as fusions to protein 3 (p3) on the coat offilamentous bacteriophages and thereafter be specifically enriched 1000-fold (overwild-type phage) by affinity selection against an antibody target for the displayedpeptide [149]. The resulting process, referred to as phage display, is now widely usedas a high-throughput screening procedure for identifying high-affinity molecularrecognition elements for target molecules, proteins, and crystals, among other uses[150]. Since its inception, the concept of displaying peptides on the protein coatof phage had sparked immediate interest in producing combinatorial libraries orcollections of phage (typically having a diversity of 109) with different displayedpeptide sequences capable of serving as recognition elements [151]. The efforts ofSir Gregory Winter and coworkers have been pivotal in improving the functionalityand capabilities of the evolutionary screening of phage for creating highly selectivereceptors for predetermined targets. One of their most important contributionsrevealed that large libraries, or collections of phages, could be created in whichthe p3 phage coat was fused with a short variable antibody fragment, whichmay act as a receptor for a given target. By introducing the library of differentreceptive antibody fragments to a target antigen, they could selectively removethe nonbinding phage and isolate the phage that displayed antibody fragmentswith high affinity for the target antigen [152]. In doing so, they could analyzethe peptide sequence to effectively identify an antibody engineered for a giventarget by phage display selection to that target, thereby providing receptive peptideswith the high affinity and selectivity for antibodies but in a much smaller size.The use of such antibody fragments is less expensive and less time consumingcompared to polyclonal or monoclonal antibody production, and they have beensuccessfully used to detect toxins, proteins, and a number of other targets ofinterest [128]. In addition to discovering new receptors for target molecules, thephage display system is also an effective format for directed evolution to improvethe molecular recognition capabilities of existing low-affinity receptors [153]. Byincorporating an existing low-affinity antibody into the phage protein coat andvarying key amino acids in the binding site, a receptive antibody with betterselectivity and higher affinity may be identified through the high-throughputscreening process.

The general strategy for using phage display to identify high-affinity molecularrecognition elements first requires an appropriate library of candidate receptorto be created. The library may be designed with varying diversity, with varyinglengths of amino acid sequences, and by varying the placement of amino acidswithin the scaffold, and may even be designed to have constrained loops toavoid unfavorable entropic folding on target binding. Once the phage libraryis cloned and expressed in Escherichia coli, the phage may be collected for usein screening against a target molecule of interest. The target molecule may beimmobilized onto a surface, allowing physical separation of the target from thesolution containing the phage library. The target is incubated with the libraryto allow the phages bearing specific receptors to bind to the target, while the


nonspecific binders are consequently washed away. The bound phages, as comparedto the original library, on average contain better receptor sequences for thetarget of interest. After isolating and capturing the bound phage, they may bereplicated by infecting E. coli to produce many copies of themselves. Generally,the phages are subjected to four or more cycles of the screening procedure (insome cases, with more stringent binding conditions) to enrich primarily the targetbinding sequences [154]. Since the protein sequence displayed corresponds to thegenetic sequence inside the phage, the DNA sequence can be read to identifythe amino acids comprising the target-specific recognition motif. A number ofimprovements and variations can be employed as useful strategies for enhancingthe capabilities and applications of the systems. Before screening, for instance,the phages may be chemically modified to produce constrained peptide loops withan organic core. Such strategies have been effective at enhancing the bindingfunctionality as well as the stability of the receptors to protease degradation[155]. These methodologies are critical in obtaining a recognition element withproperties matching the necessary application. Moreover, the facile nature ofchemically modifying these engineered peptides has allowed them to be rapidlyincorporated into a variety of chemical-sensing platforms with great success[156–158].

An influential factor for finding a suitable receptor to a target using displaytechnologies is the diversity of the library. Larger libraries will inherently have morepotential for containing sequences that bind with high affinity to a target of interest.As such, researchers strive to extend the diversity of their combinatorial phagelibraries. It is generally accepted that the bacterial transformation requirement inthe production of phage display libraries is the limiting factor, thus restrictingstarting phage libraries to a diversity range of 109−1010 varying peptide sequences[159]. Another screening approach, known as ribosome display, offers a fully in vitroalternative to phage display screening and has been effective in screening randompeptide libraries to achieve target-selective binding with nanomolar affinities [160].Before its development, there were no effective methods to link phenotype andgenotype for peptide screening [160, 161]. By avoiding the low efficiency of DNAuptake required for in vivo peptide screening systems, ribosome display (in additionto the related mRNA display discussed later) can offer better diversity. As such,initial studies showed decapeptide libraries with 1012 diversity can be effectivelyscreened for enrichment of binders [160].

While the basics of ribosome display are similar to other screening proceduresin that alternating steps of affinity selection and amplification of binders areperformed, there are significant differences in the details and complexity of theprocesses. In the particular case of screening a peptide–antibody fragment libraryby ribosome display, an antibody fragment DNA library must be amplified by PCRto contain a T7 promoter for transcription, a ribosome binding site for translation,a spacer segment to allow sufficient space between the antibody fragment andthe ribosome, and stem loops at the 5′ and 3′ ends to prevent destruction byexonucleases. The sequence is then transcribed into mRNA that is translated bythe E. coli S-30 in vitro system. During translation, the activity of the ribosome is


stopped by cooling on ice, with further stabilization provided by increasing thelevel of magnesium [160]. These conditions must be preserved in order to maintainthe complex formed between mRNA, ribosome, and the translating antibodyfragment. The antibody fragment complexes are then subjected to affinity selectionand removal of nonspecific binders. The complexes are then eluted directly, or bydissociation of the mRNA from the complex, by EDTA exposure, at which pointthe isolated mRNA is amplified by RT-PCR and is ready for the next round ofscreening. Using this concept, researchers have successfully demonstrated thatsingle-chain fragment antibodies can be enriched 108-fold over five cycles of theabove-mentioned transcription, translation, affinity selection, and amplification[160].

While the above ribosome display system has proven successful in the discoveryof many receptors capable of molecular recognition, researchers sought out ameans of improvement. In a very similar approach known as mRNA display,researchers were able to directly link the mRNA to the nascent peptide by use ofan adapter molecule, thereby making the system more robust by eliminating theneed for stabilization of the ribosome complex [162]. The elegant adapter moleculeis linked to the 3′ end of the oligonucleotide by way of a DNA spacer directlyattached to the mRNA. As the translating ribosome reaches the junction betweenthe mRNA and DNA, the ribosome becomes stalled. At this point, the adaptermolecule, puromycin, mimics the necessary aminoacyl end of tRNA by enteringthe A site of the ribosome where it forms a stable amide linkage with the nascentpeptide. The resulting fusion is an mRNA–peptide hybrid that effectively formsa direct link between genotype and phenotype, with enhanced stability over thecomplex used in ribosome display. Since the capabilities of the mRNA displaysystem indicate that more diverse libraries (1013) can be created by the in vitroscreening systems over conventional phage display (109), there is an inherentimplication that mRNA display is a preferable method for creating longer peptidelibraries in order to possess a more complete repertoire of the randomized sequencespace [159]. Indeed this is also seen in practice, as typical phage display librariestend to utilize randomized domains in the size range of 6–14 amino acids, whilemRNA display has shown to be effective for peptides that are 27 amino acidslong [162]. In some cases, this increased length and diversity achievable by mRNAdisplay has resulted in selected peptides with higher affinity than those obtainedfrom shorter and less complete phage display libraries [163]. However, despite theapparent advantages, phage display remains a more commonly used screeningplatform, perhaps due to its ease of use and inexpensive commercially availablekits [150].

Regardless of the approach, the molecular recognition capabilities achievablethrough evolutionary screening can yield peptide-based receptors, with directapplications as biomolecular coating materials for chemical sensors. Over the years,evolutionary selection of peptides has been fruitful in achieving highly selectivebinders with superb affinities for targets including crystal surfaces [164], smallmolecules [17], and proteins [165] by an array of innovative screening strategies.As we continue to explore the use of more constrained peptide morphologies in


these systems for enhancement of binding affinities as well as the use of organicchemical core hybrid systems for expanding the functionality and immobilizationcapabilities of these systems, we will undoubtedly find the incorporation intoanalytical devices and sensor platforms more frequent as biomimetic molecularrecognition elements.

6.5.3.3 Computational and Rational Design of Biomimetic ReceptorsGiven the large amount of sequence and structural data available for receptorproteins, it may not be surprising to know that researchers are able to create newbiological receptors for different target molecules by de novo design and in silicotesting. While the capabilities of tailor designing an effective biomolecule-basedreceptor for any given target of interest may be still under development, thefoundation of such work has been laid by pioneers using computational proteinengineering to convert the specificity of existing biomolecules [166, 167]. Progressin the area of receptor design had generally been hindered due to the large numberof structures that must be considered [168]. Using existing protein scaffolds,such as proteins from the periplasmic binding protein superfamily, researchcould limit the range of the potential structure space to examine specific classesof receptor–target interactions [169]. In doing so, the complexity of identifyingthe desired electrostatic and structural complementarity between a target andreceptor becomes less overwhelming. The design of a receptor in this fashionmay often utilize an iterative process of sequence modification and assessment ofinternal strain, desolvation, and van der Waals and coulombic interactions withinthe primary coordination sphere of the receptor–target complex to maximizeestimated binding constants and selectivity.

Several members of the periplasmic binding protein superfamily, such as thephosphate-binding protein, have proven to be effective biomolecular materialsfor detection of their natural targets [170]. Using computational approaches toreengineer the maltose-binding protein, Hellinga and coworkers [171] have shownthe capability of altering the specificity of the protein to a number of nonnativesubstrates including the explosive trinitrotoluene. Throughout their efforts, theyhave redesigned the protein-based biomimetic sensors for targets including zinc,pinacolyl methylphosphonic acid, lactate, and serotonin with enhanced affinities;however, a recent reassessment by crystallographic structural data revealed that thebinding mechanisms were not the same as those predicted [172, 173]. Nonetheless,experimental evaluation showed as low as nanomolar affinities for some of the de-signed receptor–target interactions, which signifies the potential for these systemsas molecular recognition elements in biomimetic chemical-sensing systems [171].Moreover, this rational design approach allows researchers to expand the repertoireof biomimetic sensor targets outside those for which receptors naturally evolved[174]. The strategies utilized in these design approaches recapitulate the advantageof using biomimetic materials as sensor coating in that we need not revamp anentire sensing platform if we wish to alter the selectivity, but rather the problem oftailor-made chemical sensing is best achieved from a modular recognition elementapproach [175].

6.6 Summary and Future of Biomimetic-Sensor-Coating Materials 151

6.6Summary and Future of Biomimetic-Sensor-Coating Materials

By exploring the productive history behind the use of biomimetic materials forsurface-based molecular recognition, we hope to have provided a broad knowl-edge base for better understanding the current field of chemically receptivecoatings. In addition, this provided a perspective for appreciating the recent im-provements, trends, and needs in developing molecular recognition elements aswe highlighted in the areas of molecular imprinting, supramolecular chemistry,and biomolecular-material-based recognition elements. While these three areasrepresent distinct strategies with respective benefits and drawbacks as selectivemolecular recognition materials, a comprehensive understanding allows futureresearchers to avoid pitfalls, which result in unproductive development. As anexample, purely computational strategies for the design of receptor–ligand pairsmay be ineffective, as an algorithm may not be able to capture all the characteristicsnecessary for accurately predicting complex formation. As a result, researchers mayutilize a combination of structural studies and experimental validations to improvethe computational design strategies. In addition, the wealth of data obtainable fromin vitro screening of RNA, DNA, or peptide libraries may provide a starting point forthe combination of theoretical design strategies with the observable evolution andadaptability of target binding sites. The remaining goal in chemical sensor coatingsis to find a strategy for the rapid and effective creation of a surface coating materialcapable of high affinity and selectivity for a predetermined target of interest. Whilethe traditional concepts of lock and key mechanistic fitting may still be alluring dueto their simplicity, recent findings from the case of biological molecular recognitionshow that kinetic proofreading and multistep dynamic binding events may providea better strategy to selective recognition in complex environments by overcomingnoise. In addition, these receptive materials that undergo structural changes onbinding to their target of interest may have other immediate benefits as sensorsurface coatings, as small perturbations have the potential for being transducedinto detectable signals.

With increased research in the area of biomolecular materials development, RNA,DNA, or peptides may likely become the dominant form of sensor coating materialin the near future. Aside from the commercial availability of easy-to-use, step-by-stepkits for finding peptide-based receptors via phage display, the continuing progressin modifying biomolecules to be more stable for sensor platform technologies willlikely result in their widespread use as highly robust and effective biomaterial sensorcoatings. Interestingly, the fields of catalysis, separation, and therapeutics utilizemany of the same underlying principles of molecular recognition and will thusbe impacted significantly by the continued understanding of biomimetic surfacesfor selective molecular recognition. The ability to create on-demand selectivity fora target of interest will also have a profound impact on not only the researchcommunity but also, more importantly, to the public in the areas of medicaldiagnostics and analytical equipment for assessing chemical exposure risk. Weexpect that gaining a better understanding of these principles, selection strategies,


design concepts, and computational models will aid researchers in overcomingsome of the current limitations outlined earlier on biomimetic materials forselective molecular recognition in sensing.

References

1. Fischer, E. (1894) Ber. Dtsch. Chem.Ges., 27(3), 2985–2993.

2. Franco, M.I., Turin, L., Mershin, A.,and Skoulakis, E.M.C. (2011) Proc.Natl. Acad. Sci., 108, 3797–3802.

3. Savir, Y. and Tlusty, T. (2007) PLoSONE, 2(5), e468.

4. Foote, J. and Milstein, C. (1994) Proc.Natl. Acad. Sci., 91(22), 10370–10374.

5. Ma, B., Kumar, S., Tsai, C.J., andNussinov, R. (1999) Protein Eng., 12(9),713–720.

6. Frauenfelder, H., Sligar, S.G.,and Wolynes, P.G. (1991) Science,254(5038), 1598–1603.

7. Dill, K.A. and Chan, H.S. (1997) Nat.Struct. Mol. Biol., 4(1), 10–19.

8. Goh, C.S., Milburn, D., and Gerstein,M. (2004) Curr. Opin. Struct. Biol.,14(1), 104–109.

9. James, L.C. and Tawfik, D.S. (2005)Proc. Natl. Acad. Sci. U.S.A., 102(36),12730–12735.

10. James, L.C., Roversi, P., and Tawfik,D.S. (2003) Science, 299(5611),1362–1367.

11. Schuermann, J.P., Prewitt, S.P., Davies,C., Deutscher, S.L., and Tanner, J.J.(2005) J. Mol. Biol., 347(5), 965–978.

12. Yin, J., Beuscher, A.E. IV, Andryski,S.E., Stevens, R.C., and Schultz, P.G.(2003) J. Mol. Biol., 330(4), 651–656.

13. Wedemayer, G.J., Patten, P.A., Wang,L.H., Schultz, P.G., and Stevens, R.C.(1997) Science, 276(5319), 1665–1669.

14. Ariga, K., Hill, J., and Endo, H. (2007)Int. J. Mol. Sci., 8(8), 864–883.

15. Schamel, W.W.A., Risue, R.M.,Minguet, S., Ort, A.R., and Alarc,B. (2006) Trends Immunol., 27(4),176–182.

16. Greer, J.P., Foerster, J., Lukens, J.Rodgers, G.M., and Paraskevas, F.(2004) Wintrobe’s Clinical Hematol-ogy, 11th edn, Lippincott Williams &Wilkins, pp. 453–456.

17. Jaworski, J.W., Raorane, D., Huh, J.H.,Majumdar, A., and Lee, S.W. (2008)Langmuir, 24(9), 4938–4943.

18. Park, J.S., Cho, M.K., Lee, E.J., Ahn,K.Y., Lee, K.E., Jung, J.H., Cho, Y.,Han, S.S., Kim, Y.K., and Lee, J. (2009)Nat. Nano, 4(4), 259–264.

19. Lehn, J.M. (1988) Angew. Chem. Int. Ed.Engl., 27(1), 89–112.

20. Lehn, J.M. (1985) Science, 227(4689),849–856.

21. Rebek, J. (1987) Science, 235(4795),1478–1484.

22. Naray-Szabo, G. (1993) J. Mol. Recog-nit., 6(4), 205–210.

23. Apaya, R.P., Lucchese, B., Price, S.L.,and Vinter, J.G. (1995) J. Comput.-AidedMol. Des., 9(1), 33–43.

24. Schneider, H.J., Blatter, T., Eliseev,A., Rudiger, V., and Raevsky, O.A.(1993) Electrostatics in molecular recog-nition: from ion pairs and inophores tonucleotides and DNA, Pure and AppliedChemistry, 65, Research Triangle Park,NC, ETATS-UNIS, p. 56.

25. Dean, P.M., Chau, P.L., and Barakat,M.T. (1992) J. Mol. Struct. THEOCHE,256, 75–89.

26. Perutz, M.F. (1993) Philos. Trans.R. Soc. London, Ser. A, 345(1674),105–112.

27. Plevin, M.J., Bryce, D.L., andBoisbouvier, J. (2010) Nat. Chem.,2(6), 466–471.

28. Fan, E., Van Arman, S.A., Kincaid,S., and Hamilton, A.D. (1993) J. Am.Chem. Soc., 115(1), 369–370.

29. Meyer, E.A., Castellano, R.K., andDiederich, F. (2003) Angew. Chem. Int.Ed., 42(11), 1210–1250.

30. Vijay, D. and Sastry, G.N. (2010) Chem.Phys. Lett., 485(1–3), 235–242.

31. Kumpf, R.A. and Dougherty, D.A.(1993) Science, 261(5129), 1708–1710.

32. Quinonero, D., Garau, C., Rotger, C.,Frontera, A., Ballester, P., Costa, A.,

References 153

and Deya, P.M. (2002) Angew. Chem.Int. Ed., 41(18), 3389–3392.

33. de Hoog, P., Gamez, P., Mutikainen,I., Turpeinen, U., and Reedijk, J. (2004)Angew. Chem., 116(43), 5939–5941.

34. Houk, K.N. and Wheeler, S.E. (2010) J.Phys. Chem. A, 114(33), 8658–8664.

35. Naray-Szabo, G. (1989) J. Mol. Graph-ics, 7(2), 76–81.

36. Olsson, T.S.G., Williams, M.A., Pitt,W.R., and Ladbury, J.E. (2008) J. Mol.Biol., 384(4), 1002–1017.

37. Hoeben, F.J.M., Jonkheijm, P., Meijer,E.W., and Schenning, A.P.H.J. (2005)Chem. Rev., 105(4), 1491–1546.

38. Bissantz, C., Kuhn, B., and Stahl,M. (2010) J. Med. Chem., 53(14),5061–5084.

39. Nishio, M. (2011) Phys. Chem. Chem.Phys., 13(31), 13873–13900.

40. Mecozzi, S. and Rebek, J.J. (1998)Chem. Eur. J., 4(6), 1016–1022.

41. Frederick, K.K., Marlow, M.S.,Valentine, K.G., and Wand, A.J. (2007)Nature, 448(7151), 325–329.

42. Hunter, C.A. and Tomas, S. (2003)Chem. Biol., 10(11), 1023–1032.

43. Badjic, J.D., Nelson, A., Cantrill, S.J.,Turnbull, W.B., and Stoddart, J.F.(2005) Acc. Chem. Res., 38(9), 723–732.

44. Handl, H.L., Vagner, J., Han, H.,Mash, E., Hruby, V.J., and Gillies, R.J.(2004) Expert Opin. Ther. Targets, 8(6),565–586.

45. Rao, J., Lahiri, J., Weis, R.M., andWhitesides, G.M. (2000) J. Am. Chem.Soc., 122(12), 2698–2710.

46. Ercolani, G. (2005) Org. Lett., 7(5),803–805.

47. Glass, T.E. (2000) J. Am. Chem. Soc.,122(18), 4522–4523.

48. Polyakov, M. (1931) Zhur. Fiz. Khim.,2, 799.

49. Wulff, G. and Sarhan, A. (1972) Angew.Chem., 84(8), 364–364.

50. Klotz, I.M., Royer, G.P., and Scarpa,I.S. (1971) Proc. Natl. Acad. Sci., 68(2),263–264.

51. Andersson, H.S., Nicholls, I.A., andSellergren, B. (2000) Techniques andInstrumentation in Analytical Chemistry,vol. 23, Chapter 1, Elsevier, pp. 1–19.

52. Vlatakis, G., Andersson, L.I., Muller,R., and Mosbach, K. (1993) Nature,361(6413), 645–647.

53. Kriz, D., Ramstrom, O., and Mosbach,K. (1997) Anal. Chem., 69(11),345A–349A.

54. Maier, N. and Lindner, W. (2007) Anal.Bioanal. Chem., 389(2), 377–397.

55. Umpleby, R.J., Bode, M., andShimizu, K.D. (2000) Analyst, 125(7),1261–1265.

56. Theodoridis, G. and Manesiotis, P.(2002) J. Chromatogr. A, 948(1–2),163–169.

57. Haupt, K. and Mosbach, K. (2000)Chem. Rev., 100(7), 2495–2504.

58. Shea, K.J., Sasaki, D.Y., and Stoddard,G.J. (1989) Macromolecules, 22(4),1722–1730.

59. Shea, K.J. and Dougherty, T.K. (1986)J. Am. Chem. Soc., 108(5), 1091–1093.

60. Damen, J. and Neckers, D.C. (1980) J.Am. Chem. Soc., 102(9), 3265–3267.

61. Whitcombe, M.J., Rodriguez, M.E.,Villar, P., and Vulfson, E.N. (1995) J.Am. Chem. Soc., 117(27), 7105–7111.

62. Fried, J.R. (2003) Polymer Scienceand Technology, Chapter 3, 2nd edn,Prentice Hall.

63. Mayes, A.G. and Mosbach, K. (1996)Anal. Chem., 68(21), 3769–3774.

64. Haupt, K. and Mosbach, K. (1998)Trends Biotechnol., 16(11), 468–475.

65. Alexander, C., Andersson, H.S.,Andersson, L.I., Ansell, R.J., Kirsch,N., Nicholls, I.A., O’Mahony, J., andWhitcombe, M.J. (2006) J. Mol. Recog-nit., 19(2), 106–180.

66. Dickert, F.L. and Thierer, S. (1996)Adv. Mater., 8(12), 987–990.

67. Dickert, F.L., Forth, P., Lieberzeit, P.A.,and Voigt, G. (2000) Fresen. J. Anal.Chem., 366(8), 802–806.

68. Hayden, O., Bindeus, R., Haderspock,C., Mann, K.J., Wirl, B., and Dickert,F.L. (2003) Sens. Actuators, B, 91(1–3),316–319.

69. Dickert, F.L., Hayden, O., and Halikias,K.P. (2001) Analyst, 126(6), 766–771.

70. Takeuchi, T., Fukuma, D., and Matsui,J. (1998) Anal. Chem., 71(2), 285–290.

71. Lanza, F. and Sellergren, B. (1999)Anal. Chem., 71(11), 2092–2096.


72. Hart, B.R. and Shea, K.J. (2002) Macro-molecules, 35(16), 6192–6201.

73. Schwarz, L.J., Danylec, B., Harris, S.J.,Boysen, R.I., and Hearn, M.T.W. (2011)J. Chromatogr. A, 1218(16), 2189–2195.

74. Minouraa, N., Rachkov, A., Higuchi,M., and Shimizu, T. (2001) Biosepara-tion, 10(6), 399–407.

75. Rachkov, A. and Minoura, N. (2001)Biochim. Biophys. Acta, Protein Struct.Mol. Enzymol., 1544(1–2), 255–266.

76. Andersson, L., Paprica, A., andArvidsson, T. (1997) Chromatographia,46(1), 57–62.

77. Rao, R.N., Maurya, P.K., and Khalid, S.(2011) Talanta, 85(2), 950–957.

78. Park, C.H. and Simmons, H.E. (1968)J. Am. Chem. Soc., 90(9), 2431–2432.

79. Pedersen, C.J. (1967) J. Am. Chem.Soc., 89(26), 7017–7036.

80. Cram, D.J. and Cram, J.M. (1974)Science, 183(4127), 803–809.

81. Graf, E. and Lehn, J.M. (1975) J. Am.Chem. Soc., 97(17), 5022–5024.

82. Graf, E. and Lehn, J.M. (1976) J. Am.Chem. Soc., 98(20), 6403–6405.

83. Lehn, J.M., Pine, S.H., Watanabe, E.I.,and Willard, A.K. (1977) J. Am. Chem.Soc., 99(20), 6766–6768.

84. Lehn, J.M. (1988) J. Inclusion Phe-nom.Macrocyclic Chem., 6(4), 351–396.

85. Jeong, K.S., Muehldorf, A.V., andRebek, J.J.R. (1990) Molecular Recog-nition. Asymmetric Complexation ofDiketopiperazines, vol. 112, AmericanChemical Society, Washington, DC,p. 2, ETATS-UNIS.

86. Jeong, K.S., Muehldorf, A.,Deslongchamps, G., Famulok, M., andRebek, J.J.R. (1991) Convergent Func-tional Groups. X, Molecular Recognitionof Neutral Substrates, vol. 113, AmericanChemical Society, Washington, DC,p. 9, TATS-UNIS.

87. Curiel, D. and Beer, P.D. (2005) Chem.Commun., (14), 1909–1911.

88. Albrecht, M., Fiege, M., and Osetska,O. (2008) Coord. Chem. Rev., 252(8–9),812–824.

89. Hancock, R.D., Melton, D.L.,Harrington, J.M., McDonald, F.C.,Gephart, R.T., Boone, L.L., Jones,S.B., Dean, N.E., Whitehead, J.R., and

Cockrell, G.M. (2007) Coord. Chem.Rev., 251(13–14), 1678–1689.

90. Halime, Z., Lachkar, M., Furet, E.,Halet, J.F., and Boitrel, B. (2006) Inorg.Chem., 45(26), 10661–10669.

91. Vidyadhar, H., Chi-Ying, H.,Puttannachetty, M., Cunningham, R.,Fpner, T., and Thummel, R.P. (1993)Design of Receptors for Urea DerivativesBased on the Pyrido[3,2-g]indole Subunit,vol. 115, American Chemical Society,Washington, DC, ETATS-UNIS.

92. Schalley, C.A. (2007) Analytical Methodsin Supramolecular Chemistry, Chapter12, Wiley-VCH Verlag GmbH.

93. Harada, T., Rudzinski, J.M., Osawa,E., and Shinkai, S. (1993) Tetrahedron,49(27), 5941–5954.

94. Dickert, F., Reif, H., and Stathopulos,H. (1996) J. Mol. Model., 2(10),410–416.

95. Kretschmer, R., Kinzel, D., andGonzalez, L. (2011) Int. J. QuantumChem., 112, 1786–1795.

96. McHahon, G., O’Malley, S., and Nolan,K. (2004) ChemInform, 35(23), 23–31.

97. Dickert, F.L. and Schuster, O. (1995)Microchim. Acta, 119(1), 55–62.

98. Coquiere, D., Le Gac, S., Darbost, U.,Seneque, O., Jabin, I., and Reinaud,O. (2009) Org. Biomol. Chem., 7(12),2485–2500.

99. Dickert, F.L. and Schuster, O. (1993)Adv. Mater., 5(11), 826–829.

100. Travis Holman, K., William Orr, G.L.,Atwood, J.W., and Steed, J. (1998)Chem. Commun., (19), 2109–2110.

101. Perrin, M., Gharnati, F., Oehler, D.,Perrin, R., and Lecocq, S. (1992) J.Inclusion Phenom. Macrocyclic Chem.,14(3), 257–270.

102. Walker, D., Joshi, G., and Davis,A. (2009) Cell. Mol. Life Sci., 66(19),3177–3191.

103. Gravett, D.M. and Guillet, J.E. (1996)Macromolecules, 29(2), 617–624.

104. Kim, J.S. and Quang, D.T. (2007)Chem. Rev., 107(9), 3780–3799.

105. Durmaz, M., Yilmaz, M., and Sirit,A. (2010) Org. Biomol. Chem., 9(2),571–580.

106. Tatsumi, K. and Hoffmann, R.(1981) J. Am. Chem. Soc., 103(12),3328–3341.

References 155

107. Zhao, S. and Luong, J.H.T. (1995)J. Chem. Soc., Chem. Commun., (6),663–664.

108. Mizutani, T., Kurahashi, T., Murakami,T., Matsumi, N., and Ogoshi, H.(1997) J. Am. Chem. Soc., 119(38),8991–9001.

109. Suslick, K.S., Rakow, N.A., andSen, A. (2004) Tetrahedron, 60(49),11133–11138.

110. Rakow, N.A. and Suslick, K.S. (2000)Nature, 406(6797), 710–713.

111. Anslyn, E.V. (2007) J. Org. Chem.,72(3), 687–699.

112. Wang, J., Luthey-Schulten, Z.A., andSuslick, K.S. (2003) Proc. Natl. Acad.Sci., 100(6), 3035–3039.

113. Sen, A. and Suslick, K.S. (2000) J. Am.Chem. Soc., 122(46), 11565–11566.

114. Musto, C.J. and Suslick, K.S. (2010)Curr. Opin. Chem. Biol., 14(6),758–766.

115. Suslick, B.A., Feng, L., and Suslick,K.S. (2010) Anal. Chem., 82(5),2067–2073.

116. Zhang, C., Bailey, D.P., and Suslick,K.S. (2006) J. Agric. Food. Chem.,54(14), 4925–4931.

117. Carey, J.R., Suslick, K.S., Hulkower,K.I., Imlay, J.A., Imlay, K.R.C., Ingison,C.K., Ponder, J.B., Sen, A., and Wittrig,A.E. (2011) J. Am. Chem. Soc., 133(19),7571–7576.

118. Lin, H. and Suslick, K.S. (2010) J. Am.Chem. Soc., 132(44), 15519–15521.

119. Wang, M.X. (2008) Chem. Commun.,(38), 4541–4551.

120. Yoon, S.S. and Still, W.C. (1993) J. Am.Chem. Soc., 115(2), 823–824.

121. Michael, N. and Avidin, G. (1975) inAdvances in Protein Chemistry, vol. 29(eds C.B. Anfinsen, J.T. Edsall, andF.M. Richards), Academic Press, pp.85–133.

122. Weber, P., Ohlendorf, D., Wendoloski,J., and Salemme, F. (1989) Science,243(4887), 85–88.

123. Marcio, D., Luciana, S.B., and Luis,C.L. (2011) Combining machinelearning and optimization tech-niques to determine 3-D structureof polypeptides. Proceedings of the22nd International Joint Conference

on Artificial Intelligence, 2011, pp.2794–2795.

124. Subrahmanyam, S., Piletsky, S.A., andTurner, A.P.F. (2002) Anal. Chem.,74(16), 3942–3951.

125. Matsumoto, K., Torimaru, A., Ishitobi,S., Sakai, T., Ishikawa, H., Toko,K., Miura, N., and Imato, T. (2005)Talanta, 68(2), 305–311.

126. Ju, C., Tang, Y., Fan, H., and Chen,J. (2008) Anal. Chim. Acta, 621(2),200–206.

127. Alcocer, M.J.C., Dillon, P.P., Manning,B.M., Doyen, C., Lee, H.A., Daly, S.J.,O’Kennedy, R., and Morgan, M.R.A.(2000) J. Agric. Food. Chem., 48(6),2228–2233.

128. Chambers, J.P., Arulanandam, B.P.,Matta, L.L., Weis, A., and Valdes, J.J.(2008) Curr. Issues Mol. Biol., 10(1),1–12.

129. Sakurai, K. and Shinkai, S. (2000) J.Am. Chem. Soc., 122(18), 4520–4521.

130. Wang, D., Liu, S., Trummer, B.J.,Deng, C., and Wang, A. (2002) Nat.Biotech., 20(3), 275–281.

131. Yu, R.K., Tsai, Y.T., Ariga, T., andYanagisawa, M. (2011) J. Oleo Sci.,60(10), 537–544.

132. Charych, D., Cheng, Q., Reichert, A.,Kuziemko, G., Stroh, M., Nagy, J.O.,Spevak, W., and Stevens, R.C. (1996)Chem. Biol., 3(2), 113–120.

133. Pan, J.J. and Charych, D.H. (1997)Molecular Recognition and OpticalDetection of Biological Pathogens atBiomimetic Membrane Interfaces,1997, pp. 211–217.

134. Brown, P.O. and Botstein, D. (1999)Nat. Genet., 21, 33–37.

135. Pease, A.C., Solas, D., Sullivan, E.J.,Cronin, M.T., Holmes, C.P., andFodor, S.P. (1994) Proc. Natl. Acad. Sci.,91(11), 5022–5026.

136. Kaempfer, R. (2003) EMBO Rep., 4(11),1043–1047.

137. Mandal, M., Boese, B., Barrick, J.E.,Winkler, W.C., and Breaker, R.R.(2003) Cell, 113(5), 577–586.

138. Tuerk, C. and Gold, L. (1990) Science,249(4968), 505–510.

139. Ellington, A.D. and Szostak, J.W.(1990) Nature, 346(6287), 818–822.


140. Wilson, D.S. and Szostak, J.W. (1999)Annu. Rev. Biochem., 68, 611–647.

141. Stoltenburg, R., Reinemann, C., andStrehlitz, B. (2007) Biomol. Eng., 24(4),381–403.

142. Jenison, R., Gill, S., Pardi, A., andPolisky, B. (1994) Science, 263(5152),1425–1429.

143. Nutiu, R. and Li, Y. (2005) Angew.Chem. Int. Ed., 44(7), 1061–1065.

144. Win, M.N. and Smolke, C.D. (2007)Proc. Natl. Acad. Sci., 104(36),14283–14288.

145. Lee, J.F., Stovall, G.M., and Ellington,A.D. (2006) Curr. Opin. Chem. Biol.,10(3), 282–289.

146. Hermann, T. and Patel, D.J. (2000) Sci-ence, 287(5454), 820–825.

147. Savran, C.A., Knudsen, S.M., Ellington,A.D., and Manalis, S.R. (2004) Anal.Chem., 76(11), 3194–3198.

148. Stadtherr, K., Wolf, H., and Lindner,P. (2005) Anal. Chem., 77(11),3437–3443.

149. Smith, G. (1985) Science, 228(4705),1315–1317.

150. Bratkovie, T. (2010) Cell. Mol. Life Sci.,67(5), 749–767.

151. Huse, W., Sastry, L., Iverson, S.,Kang, A., Alting-Mees, M., Burton,D., Benkovic, S., and Lerner, R. (1989)Science, 246(4935), 1275–1281.

152. Clackson, T., Hoogenboom, H.R.,Griffiths, A.D., and Winter, G. (1991)Nature, 352(6336), 624–628.

153. Rader, C. and Barbas, C.F. III (1997)Curr. Opin. Biotechnol., 8(4), 503–508.

154. Pande, J., Szewczyk, M.M., and Grover,A.K. (2010) Biotechnol. Adv., 28(6),849–858.

155. Heinis, C., Rutherford, T., Freund, S.,and Winter, G. (2009) Nat. Chem. Biol.,5(7), 502–507.

156. Cerruti, M., Jaworski, J., Raorane, D.,Zueger, C., Varadarajan, J., Carraro,C., Lee, S.W., Maboudian, R., andMajumdar, A. (2009) Anal. Chem.,81(11), 4192–4199.

157. Jaworski, J., Yokoyama, K., Zueger, C.,Chung, W.J., Lee, S.W., and Majumdar,A. (2011) Langmuir, 27(6), 3180–3187.

158. Kim, T.H., Lee, B.Y., Jaworski, J.,Yokoyama, K., Chung, W.J., Wang, E.,

Hong, S., Majumdar, A., and Lee, S.W.(2011) ACS Nano, 5(4), 2824–2830.

159. Gold, L. (2001) Proc. Natl. Acad. Sci.,98(9), 4825–4826.

160. Hanes, J. and Pluckthun, A. (1997)Proc. Natl. Acad. Sci., 94(10),4937–4942.

161. Mattheakis, L.C., Bhatt, R.R., andDower, W.J. (1994) Proc. Natl. Acad.Sci., 91(19), 9022–9026.

162. Roberts, R.W. and Szostak, J.W.(1997) Proc. Natl. Acad. Sci., 94(23),12297–12302.

163. Wilson, D.S., Keefe, A.D., and Szostak,J.W. (2001) Proc. Natl. Acad. Sci.U.S.A., 98(7), 3750–3755.

164. Whaley, S.R., English, D.S., Hu, E.L.,Barbara, P.F., and Belcher, A.M. (2000)Nature, 405(6787), 665–668.

165. Matsumura, N., Tsuji, T., Sumida, T.,Kokubo, M., Onimaru, M., Doi, N.,Takashima, H., Miyamoto-Sato, E., andYanagawa, H. (2010) FASEB J., 24(7),2201–2210.

166. Bolon, D.N. and Mayo, S.L. (2001) Proc.Natl. Acad. Sci., 98(25), 14274–14279.

167. Dwyer, M.A., Looger, L.L., andHellinga, H.W. (2004) Science,304(5679), 1967–1971.

168. Moon, J.B. and Howe, W.J. (1991)Proteins Struct. Funct. Bioinf., 11(4),314–328.

169. Dwyer, M.A. and Hellinga, H.W.(2004) Curr. Opin. Struct. Biol., 14(4),495–504.

170. Salins, L.L.E., Deo, S.K., and Daunert,S. (2004) Sens. Actuators, B, 97(1),81–89.

171. Looger, L.L., Dwyer, M.A., Smith, J.J.,and Hellinga, H.W. (2003) Nature,423(6936), 185–190.

172. Schreier, B., Stumpp, C., Wiesner, S.,and Hocker, B. (2009) Proc. Natl. Acad.Sci., 106(44), 18491–18496.

173. Vallee-Belisle, A. and Plaxco, K.W.(2010) Curr. Opin. Struct. Biol., 20(4),518–526.

174. Marvin, J.S. and Hellinga, H.W.(2001) Proc. Natl. Acad. Sci., 98(9),4955–4960.

175. Hellinga, H.W. and Marvin, J.S. (1998)Trends Biotechnol., 16(4), 183–189.

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