Biosensors and the transduction of molecular recognition

Biosensors and the Transduction of Molecular Recognition Michael Thompson and Ulrich J. Krull Department of Chemistry University of Toronto 80 St. George St. Toronto, Ontario M5S 1 A l Canada

The recent history of instrument development in analytical chemistry can be divided into two major areas: instrumentation with an inherently large number of resolution elements (such as hyphenated techniques) that are used primarily for analysis of a broad range of samples with equally broad differences in chemical speciation; and instrumentation for highly selective analyses, which offer a very limited number of resolution elements and are usually ysed to analyze samples of known chemical composition. The major differences in these two types of analysis are related to the amount of useful analytical information that they can convey. A selective sensor provides no structural information about an analyte; instead, it simply indicates the presence and relative concentration of a species of interest. In contrast, broad-based techniques such as GC/MS can provide additional information about the types of chemical species present. We believe, however, that this apparent dichotomy in instrument devel0003-2700191/0363-393A/$02.50/0 @ 1991 American Chemical Society

opment is artificial and could be superceded, possibly in the near future, by application of selective chemical recognition in associated biosensors. Structural information can be derived from selective sensors when multidimensional analysis of analytical signals and/or multiple assemblies of linked devices are used. Theoretically, such analyses could provide analytical information analogous to that gleaned from techniques such as GC/MS (I).Selective biosensors thus have the potential to revolutionize both wet and instrumental analytical chemistry. The analytical potential of biosen-

provided by selective sensors on the quality and reliability of particular analyses, and we will suggest ways to overcome these limitations. We will begin with an analysis of the structural information that is available from a molecule. Although such concepts have been developed theoretically (e.g., for information indices), most of this information cannot be transduced by a selective binding event, as we will show by considering the energetics and mechanisms of selective binding interactions based on molecular recognition. Finally, because the choice of transduction device is crucial in maxi-

sors has long been recognized, and active research and commercial interest have been evident for two decades. So where is the analytical utopia promised by this technology? It has not materialized because of the lack of both understanding and complete control of the interfacial chemical processes. These processes provide selective and nonselective responses; intrinsically allow the transduction of structural information; and control all operational characteristics of sensitivity, speed, reversibility, and stability. In this article, we will illustrate the ramifications of the lack of information

mizing the structural information that can be derived from selective chemistry, we will examine various types of biosensors and discuss the developments needed to make biosensor technology acceptable as a major component of mainstream analytical chemistry. The biosensor concept

The elegant concept of chemical analysis by selective binding first became fashionable in the area of chelation (e.g., the use of “ionic receptors” such as crown ethers in potentiometric experiments). A selective chemical interaction based on molecular properties of

ANALYTICAL CHEMISTRY, VOL. 63, NO. 7, APRIL 1, 1991

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INSTRUMENTATON size and charge distribution results in an enhanced concentration of analyte at an interface and provides a scheme for molecular interactions of biological species. This interaction can be directly monitored in a “passive” detection scheme (i.e., no external energy is supplied to the detector) by observing changes in surface free energy that are associated with changes of electrochemical potential (Figure 1).This experiment represents the desirable case of direct selective measurement of the intrinsic chemical properties of an analyte and demonstrates how the chemistry must set fundamental limitations on selectivity, sensitivity, and response speed, as can be theoretically predicted from the Nernst equation and electrochemical kinetics. Perhaps the most significant sensitivity limitation of this measurement system results from the relatively small amount of energy change associated with each binding event, which indicates that many binding events are necessary to provide a useful signal. For example, a change in electrochemical potential of -60 mV is necessary for a 10-fold activity change of a monovalent ion with a micromolar detection limit for many ion-selective electrodes. These limitations in signal magnitude can be overcome by allowing the chemical interaction to modulate a large amount of energy (signal) derived from an external source. The external energy can be introduced in many different forms, such as electrochemical potential, electromagnetic radiation, and mechanical motion, as used for amperometric (Figure 2a), fluorometric (Figure 2b), and piezoelectric (Figure 2c) sensors, respectively. In such “active” detection schemes there is no intrinsic signal associated with the binding of the receptor, and changes in the interaction of externally

applied energy with the selective matrix are used to detect reactions. This is a secondary measurement of binding, and sensitivity is dependent on how much the environment of the selective matrix is altered by the reactions. Structural information is not directly available from the binding interactions but can sometimes be derived from the interaction of reaction products with externally applied energy. Natural biosensors associated with ion-channel type chemoreceptive processes present an instructive scheme by which the advantages of “passive” and “active” sensing methods can be combined (1). Figure 3 illustrates that a chemoreceptive membrane does not continuously probe a sensing surface by input of external energy. Instead, energy is stored across the membrane as an electrochemical potential. The free energy change associated with a selective binding event is directly used to generate an analytical signal, but in Figure 3 the signal magnitude is not limited by the energy of the selective binding event. An intrinsic amplification of the energy of the binding event is generated by a receptorinduced discharge of the electrochemical energy that is stored across the membrane. This amplification provides high sensitivity for each selective binding event. The membrane system can be considered as an ideal hybrid of active and passive biosensors; the energy for amplification is directly linked to the function of a receptor that controls conductivity, making this a primary measurement of binding. In all three types of biosensors, a signal develops because of alterations in the structure of a receptor or its environment (e.g., complex formation, product evolution). It is not clear, however, how such a signal can be used to deduce structural information about the analyte.

Figure 1. Passive biosensors for direct measurement of an enzyme (E) reactiwl. Potentiometricand thermal sensors (shaded regions) use the intrinsic chemical properties of an analyte to generate a signal.

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Information intrinsic to molecular structure

Selective and sensitive detection of molecular species within complicated matrices without sample preparation is the analytical goal for a biosensor. This requires that the analytical recognition be derived from the charge, size, and three-dimensional steridcharge distribution at the reactive surface of an analyte. The availability of distributed potential contact points in three dimensions for a large organic analyte means that such molecules intrinsically contain more potential binding sites that can be used to generate information for molecular recognition than do small molecules or ions. I t will become apparent that the word “information” applied to molecular recognition is simply

Figure 2. Active biosensors based on application of external energy to measure the extent of selective chemical interactionsat a surface. (a) Amperometric, (b) fluorometric, and (c) piezoelectric. No intrinsic signal is measured from the binding of receptor (X) with analyte.

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Figure 3. Natural chemoreceptor in which a voltage is stored across the membrane and a receptor protein spans the membrane.

a substitute for the physicochemical forces that govern binding. Formal calculation of the amount of information contained in a particular molecular structure has been proposed (2).A structure is considered to contain a certain number of structural elements, N , subdivided into groups, i, of equivalent elements. Each group may occur with a certain probability, Pi = Nilv; and a mean information content per element, I M ,can be derived from (1) IM = - c p i log2 Pi The total information content of the structure, I , can be derived from

I = NIM = N log2 N - C N i log2 Ni (2) Equation 2 considers the variety of elements in the structure. If i = 1,all elements belong to one class and are considered equal; thus, N = Ni and I = 0. According to Equation 2, an information index based on atomic and molecular composition can be calculated. For example, in ethanol, C~HGO, there are a total of nine atoms (i.e., N = 9, and Nc = 2, N H = 6 , and NO = l),and the information index can be calculated from Equation 2 to be 11.0 bits. Similarly, information indices can be calculated by using structural features of functional group distribution, molecular symmetry elements, electron delocalization, and various combinations of these. General indices of molecular complexity have been proposed by Bertz (3) and Dosmorov (4). These indices combine information on atomic composition, type and total amount of chemical bonds, symmetry, and conformations, and indicate the variety of these elements in a structure. Theoretical information indices are useful in the classification of compounds, quantitative structure-property and structure-activity correlations, and computer processing and retrieval of chemical information. Information indices are not absolute properties of molecules; theylave only relative values and, although they are 396 A

related to atomic and molecular properties, these relationships can be complicated. In our opinion, theoretical information indices should be regarded as a shorthand representation of the drawing of a molecular structure and should be given statistical but not physicochemical importance. The information content of an analyte, or alternatively the information that defines the selectivity of a receptor, can be directly related to the size and three-dimensional complexity of the molecule. However, this only indicates that information is available-not how binding is optimally achieved or how this binding process can lead to a direct analytical signal. Energetics of bimolecular interaction The general consensus in biochemistry seems to be that receptor selectivity is directly proportional to binding energy. This concept is misleading in the analytical context, however, because selective binding does not necessarily provide an analytical signal, and it may provide either no signal or an interference to the desired analytical signal. In an analytical context, the term “selectivity” refers to the experimentally observed generation of an analytical signal; this is not necessarily equivalent to thermodynamic selectivity. Although it is true that a receptor will thermodynamically select the ligand with which it can bind most strongly, it is possible that a receptor may bind strongly to several different ligands-some of which may not produce an analytical signal. This idea is illustrated in the hypothetical case in Figure 4. The receptor binds to ligands A, B, and C with approximately equal strength and thus cannot distinguish between them. The

Figure 4. Selective binding of ligands A, B, C, and D to a hypothetical receptor protein. Ligand D has the ability to block ion channels (E) so that only selective binding provides alteration of the biologicalfunction.


total binding energy of ligand D may be similar to that of A, B, or C, and it may also provide an analytical signal by altering the function of the protein in ion transport. This alteration further complicates development of an analytical signal and indicates that selective binding based on statistical probability can confuse the relationships between recognition and development of an analytical signal. This problem can be appreciated by comparing Figures 5a and b, which show different extents of receptor site occupation for varying concentrations of analyte and interfering species. This example is based on the binding processes of Figure 4:Binding and biological signal generation canbe treated as separate phenomena that depend on the properties of the ligands. Recognition of selective binding of the receptor can be derived from detection of the extent of occupation of the available receptor sites (Figure 5a) or from detection of a secondary biological signal induced by an analyte that induces a biological response when binding to the receptor occurs (Figure 5b). Panel 1shows a low concentration of interfering species and little binding of the receptor. No recognition is available on the basis of receptor site occupancy. No biological signal is induced because the analyte is not available. Panel 2 shows that an analytical response based on recognition of receptor site occupancy can be induced by the interfering species, but no biological signal is present in the absence of analyte. Panel 3 shows that an analytical response based on recognition of receptor site occupancy can be induced by the interfering species, yet no significant biological signal develops in the presence of analyte because of the low concentration of the analyte and the partial occupancy of many of the receptor sites by the interfering species. Panel 4 shows recognition of the extensive binding of the receptor, and the analyte occupancy of selective sites is sufficient to produce a biological signal above the background level of noise. The significance of this example for practical measurement situations is derived from the fact that recognition (i.e., an analytical signal) of the extent of receptor site binding is not necessarily related to the occupancy of receptor sites by an analyte in the presence of an interfering species that has a high binding affinity for the receptor. It is this occupancy that forms the basis of most arguments of information theory; yet for this example, accurate information about analyte binding is derived

from a secondary process of biological signal generation induced by an agonist (analyte). In bimolecular recognition the energies responsible for binding can be low to allow for reversibility, and the strength of binding is therefore not a good index of the degree of selectivity or signal generation. In protein-protein and protein-ligand interactions, there is a considerable loss in rotational and translational degrees of freedom associated with binding. Janin and Chothia (5) determined this entropy loss to be 23-30 kcal/mol for the association of two proteins and 17-22 kcal/ mol for the binding of a small molecule to a protein. This unfavorable loss is somewhat compensated for by free energy favoring association that results from the decrease of hydrophobic surface area in contact with water when the molecules associate. This hydro-

phobic contribution gives a stabilization of about 25-40 kcal/mol for most interactions. The size of the ligand binding to the protein receptor is an important factor that determines the stability of the association. Small ligands cannot provide a large reduction of surface area and thus have lower affinities for receptors. The hydrogen bonds and van der Waals forces a t a binding site do not contribute a large amount of binding energy because they replace similar interactions made with water prior to association. They do, however, confer selectivity to the site in the sense that if an incorrect ligand is able to bind to the receptor but is unable to form hydrogen bonds or make van der Waals contacts, an unfavorable enthalpy contribution will result from the loss of these interactions. These short-range forces thus prevent incorrect recognition and

ensure selectivity. Simon (6) and Fraga et al. (7) have described the basic physical chemistry of the bimolecular recognition interaction, particularly with respect to quantum biochemistry. Several assumptions are made in these treatments. First, the recognitive process involves an associative one-to-one reaction between the selective binding site and a variety of ligand molecules, including the ligand of primary interest (the analyte). The type of process will be governed by thermodynamic considerations in contrast to the kinetic control exhibited by reactive (e.g., enzyme) systems. Second, the receiving surface of each receptor molecule is monomeric and contains only one recognition site. This will clearly not be the case for highly complex biopolymers. Finally, the concentration of analyte is much larger than the concentration of binding sites. A site or receptor ( R )imposed on a surface may interact with a variety of species A; in an equilibrium shown in Equation 3.

R+A;+RA;

(3) Here we use the term “receptor” in a general way that does not imply the specialized molecular receptor involved in membrane biology (i.e., it does not imply that there is a mechanism for transduction). The process in Equation 3 is governed by an equilibrium constant where [RAi],[ R ] ,and [Ai]represent activities (concentrations in the usual approximation) of the various species. The probability of recognition, Pi, of any particular analyte, Ai, is where N represents the total population of a n a l s e molecules and the summation is from j to N when j is not equal to i. If the selective recognition of one particular analyte, Ao, is required, then Po> CPi (6) for the range i to N-1, and P; is not equal to 0. If we can assume that recognition is normalized, then 1>Po>1/2

(7) Turning to the energetics of the process, we note that simple thermodynamics indicate that the equilibrium constant is related to the change in free energy. Flgure 5. The concept of molecular recognition shown in the context of (a) the extent of receptor site occupation and (b) the extent of generation of a biological signal dependent on selective binding of analyte.

AGi = -RT In Ki

(8)

Accordingly, the free energy for all associative reactions can be expressed as


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INSTRUMENTATION

where Z represents partition functions and h E i is the important energy of molecular association when R, A, and RA are in the molecular ground state. The usual approximation that follows here is that the partition functions are all unity and, accordingly, the expression for Ki becomes

between ligands) and can be regarded as the amount of uncertainty that exists before an event took place. However, the mathematical notion of information says nothing about its quality or value. The entropy of a system can also be defined statistically by considering the number of possible microstates, N, in which the system can be found

Ki = exp(-hEi/RT)

S=klnN

(10)

The result implies that the probability of recognition is governed by A&, which in turn is related to the nature and number of intermolecular forces invoked between the receptor and the analyte. Before proceeding, we should note that in selective biochemical interactions neither the receptor nor the analyte is probably interacting in its conformational ground state. The empirical classification of the types of intermolecular forces involved in bimolecular recognitive processes has been discussed on numerous occasions and is summarized in Table I. The “correct” recognition of an analyte is often discussed in terms of the spatial orientation of bimolecular interactive forces in an appropriate “lockkey” fit. More recently, particularly in the realm of antibody-antigen, molecular receptor-stimulant, and other biochemical interactions, it has become common to evaluate such interactions by taking into account the degree of cooperativity. In this case, the dynamics of the interaction are more likely to be described by a “hand inserted into a glove” metaphor .

where k is the Boltzmann constant. This definition of entropy is the negative equivalent of the definition of information, where equally probable outcomes can be thought of as the system’s complexions or “solutions.” Entropy decreases when information is obtained, thus reducing the number of complexions; entropy reduction therefore implies information gain. The information is actually furnished by some external agent whose entropy must increase. Fraga et al. (7) have considered a recognition process in which several ligands compete to bind to a single receptor site. They define the probability of recognition of the correct ligand by the receptor in terms of the binding constant for the association. This definition is true for the probability of thermodynamically selecting the correct ligand, but it does not necessarily coincide with the probability of recognition of the analytical signal. Furthermore, when the link is made

between the probabilities of recognition and information theory, no further advancement toward the qualitative and quantitative prediction of the recognition process is made because information theory is purely statistical, whereas the probability of a receptor binding to the correct ligand (i.e., the selectivity) is governed by real physicochemical parameters and not by chance. Thus the selectivity of molecular recognition cannot be treated statistically and cannot be thought of as transferring information in the realm of information theory. Information is generated when an unpredictable outcome results from an experiment and the uncertainty about a system is reduced. In recognition (ideally) a given receptor will selectively bind with one type of molecule, and only information about the occurrence of the binding event is generated (i.e., bound vs. not bound). This provides no structural information about the ligand. A better chemical example of the generation of new structural information is found in random mutation of the genetic code, because in this case a new DNA molecule is produced with a different information content. However, “transferring information in bimolecular recognition” is usually thought of as communication between the molecular partners, where the receptor sees the ligand and recognizes that it is right for binding. The mathematical concept of infor-

Information theory applied to bimolecular recognition

Information theory was formulated in 1948 by Shannon and Weaver (8) as a theory of communication to study the receiving, conserving, processing, and transmitting of signals. The notion of information, however, goes back to Boltzmann (9),who stated that the increase in information about a certain system is related to a reduction in the number of states of the system, and therefore to an entropy decrease. This concept was later generalized by Brillouin (IO),who defined information as negative entropy, or negentropy. Since the origin of information theory, interest in it has grown and applications to numerous scientific fields have been attempted. If an event results in some outcome, i, then the definition of information, I, becomes identical to that in Equation 1. Thus one can get additional information from the experiment by knowing the statistical outcome probabilities. It is the result of a choice (Le., a choice 398 A

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range contacRemoval of water in contact with surface t-ion ( 1 lon-dipole (r Dipole-dipole (rj lon-induced dipole (f‘) bole-induced dipole [A spersion forces (B) Large difference in electronegativity of interactinc molecule: sociation and dissociation of particles

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INSrRLJMEN7ArlON mation implies the generation of a sequence (i.e., that information can be encoded by a sequence of binary digits). Mathematically it is erroneous to speak of information in this case because no sequenced message is either generated or duplicated. For the practical biosensor specialist, the focus of attention therefore must be on the development of an analytical signal that is derived directly from changes of these physicochemical forces and on interpretation of the signal as a quantity that does not by itself provide structural information. The potential of chemometrics as applied to distributed arrays of partially selective sensors to obtain and process information has become a common defense to concerns raised about interferences from the sample matrix. The problem of isolation of interference effects is rooted in the lack of structural information available from a selective binding event. Given that the individ-

ual sensing experiment does not convey structural information, the reduction or subtraction of signal derived from interferences cannot provide any structural information. However, arrays of partially selective sensors can provide binding data that may imply the presence of particular molecular species or functional groups. A multidimensional approach to signal collection can therefore indirectly provide structural information and improve the reliability of the analysis. The chemometric approach to elimination of all interferences is based on knowledge of the action of each sensor in the presence of each chemical component in the system. Unfortunately, this calibration will likely be unsuitable in “real” analyses when new species, which can completely obviate any validity in an analysis, are present (11). The analytical signal, and the intrinsic experimental limitations, originate primarily from the selective chemical

within the common types of biosensors. A: surface occlusion, physical barrier to mass transport, nonselectiveadsorption, binding site activity drift and extraneous signals, diffusion rate limitations, and transport kinetics. B: denaturation, drift of binding site activity, reaction kinetics, controlling association for signal development and dissociationfor regeneration, thermodynamicproduct stability with respect to reversibility,and stability of environment. C: separation distance between analyte or reaction product and electrode surface, conductivity of matrix, and viscosity of matrix with respect to mass transport. D: transmissionof excitationor emitted radiation. E: physical adhesion of sample to surface and kinetics of microviscosity alterationsat hydrated interfaces. F: double layers, polarization,mixed potentials,Debye length considerations, semiconductor poisoning by hydration or ions, and external noise. G: spectral interferences,scattered radiation, optical density, inner filter effects, and external noise. H: multiple wave modes and frequenciesand external noise.

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binding process. Rather than recognizing that a transduction device should derive its function from direct monitoring of the energetics of a selective interaction, the vast majority of devices proposed in the literature monitor the evolution of secondary reaction products, as commonly encountered in enzymic reactions or enzyme-linked assays. Although these devices may work under certain conditions, problems associated with speed, reversibility, and interference are significant. The lack of confidence in the signal-calibration relationship of most biosensors when applied in real analyses has greatly impeded the commercial implementation of this technology. A summary of the problems associated with common biosensors is provided in Figure 6. Common biosensors and limitations

Biosensors are usually associated with selective biochemical “receptors” or recognition elements that are located a t a sensing surface. Common device structures are electrochemical, semiconductor, piezoelectric, and optical. Electrochemical sensors. Electrochemical systems represent the best established family of devices; they can operate amperometrically, coulometrically, or potentiometrically. A typical electrochemical biosensor consists of a polymeric sensing membrane containing a selective reagent (such as an enzyme) interfaced to a measurement device such as an ion-selective electrode (ISE) or a platinum electrode. Several reviews of this device have been published (12-15). Such an enzyme electrode (e.g., the first known biosensor [IS])catalytically converts a target substrate to products, and the development of these molecular or ionic products is then monitored by subsequent redox or potentiometric methods. The enzyme usually is covalently bound or trapped near the electrode surface. This covalent immobilization often results in improved enzyme stability. Enzymes usually are immobilized in an aqueous environment, but recent reports suggest that immobilization in anhydrous environments also is possible (17,18).In most cases, a large excess of enzyme is deposited so that response becomes limited by mass transport and is independent of the concentration of the enzyme. Such an arrangement also extends the life of the electrode, increases the linear range, and reduces susceptibility to low concentrations of interfering species such as inhibitors. The signal can be derived at steadystate conditions where the rate of formation of products is equivalent to the

rate of loss of the product by diffusion from the zone containing the enzyme. The rate of change of the signal can also be used as a kinetic method of concentration determination. Recent work has resulted in methods that use electron tunneling between an enzymatic redox site and a conductive surface to provide an electronic current as an analytical signal (19). This is a relatively fast and efficient detection scheme, limited by enzyme turnover and diffusion rates of substrate and product. These electron tunneling experiments, however, do not measure the energetics of the binding event and are limited by the kinetics and thermodynamics encountered in any enzyme reaction. Coupled reactions of two or more enzymes and/or substrates have been described for extending the variety of analytes that can be measured and for improving the sensitivity of analyses from micromolar to nanomolar detection levels. In the first case, the product of the first selective reaction is consumed in a subsequent reaction to create new products amenable to detection by standard ion-selective or coulometric electrodes. An electro-inactive species can thus be converted to one that can be detected by the measurement electrode. For amplification of an analytical signal, one enzyme regenerates the substrate of a second enzyme, leading to a time-dependent increase of analytical signal magnitude. Better reversibility and control can also be achieved by coimmobilization of an enzyme with a cofactor. In a strategy that involves a sandwich assay or a competitive binding assay, antibody-antigen interactions can be observed if either of the binding species is tagged with an enzyme (12,14). Localization of the enzyme a t the electrode surface by the immunochemical complexation provides a source of measurable analyte if substrate is also available. Such enzyme-linked immunosensors generally are limited by slow response. times and slow reversibility. The sandwich assay method is the basis of one of the more successful commercial biosensing systems, the lightaddressable potentiometric sensor (20).Antibody chemistry is available in assay kits for many different species, such as digoxin, and uses an enzyme such as urease as an amplifier. Immunopotentiometric systems that can directly observe interactions a t an electrode surface have been proposed. Such systems represent one of the few cases in which an attempt has been made to use the energetics of a binding interaction to provide a direct analyti-

cal signal. This experiment requires a change in formal charge on the surface of an electrode (e.g., Pt) as a result of the net electrostatic charge associated with the antibody-antigen complex and the alteration of dipolar fields and/ or double-layer structures by formation of the complex. Unfortunately, these systems are not practically or theoretically functional in complex samples (21)because of dynamic interferences that cannot be quantitatively removed. Sources of interference include electrical noise; nonselective adsorption and surface occlusion leading to drift of binding activity and evolution of mixed potentials; Debye length restrictions for larger biological receptors; and ionic strength, temperature, and pH sensitivity. The inability to identify the analytical signal within the total signal reflects the fact that there is no structural information transferred in the binding process, and it highlights one of the major problems that must be solved: How can the physicochemical forces associated with the selective binding process be quantitatively isolated from the multitude of dynamic binding events that occur at any interface? Semiconductor sensors. Semiconductor devices are available for observation of products from catalytic reactions as well as for observation of temperature alterations and other physical changes caused by biochemical reactions. A representative device is the chemically selective field-effect transistor (CHEMFET), in which the gate of a field-effect transistor is replaced by a chemically selective surface supported on an insulator (22). Modulation of the gate voltage by a biochemical reaction controls an electronic current within the transistor in a device that typically occupies an area of a few square millimeters. Most enzyme-based field-effect transistor (ENFET) devices use a transduction scheme based on potentiometric determination of hydronium ion activity, which has set a fundamental limitation of the type of enzyme that can provide an analytical signal. To overcome this problem, devices such as the ammonia-sensitive iridiummetal oxide semiconductor system have been developed (23). The use of semiconductor technology has made it possible to develop a sensor for microelectronic signal processing and control that is located in a miniature package on a chip. Semiconductor devices for biosensor applications usually use potentiometric methods and are therefore limited by the same problems as other potentiometric sensors.

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Another method of detection by semiconductor devices is based on the use of thermistors to monitor changes associated with enzyme reactions. The heat of reaction, which can be quantitatively determined in a closed reaction cell, represents a direct observation of the free energy changes associated with product evolution (24). This is not identical to observation of the free energy of binding (thermal energy is a product of a reaction) and is, in comparison, a much more complicated physicochemical process. The systems are suitable only for selective coatings based on enzyme reactions and require relatively large quantities of substrate for reproducible signal generation as determined by heats of reaction. These devices are completely reversible, but they must be carefully designed to avoid interference from the reaction products. Piezoelectric sensors. Piezoelectric devices generally are based on specially cut quartz crystals that mechanically oscillate when subjected to an alternating electrical potential. Although other piezoelectric materials can be used, the availability of appropriate quartz crystals has led to extensive investigation of two modes of operation. Surface acoustic wave (SAW) devices have been described as gas sensors, but apparently they cannot operate simply in liquid media (25). Bulk acoustic wave (BAW) devices have been used extensively as gas sensors and recently also as liquid-phase devices with potential for biosensing, as determined by studies of antibodies located on one surface of a device (26). The mechanical oscillation of the crystals (usually a t MHz frequencies) is very regular and can be perturbed by minor mass or microviscosity (physicochemical interactions of surface chemistry with bulk environment) changes caused by selective binding interactions a t the surface of the crystal. Frequency changes smaller than 1 Hz can sometimes be measured reproducibly, providing nanogram sensitivity with respect to adsorption to the surface of the device. Evolution of products from selective reactions is not necessary for transduction to take place. The major limitations of these devices are governed by nonselective adsorption, surface occlusion, and receptor denaturation. Because many of the problems encountered during electrochemical experiments (such as mixed potential effects) do not greatly affect mass response, these devices offer practical advantages over other electrochemical devices. Optical sensors. Significant innovation has been achieved with optical


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sensors by using optical fibers that act as light guides to transmit optical information to and from a remote, compartmentalized target reaction (2730). The most common configuration of such devices is the extrinsic sensor, in which a selective reagent is immobilized at the terminus of a fiber. When an intrinsic sensor is used, optical fibers are directly coated along the surface with selective reagents, thereby permitting optical transduction by evanescent wave stimulation. Sensors that use optical fibers are sometimes referred to as optodes, in analogy to the electrode. The sensitivity of an optical system based on absorption of electromagnetic radiation is greatly limited by short pathlengths, and most fiber-optic biosensors use fluorescence processes for analytical signal generation. Although many of these sensors are designed to detect the evolution of products from enzymatic processes (e.g., a fluorescent dye sensitive to pH changes), it is possible to directly monitor the selective binding event. For example, a fluorescent label such as dansyl chloride located near the binding site of an Fat, antibody fragment may experience significant electrostatic and motional alterations induced by an analyte. The altered fluorescence intensity (average and time-resolved) provides an analytical signal that results from direct interaction of the probe with the free energy changes induced by the binding event and represents a generic process suitable for many different antibodies (31). An external label perturbs the binding event by virtue of the location of the label(s) near or at the receptor site. An alternative sensing strategy based on observation of intrinsic fluorescence from selective chemistry may be possible. For example, Trettnak and Wolfbeis (32)reported a biosensor that monitored fluorescence from the flavin-based cofactor of a glucose oxidase reaction. Fluorescence processes offer the possibility of multidimensional analysis by concurrent observation of wavelength, intensity, polarization, and event lifetime (33,34).In combination, these analytical parameters can be used to define unique solutions to both qualitative and quantitative analyses. Although there is no structural information available in a single type of binding process, the distinct contributions from selective and nonselective binding can be identified in some cases. For example, a selective binding process may alter both the emission wavelength and the intensity of a label close to the binding site. Labels far from the

selective site would not be affected by the selective binding process, but they would be subjected to effects caused by nonselective binding-leading to timeresolved intensity variations that are distinct from those associated with selective binding. Observation of time-resolved fluorescence intensity, perhaps a t a number of different wavelengths, would provide information about the different chemical environments associated with the labels. The binding events would not provide structural information, but the distribution of environments experienced by the fluorescent labels could be used to identify and quantify the physicochemical changes caused by both selective and nonselective binding events. In fact, the use of time-resolved fluorescence intensity, fluorescence polarization, and different fluorophores may provide a method for following protein receptor denaturation and correction of calibration curves, such as for monitoring tryptophan exposure as the tertiary structure of protein changes at an interface (35). Selective chemistry for biosensors

Biosensors can operate passively or actively. They either derive the analytical signal from the energy changes associated with selective interactions (e.g., potentiometry, enzyme thermistors) or probe alteration of the free energy of a selective coating by applying external sources of energy to study interfacial structure (e.g., piezoelectric and fiberoptic devices). The selective chemistry that has been described in the literature provides chemical transduction either by direct alterations of interfacial free energy (e.g., microenvironmental structure and molecular mobility) or by indirect alterations of other associated detectable chemical equilibria (e.g., time-dependent presence of products from enzyme reaction). To prepare useful commercial sensors, there must be a way to provide reliable quantitative detection of the selective binding processes. Thus a device that provides multidimensional information about interfacial structure (piezoelectric,fiber-optic), in combination with selective chemistry that provides large direct alterations of interfacial free energy, has the best potential for success. The chemistry could be improved by linking the free energy change of the binding process to the release of stored energy for signal amplification. Such is the case in chemoreception by nerve depolarization, in which the receptor binding event alters the structure of a protein and/or an ordered lipid mem-

brane to permit discharge of a large stored potential energy by the spontaneous movement of ions through a membrane. A large variety of selective biochemical compounds, including enzymes, antibodies, lectins, and molecular receptors, have been used in biosensors. Although all of these systems provide selectivity, they should also provide substantial intrinsic free energy changes upon binding and reasonably rapid release of analyte to ensure reversibility. They should also eliminate interfering secondary products. Once a selective binding agent with desirable characteristics is chosen, it must be incorporated into a device structure. This process is difficult when one considers binding activity, reversibility, speed, and long-term stability. Immobilization techniques have been used extensively to place proteins on silicate surfaces as encountered at semiconductor sensors, piezoelectric sensors, and quartz or glass optical fibers. The most common technique involves surface modification to provide functional linking groups with materials such as aminopropyltriethoxysilane (APTES) followed by covalent attachment using glutaraldehyde, tresyl (2,2,2-trifluoroethanesulfonyl)chloride, or similar reagents. The most significant problems in immobilization methods are based on the loss of binding activity when selective proteins are attached to a surface. Such activity losses can result from a large number of related factors. The orientation of the protein must be such that the binding site is exposed. The binding site must retain the original tertiary structure associated with ligand recognition and must not be conformationally altered by the chemicals used in the deposition process, by the new strains induced by covalent attachment and surface adhesion, or by nearest-neighbor interactions at a surface that is densely packed with proteins. Ideally, the surface coverage would be two-dimensional to optimize response speed and reversibility (availability of exchange environment). However, three-dimensional layers of proteins (e.g., those trapped in a thick gel layer) may offer a greater amount of active binding agent, possibly improving the S/N. Current immobilization methods offer a sufficient variety of chemical conditions to place some active protein of almost any binding type on silicate surfaces, but loss of activity with time provides serious calibration limitations. The final design and associated characterization of density, orientation, and layer structure represent a difficult analytical task that must be

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Biosensors and the transduction of molecular recognition

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