Survey of the year 2006 commercial optical biosensor literature

Review

Survey of the year 2006 commercial opticalbiosensor literature

Rebecca L. Rich and David G. Myszka*

Center for Biomolecular Interaction Analysis, University of Utah, Salt Lake City, Utah, USA

We identified 1219 articles published in 2006 that described work performed using commercial opticalbiosensor platforms. It is interesting to witness how the biosensor market is maturing with an increasednumber of instrument manufacturers offering a wider variety of platforms. However, it is clear from areview of the results presented that the advances in technology are outpacing the skill level of the averagebiosensor user. While we can track a gradual improvement in the quality of the published work, we clearlyhave a long way to go before we capitalize on the full potential of biosensor technology. To illustrate what isright with the biosensor literature, we highlight the work of 10 groups who have their eye on the ball. To helpout the rest of us who have the lights on but nobody home, we use the literature to address common mythsabout biosensor technology. Copyright # 2007 John Wiley & Sons, Ltd.

Keywords: affinity; Biacore; biomolecular interaction analysis; evanescent wave; IAsys; kinetics; Surface PlasmonResonance; resonant mirror

Received 10 September 2007; accepted 14 September 2007

INTRODUCTION

Using a number of internet-based literature search engines,we uncovered 1219 articles published in 2006 that reportedusing optical biosensor technology. Wewould classify about100 of these articles as reviews of the technology,methodology, and theoretical discussions, and the remaining1100 or so as primary research articles. We considered onlypapers which utilized commercial instruments and wepurposefully avoided specified techniques, such as the use ofnanoparticles with surface plasmon resonance, that wouldrequire their own reviews.

We continue to see a �10% increase in the number ofarticles published each year utilizing biosensor technology.This increase is due in part to the range of instruments nowavailable from 24 different manufacturers. This diversity inplatforms, from the lower cost, bench-top end to the higherthroughput, high-sensitivity end, demonstrates the market’sgrowing need for a full range of sensor technology.Unfortunately, in many ways the technology’s brawnoutweighs the user’s brain.

Our thorough review of the literature uncovered a numberof key areas in which the general user needs to improve interms of experimental design and data analysis. In order toremain positive, we have selected the work from 10 differentteams who provide shining examples of how to properlyimplement, as well as report, biosensor analyses. Byfollowing their lead, we can all learn how to take fulladvantage of what the technology has to offer.

Finally, we take a look at some myths and misconceptionssurrounding biosensors and we use the literature to help

dispel some of the more commonly expressed misgivingsabout the technology.

REVIEWS, THEORY, AND METHODS

Reviews

In the reference list, we subdivided the reviews into articlesthat (a) focus on optical biosensor technology and itscontributions in various applications [1–16], (b) discussoptical biosensors alongside other interaction technologies[17–48], and (c) describe how optical biosensor-basedanalyses, in concert with other methods, have advanced aparticular field of study [49–92].

Optical biosensors. Each year we see optical biosensors’increased impact in both basic and applied research efforts.Tomeet this need, established biosensor companies continueto produce next-generation platforms and new manufac-turers are emerging in the biosensor market. References5,15 and 16 illustrate the number and diversity of platformsnow available or under development, with Cooper’s [5]article in particular providing details of the latest instru-ments available from several of the more familiarmanufacturers.

We recommend two particularly noteworthy references toevery biosensor user. The book, Surface Plasmon ResonanceBased Sensors edited by Homola [9], spans aspects of SPRfrom its theoretical basis to assay development and specificapplications. Book chapters include in-depth discussions ofhow plasmons are generated and used to create sensingdevices, introductions to molecular interactions and masstransport effects, and examples of the diversity of biosensor

JOURNAL OF MOLECULAR RECOGNITIONJ. Mol. Recognit. 2007; 20: 300–366Published online in Wiley InterScience(www.interscience.wiley.com) DOI:10.1002/jmr.862

*Correspondence to: D. G. Myszka, University of Utah School of Medicine

4A417, 50 N. Medical Drive, Salt Lake City, UT 84132, USA.

E-mail: [email protected]

Copyright # 2007 John Wiley & Sons, Ltd.

analyses, ranging from identifying binding partners, rankinganalyte libraries, and epitope mapping to examining kineticsand thermodynamics. Also, this book provides particularlyuseful suggestions regarding the various methods availablefor coupling ligands to the sensor surface and highlights thetechnology’s increasing impact in environment monitoring,food safety, and medical diagnostics. Huber and Mueller’s[10] review article, although targeted toward the drugdiscovery community, is widely applicable to the generalbiosensor user. These authors explain the fundamentalphysics of SPR, describe how to set up an experiment,summarize different immobilization strategies, outline howto analyze data to obtain stoichiometry, specificity, kinetic,affinity, and/or thermodynamic information, and highlightthe biosensor’s application in characterizing biopharma-ceuticals and small molecules.

Other authors provided brief summaries of SPR detection,flow cell construction, surface chemistries, assay formats,and data collection [4,11,15]. Indyk’s [11] review alsoemphasized the technology’s range of biological appli-cations while others detailed how biosensors are used in foodscience [1,7,8,12,14,15] and drug discovery [6]. Also, in oursurvey last year [16], we highlighted excellent biosensorwork published by 10 groups in 2005. We also providedside-by-side examples of good and bad data sets from the2005 literature to illustrate some common experimentalmistakes, as well as to demonstrate the effort required toobtain high-quality data.

Interaction technologies. A wide range of optical andnon-optical interaction technologies is now commerciallyavailable. References 17–48 describe how optical biosensorscompare with other technologies to characterize bindingevents. For example, several authors summarized howbiosensors are now used in health care to diagnose disease[24,43], track blood coagulation 25, and monitor theimmune system 17 and in the environmental/food/veterinarysciences to evaluate food and water quality 18,26,38,39,40,48, and detect drug metabolites 46. Patel 37 reviewedmany of the biosensor technologies applied throughout thefood industry while Cooper 22, Goodnow 27, Ince andNarayanaswamy 29, and Ma et al. [35] described how theseinstruments are advancing drug discovery. Other authorsoutlined the various biosensors’ detection systems used toexamine proteins 20,23,30,33,44, self-assembled mono-layers and polymers [34,42], and antibodies 36. And,References 19,21,28,31,32,41,45, and 47 provided over-views of the recent achievements in developing interactiontechnologies in array formats.

Biological applications. A number of review articlesillustrate how optical biosensors, along with complementarybiochemical and biophysical techniques, have furthered ourunderstanding of protein interactions [50,56–58,61,63,67,79,80,86], as well as the characterization of carbohydrates[55,78,82], oligonucleotides [91], membranes, lipids, films,and other biological interfaces [53,59,68,74,75,83,87,92].Other reviews demonstrate the biosensor’s contribution inbasic research of small molecules [73] and antibodies[62,65,71,88], as well as the development of small-moleculedrugs and protein therapeutics [49,52,54,89,90]. Severalauthors demonstrate the biosensor’s escalating role in two

areas: (1) food composition/contamination and environ-ment/food safety [51,64,70,72] and (2) detection of virusesand bacteria as emerging disease elements and biologicalwarfare agents [66,76,77,81,85]. Downard [60] andNedelkov [84] described how the biosensor can be usedin tandem with mass spectrometry to identify bindingpartners, an application that unfortunately has not yet beenwidely adapted by biosensor users.

Theory

References 93–101 discuss theoretical analyses of bothinstrument construction and the signals produced by bindingevents. For example, Zhu [101] compared biosensordetection systems based on SPR and oblique-incidencereflectivity difference from a theoretical viewpoint whileother investigators modeled various features of sampledelivery to the flow cell surface [94–97,99]. Also, Anderssonand Hamalainen [93] modeled the rate constant informationuseful in structure–activity relationships and Snopok andKostyukevich [100] described aspects of protein adsorptionto the sensor surface.

Methods

References 102–107 provide protocols for the biosensor-based characterization of specific biological systems,although many of the tips these authors provide can beapplied to biosensor analyses in general. Sambrook andRussell [107] outlined a common antibody capture/antigenbinding strategy and Nedelkov and Nelson [106] describedthe steps required in a SPR/MS experiment. Three authorsoutlined methods for characterizing selectin/ligand [102],protein/DNA [103], and protein/lipid [105] interactionsusing traditional Biacore technology. Using DNA/proteinand peptide/protein examples, Goodrich [104] detailed whatis required for, and the data obtainable from, an interactionanalysis using the array biosensor platform manufactured byGWC Technologies.

PRIMARY RESEARCH ARTICLES

We found 1112 primary research articles published in 2006that included work performed using optical biosensors.Although the year’s biosensor work was most oftenpublished in J. Biol. Chem. (13%), Biochemistry (4%),and J. Mol. Biol. (4%), this collection of articles was culledfrom more than 300 journals.

The year 2006 was a watershed year with regards to theinstruments available to the biosensor community. Asdemonstrated in Table 1, we found articles describing workperformed using 39 platforms developed by 24 manufac-turers. While studies using Biacore biosensors weredescribed in 86% of the year’s literature, results reportedfrom the variety of instruments available demonstrates thereare now sensor options to meet the wide range of users’experimental needs and cost requirements.

Of the articles that mentioned which Biacore platformwas used, the majority described using the 3000 (36%), 2000

Copyright # 2007 John Wiley & Sons, Ltd. J. Mol. Recognit. 2007; 20: 300–366

DOI: 10.1002/jmr

2006 OPTICAL BIOSENSOR LITERATURE 301

(27%), X (14%), and 1000 (5%) platforms, with a fewinvestigators describing work performed using Bialite[810], C [1016], J [205,323,325,326,696,752], Q [771,1025,1026,1031,1034,1036,1037], S51 [194,395,591,828,887,902,917,919,929,932,933,937], and Flexchip [135,494,665,1059]. Also, in 2006 several articles describingBiacore’s recently released T100 [363,435,629,695,719,929,937] and A100 [295,455] platforms were published.

In the reference list, articles that describe Biacore-basedstudies are subdivided by application area. Biacoretechnology continues to be used primarily to examinediverse proteins [108–327], antibodies [328–509], receptors[510–634], and peptides [635–729], although its impact inlipid/self-assembled monolayer/polymer studies [730–817]is increasing rapidly. The technology’s breadth of appli-cations is further illustrated by the range of articlesinvestigating oligonucleotides [818–898], small molecules[899–944], carbohydrates [945–981], and extracellularmatrix components [982–1007]. In addition, the numberof articles describing biosensor work in clinical support[1008–1024], food/agricultural/veterinary/environmentalsciences [1025–1038], and membrane/virus/cell character-ization [1039–1050] demonstrate Biacore’s emergingcontributions in these fields. Biacore’s more unusualapplications, including the characterization of fibrils andthe analysis of crude analytes are summarized in References1051–1066.

An overview of these research articles revealed how usersare implementing biosensors and presenting their results.For example:

Analysis type

Seventy per cent of the research groups took advantage ofthe technology’s capability to measure binding constants;the remaining 30% used biosensors in qualitative analyses(e.g., epitope mapping, identifying binders, and comparingrelative binding parameters). Almost half of the quantitativeexperiments yielded kinetic rate constants and the remainderproduced affinity constants (from equilibrium or compe-tition analyses).

Immobilization methods

For the majority of applications that employed standarddextran surface (i.e., CM5 and CM4 sensor chips), directimmobilization was used 60% of the time and indirectcapture 40% of the time. Users overwhelmingly preferred toimmobilize the ligand via amine coupling (96%), although afew groups used thiol coupling (3%) or developed novelimmobilization chemistries [e.g., Reference 717]. Incapturing assays, ligands were most often tagged withbiotin (63%), Hisx (10%), or GST (8%), although severalauthors captured using anti-IgG [353,364,372], protein A, G,or A/G [217,294,388,474,487,542,557], or ligand-specificantibodies [252,558,561]. Clark et al. [143] and Le Pogamet al. [919] minimally biotinylated proteins for streptavidincapture and Zaman et al. [943] blocked a target’s active sitewith a small-molecule binder during immobilization toensure the target was not coupled via this site [943]. More

Table 1. Commercial optical biosensor technologies

Manufacturer Platforms References

Biacore AB Biacore, Bialite, C, J, Q, X, 1000, 2000, 3000,Flexchip, S51, T100, A100

108–1066

Affinity Sensors IAsys, IAsysþ, IAsys Auto 142, 357, 1067–1107Texas Instruments Spreeta 1030, 1108–1123EcoChemie Autolab Espirit 1124–1138Nippon Laser & Electronics SPR-670 1139–1150Analytical m-systems BIOSUPLAR, BIOSUPLAR-2 1151–1159Optrel Multiskop 1160–1167GWC Technologies SPRimager II 292, 1168–1174Sensia b-SPR 1175–1179Artificial Sensing OWLS 1180–1183Genoptics SPRi-Array 1184–1187Reichert Analytical Instruments SR7000 1188–1191Resonant Probes SPRTM 1145, 1192–1195Toyobo MultiSPRinter 1196–1199Corning Epic 1200–1202Farfield Sensors Analight Bio200 1203–1205SRU Biosystems BIND 1206–1208Bio-Rad ProteOn XPR36 1209, 1210DKK-TOA SPR-20 1211, 1212Lumera Proteomic Processor 1213, 1214Nanofilm Technology EP3 1215, 1216Axela Biosensors dotLab 1217HSS Systeme Plasmonic 1218K-mac SPR LAB 254, 1219


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302 R. L. RICH AND D. G. MYSZKA

than 30 research groups used more than one immobilizationmethod, and a handful of investigators directly comparedmultiple immobilization and capture approaches [e.g.,Reference [558]. For the most part, these researchersdiscovered that one method better preserved ligand activityand/or produced higher density surfaces, but the optimalimmobilization method needed to be determined empiricallyfor each system.

Data presentation

We find figures of binding responses essential for evaluatingboth the quality of the experiment and interpretation of thedata. Unfortunately, only 70% of the year’s literatureincluded at least one figure of binding responses and only20 articles (<2%) showed overlaid responses from replicateanalyte injections. Also, few of the quantitative analysesindicated how accurately the interaction model described theresponses. For example, only one-fourth of the kineticanalyses showed the data overlaid with the fit of the selectedinteraction model.

While we are disheartened by the disappointing quality ofmuch of the biosensor work described in the literature, weremain optimistic. Over the past few years, we have seen agradual improvement in the biosensor community’s level ofexpertise, as well as the increased inclusion of figures ofprimary data and descriptions of experimental details in theliterature. In 2006, we estimate that 15% of the year’s reportsincluded reliable biosensor data. From this pool, wehighlight examples that illustrate the versatility of thetechnology and the quality of data we expect to see from abiosensor experiment. In addition, we choose otherexamples from this pool to debunk several of the biosensormyths we see in the literature.

2006 LITERATURE HIGHLIGHTS

We are always pleased to find research articles that describewell-performed biosensor experiments. We focus onparticularly exceptional work published by 10 groups.One way these articles stand out from the rest of the year’sliterature is that they all show data. More importantly, theauthors of these articles do the experiments right. Inaddition, several of these articles demonstrate how thetechnology can be used for applications beyond kinetics(e.g., thermodynamics and stoichiometry), and/or describenew assay designs and novel applications.

High-quality experiments

The first three articles we selected are excellent examples ofbiosensor-based experiments. This collection demonstratesthat high-quality data can be obtained for a variety ofbiological interactions; here we spotlight well-performedRNA/protein, small-molecule inhibitor/enzyme, and proteinligand/receptor studies. Also, each of these articles includesmany figures of biosensor data, which reveal much moreabout an interaction (and the quality of the experiment) thana table of numbers. And finally, these authors provided

important details regarding their assay design and datainterpretation.

To investigate how both structure and sequence contributeto target recognition by RNA-binding proteins, Law et al.[852,853] characterized the interaction between U1Aprotein and its target, the hairpin II of U1snRNA (U1hpII).These researchers prepared panels of mutations in bothbinding partners and determined how each mutation affectedU1A binding to biotinylated U1hpII captured on streptavi-din-coated surfaces. Figure 1 shows how mutating either theRNA (Figure 1A) or protein (Figure 1B) altered theinteraction kinetics.

We consider this analysis to be a particularly well-performedbiosensor study since Law et al. optimized several experi-mental design parameters. First, low densities of RNAwerecaptured on the biosensor surface, which minimized masstransport and avidity effects. Second, the concentrationrange of U1A produced responses spanning from almost nodetectable binding to nearly surface saturation. This wideanalyte concentration range is important in fitting the datasince responses from the higher concentrations provideinformation about surface capacity while responses fromthe lower concentrations contribute to determining the rateconstants. Third, the lengths of the U1A injection time(1min) and wash-out time (5min) were long enough todetect both curvature in the association phase and decay inthe dissociation phase for even the most tightly boundcomplexes. Fourth, each U1A concentration was tested threetimes for binding to the RNA surface. The responses fromthese triplicate injections overlay, demonstrating the bindingpartners were stable throughout the experiment and theregeneration conditions were appropriate. And finally, the fitof a 1:1 interaction model that included a mass transportterm is superimposed over each data set. The agreementbetween the responses and the model indicates the reportedrate constants accurately describe the interaction.

The biosensor-based characterization of this RNA/proteinpair revealed a number of features required for its high-affinity interaction. As illustrated in Figure 1A, lengtheningthe RNA stem did not significantly alter the interaction,whereas other structure and sequence changes dramaticallyaffected the binding kinetics. The responses shown inFigure 1B revealed that a few positively charged residues inthe protein play critical roles in both forming and maintain-ing the U1A/U1hpII complex. These studies advanced theauthors’ understanding of the U1A/U1hpII binding mechan-ism and serve as a model for other protein/RNA interactions.

The role of positively charged amino acids andelectrostatic interactions in the complex of U1Aprotein and U1 hairpin II RNA. Law MJ et al. (2006)Nuc. Acids Res. 34: 275–285.

The role of RNA structure in the interaction of U1Aprotein with U1 hairpin II RNA. Law MJ et al. (2006)RNA 12: 1168–1178.

Interaction kinetic characterization of HIV-1 reversetranscriptase non-nucleoside inhibitor resistance. Geit-mann M et al. (2006) J. Med. Chem. 49: 2375–2387.


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Non-nucleoside reverse transcriptase inhibitors (NNRTIs)block HIV-1 reverse transcriptase (RT) activity but do notbind at this polymerase’s active site. Instead, thesecompounds bind at a separate site and structurally distortthe enzyme. But several RT mutants, identified in

patients treated with NNRTIs, display high levels of drugresistance. To investigate the mechanism of NNRTIresistance, Geitmann et al. [915] characterized a series ofstructurally related inhibitors binding to both wild type andmutant RT.

Figure 1. Kinetic analyses of a protein/RNA interaction. (A) Responses for U1A protein binding to wild type and mutant U1hpII RNAs.RNA structures are shown on the right. (B) Responses for wild type andmutant U1A binding to U1hp11. The protein/RNA structure, withresidues of particular interest highlighted, is shown on the right. In each panel, responses (black lines) are overlaid with the fit of a 1:1interactionmodel that includes amass transport term (red lines). Figures reproduced fromReferences 852 and 853with permission fromthe authors and the RNA Society # 2006.


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Figure 2 shows the responses and global kinetic fits of 12compounds binding to seven RTs amine-coupled to thesensor surface. From a technical viewpoint, this figurereveals the quality of the experiment. For example, in eachpanel the response intensities are proportional to the injecteddrug concentration and the binding profiles can be described

by exponentials. Also, the overlay of each data set with themodel fit establishes the reliability of the reported rateconstants. From a biological viewpoint, Figure 2 demon-strates how both enzyme mutations and inhibitor structureaffect the kinetics of NNRTI/RT interactions. Looking downa column of data sets we can see how different functional

Figure 2. Kinetic analysis of 12 small-molecule inhibitors binding to seven enzyme variants. In each panel, responses(colored lines) are overlaid with the fit of the interaction model (black lines). Reproduced from Reference 915 withpermission from the American Chemical Society # 2006.


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groups in the NNRTIs alter binding to one RT variant.Looking across one row we can compare how different RTvariants recognize one compound.

This kinetic analysis of wild type and mutant RT aids ininterpreting the role of specific amino acids in drug binding,as well as designing drug therapies against specific enzymevariants. For example, the drug panel in general dissociatesfastest from (and has the weakest affinity for) the K103N/L1001 RT mutant, suggesting this mutation is most resistantto NNRTI therapy. Also, the biosensor-based characteriz-ation allows for kinetic profiling of the inhibitors since theresearchers could identify which compounds form high-affinity, stable complexes with each enzyme variant. Thiswork also shows the progress made in development of theseinhibitors: overall, the first commercial NNRTI, nevirapine,displays the weakest RTaffinities, while modifications in themore recently developed inhibitors have in fact resulted inhigher affinity interactions. Perhaps most importantly, thiswork demonstrates the need to take a combinatorialapproach, changing elements in both enzyme and drugstructure, toward understanding this interaction.

Expanding on the idea that specific amino acids (hotspots) contribute significantly to the energetics of a bindinginterface, Moza et al. proposed these residues can beclustered into ‘hot regions’ and cooperative effects aregreater between than within the hot regions [588]. Theseresearchers demonstrated this concept using a T cell receptorsubunit (b chain variable domain 2.1, hVb2.1)/toxic shocksyndrome toxin 1 (TSST-1) binding pair.

The hVb2.1 wild type analog (EP-8), a variant (D1) thatcontains 14 residue changes compared to EP-8 and displaysan affinity 30 000 fold tighter than EP-8, and various otherreceptor mutants were each tested for binding to immobil-ized TSST-1. To identify residues that contribute to D1’shigher affinity, each of the 14 residues of interest were singlymutated in the EP-8 background and characterized using thebiosensor. Figure 3A shows the kinetic and equilibriumanalyses of several mutants. Compared to theWT interaction(upper left panel), the most dramatic changes in kineticsoccurred when glutamic acid was replaced with valine atposition 61 (lower right panel). The left panel inFigure 3B summarizes how each mutation affected bindingfree energy. Four residues are energetically significant.Structural studies revealed two hot regions: three of the fourresidues (51, 52a, 53) are located in one loop (CDR2) and thefourth (61) in a loop (FR3) �23 A away (Figure 3B, rightpanel). Responses for EP-8 injected across TSST-1 mutantspredicted to eliminate binding are shown in Figure 3C.These studies confirmed that the orientation of the structuralmodel shown in Figure 3B was correct. Figure 3D showsintra- and inter-hot region mutants binding to TSST-1 andFigure 3E shows several hydrophobic mutations at position61 did not disrupt the binding interface. From this extensivebiosensor analysis, Moza et al. calculated DGcoop for eachmutant and determined the hot regions were not energeti-cally isolated.

As illustrated throughout Figure 3, Moza et al. werecareful in both their experimental design and data analysis.For example, Moza et al. recognized that for very stablecomplexes (e.g., forD1 and S52aF/K53N/E61Vin Figure 3D),dissociation information needed to be collected until notice-able decay was detected in the signal (i.e., the dissociationphase was monitored for two hours). In addition, theseresearchers performed equilibrium analyses only for datasets in which responses for every analyte concentrationreached equilibrium by the end of the association phase(e.g., in five of the six panels in Figure 3A). All too often wesee data published in which equilibrium constants weredetermined from data sets like that shown for E61V inFigure 3A. Fitting these responses to a binding isothermwould yield an erroneous KD.

Beyond kinetics

Traditionally, determining kinetic parameters has beenconsidered the pinnacle of biosensor experiments. However,much information in addition to ka/kd pairs is extractablefrom advanced biosensor kinetic analyses. For example,determining kinetic parameters under different salt concen-tration and/or temperatures reveals details about the bindingmechanism. The three examples we highlight here demons-trate how the biosensor can be applied to interpretinteraction thermodynamics, electrostatics, and stoichi-ometry.

IkBa sequesters NF-kB outside the nucleus to preventunwanted transcriptional activity. The IkBa/NF-kB complexwas determined in vivo to form a very stable complex havinga half-life of >2 days. As illustrated in Figure 4A, theinteraction is complicated since NF-kB exists as a hetero-dimer and each monomer subunit is composed of severaldomains. Even though the structure of the IkBa/NF-kBcomplex has been solved, the structure did not reveal whichelements regulated this high-affinity interaction. To deter-mine how the individual domains of p65 contribute tothe IkBa/NF-kB interaction, Bergqvist et al. [123] exami-ned IkBa binding to a variety of biotinylated NF-kBconstructs captured on streptavidin-coated sensor surfaces.

Figure 4B shows the responses for IkBa binding to twoNF-kB constructs at two temperatures. In the left panel,IkBa was injected across the nearly intact NF-kB at 378Cand the kinetic fit yielded KD� 40 pM. The middle panelshows how the loss of the NF-kB N-terminal domaindecreased the interaction affinity to �320 pM. For com-parison, data in the right panel illustrate how the dissociationrate slowed fourfold as the temperature was decreased from37 to 258C. The effects of deleting the NF-kB C-terminalhelix 4 are shown in Figure 4C: this truncation increases thedissociation rate constant by more than 1000-fold (KD�460 nM at 258C and�160 nM at 158C), indicating this helixis responsible for the stability of the IkBa/NF-kB complex.

Bergqvist et al. also investigated this interaction usingisothermal calorimetry (ITC) and demonstrated how these

Long-range cooperative binding effects in a T cellreceptor variable domain. Moza B et al. (2006) Proc.Natl Acad. Sci. USA 103: 9867–9872.

Thermodynamics reveal that helix four in the NLS ofNF-kB anchors IkBa, forming a very stable complex.Bergqvist S et al. (2006) J. Mol. Biol. 360: 421–434.


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Figure 3. Kinetic analysis of a receptor/ligand complex. (A) Equilibrium and/or kinetic analysis of wild type (EP-8) and single-sitemutants of human T cell receptor b chain variable domain 2.1 (hVb2.1) binding to immobilized TSST-1. (B) Left: change in freeenergy for each of the single-site hVb2.1 mutants binding to TSST-1. The dotted red line indicates the threshold value used todistinguish energetically significant mutations. Right: Two hot regions for TSST-1 interaction in hVb2.1. In the top panel, theCDR2 hot region is shown in red and FR3 hot region in blue. The b-strand that connects the two hot regions is shown in green. Inthe model of the hVb2.1/TSST-1 complex (bottom), the hVb2.1 hot spots are shown interfacing the TSST-1 hot spots (purple andorange). (C) Equilibrium analysis of TSST-1mutants binding to hVb2.1 EP-8. (D) Kinetic (and equilibrium for E51Q/K53N) analysisofmulti-site hVb2.1mutants binding to TSST-1. (E) Kinetic analysis ofmutations at position 61 in hVb2.1. In each kinetic analysis,the responses are fit to a 1:1 interaction model (shown in black) and the corresponding residual values are plotted below eachdata set. In each equilibrium analysis, the responses at the end of the injection are plotted against analyte concentration and fit toa simple binding isotherm (shown in the insets). Reproduced fromReference 588 with permission from TheNational Academy ofSciences USA # 2006. This figure is available in colour online at www.interscience.wiley.com/journal/jmr


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two functional methods complemented each other andsupported the structural analyses of IkBa/NF-kB. Theirkinetic studies quantitated the slow dissociation rate ofthe IkBa/NF-kB complex, which explains the complex’slong half-life observed in vivo, and revealed that theNF-kB C-terminal helix was required to form a high-affinitycomplex. Their thermodynamic measurements indicatedhow IkBa maintains its stability when complexed withNF-kb but is otherwise quickly degraded.

Fox-1 is an unusual protein since it binds with high affi-nity (KD� 1 nM) to a short nucleotide sequence, GCAUG.Auweter et al. [819] employed both structural and functionalstudies to interpret RNA recognition by Fox-1 protein. Asdetermined by NMR, the RNA sequence, unstructured insolution, binds the protein in a bent conformation(Figure 5A). In addition, the structural studies suggestedthe binding events are regulated primarily by electrostaticand hydrophobic interactions. These researchers used thebiosensor in various assay formats to confirm the affinity ofthis RNA/protein interaction, as well as to characterizethe molecular mechanism by which Fox-1 binds RNAsequences.

In the first set of analyses, a concentration series of Fox-1was injected over a short (10-mer) biotinylated sequence thatcontained the GCAUG sequence and was captured on astreptavidin surface. As shown in the top right panel ofFigure 5B, the protein bound the surface-tethered oligonu-cleotide with high affinity (KD¼ 0.49 nM at 150mM NaCl).In addition, mutational analyses established that one residue(F126) is essential for RNA recognition. Next, the kineticsof the protein/RNA interaction were determined inbuffers having different NaCl concentrations. As shownin Figure 5B, the interaction affinity was strongly dependenton the salt concentration. Furthermore, the linearity of the

plots of logKD, logkon, and logkoff, versus logf� indicatedelectrostatic interactions affected the rate-limiting step inboth complex association and dissociation (Figure 5C). In athird experiment, Auweter et al. mapped the contributions ofindividual hydrogen bonds (both intermolecular andintra-RNA) to complex formation. Mutant oligonucleotidesequences were added to the Fox-1 solution and the mixturewas injected across the GCAUG surface. By measuring howthe mutant sequences competed with wild type for proteinbinding, the researchers could calculate how each mutationaffected the free binding energy in the structure and therebyidentify which hydrogen bonds were lost.

These experiments provide another example of howbiosensor studies can complement structural analyses. Bycharacterizing the interactions of both wild type and mutantprotein and RNA constructs, as well as evaluating thecomplex’s dependence on salt concentration, Auweter et al.determined the extent to which electrostatics and hydrogenbonds regulate Fox-1’s RNA-binding mechanism.

Nucleocapsid (NC) of HIV-1 binds nucleic acids,particularly the repeating d(TG)n sequence, at several stepsduring retroviral replication. Using biosensor technology, inconcert with other biophysical characterization methods,Fisher et al. examined the kinetics and stoichiometry of NCbinding to a simple oligonucleotide (TG)4 and developed aprotocol to detect lower affinity NC/oligonucleotide intera-tions [828]. In these series of biosensor experiments, NCwasinjected under different conditions across biotinylated (TG)4captured at a range of densities on streptavidin surfaces.

At the very low (TG)4 density (�5RU), in which theestimated distance between oligonucleotides on the sensorsurface is approximately twice the length of a fully extendedNC, no cross-linking can occur so it is not surprising that the

Figure 3. (Continued)

Molecular basis of RNA recognition by the humanalternative splicing factor Fox-1. Auweter SD et al.(2006) EMBO J. 25: 163–173.

Complex interactions of HIV-1 nucleocapsid proteinwith oligonucleotides. Fisher RJ et al. (2006) Nuc.Acids Res. 34: 472–484.


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Figure 4. Kinetic and equilibrium analyses of a protein/protein interaction. (A) Schematic illustrating the domain structure of theNF-kB p50/p65 heterodimer. (B) Kinetic analyses of IkBa constructs binding to streptavidin-captured NF-kB at two temperatures.Cartoons of the IkBa constructs used in each experiment are shown in the insets. (C) Kinetic and equilibriumanalyses of an IkBa constructthat does not contain helix 4 binding to NF-kB at two temperatures. Cartoons of the IkBa construct used in this experiment are shown inthe insets. Reproduced from Reference 123 with permission from Elsevier # 2006. This figure is available in colour online atwww.interscience.wiley.com/journal/jmr


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responses for the NC/(TG)4 interaction are well described bya simple interaction model and display 1:1 stoichiometry(Figure 6A, left panel). In addition, this interaction isspecific, as demonstrated by the lack of NC binding tosurface-tethered (A)8 olignucleotide (Figure 6A, rightpanel). This protein/oligonucleotide interaction, however,becomes more complicated at higher (TG)4 surface densi-ties. Figure 6B shows the responses for NC binding to a150RU (TG)4 surface. First, the different profile shapes inthe left panels of Figure 6A and 6B indicate NC can bindmultiple (TG)4 sequences when the oligonucleotide surfacedensity is high enough. Second, in Figure 6B the responseintensities for NC binding are greater than what would beestimated for a 1:1 interaction, indicating the protein canbind the NC/(TG)4 complex. Together, these featuressuggest that both binding partners are multivalent. Further

characterizing this complicated interaction, Fisher et al.tested the salt dependence of NC binding to the 150RU(TG)4 surface to determine the electrostatic contributions tobinding. The decrease in kinetics and stoichiometry withincreased ionic strength (compare the two panels ofFigure 6B) demonstrates that electrostatic forces play asignificant role in this multivalent protein/oligonucleotideinteraction.

Fisher et al. also used a competition approach tocharacterize low-affinity NC/oligonucleotide interactions.In these competition experiments, a standard curve was firstconstructed and then the competitive effects of oligonucleo-tides in solution were monitored. When NC was injectedover sufficiently high densities (>300RU) of (TG)4, theinitial binding rates were linear (Figure 6C, left panel) andthe slopes of these initial response were plotted against

Figure 5. Characterization of the electrostatic contributions in a oligonucleotide/protein interaction. (A) Structure of the Fox-1 RNA-binding domain (RBD) in complex with UGCAUGU. (B) Biosensor-based salt dependence of RNA binding to Fox-1. Responses for Fox-1RBD binding to streptavidin-captured biotin-50-CUCGCAUGU-30 at four NaCl concentrations overlaid with the fit of a 1:1 interactionmodel that includes a mass transport term. (C) Plot of logKD, logkoff, and logkon versus logf�, where f� is the electrostatic contribution(linked to the ionic strength). Reproduced from Reference 819 with permission from the European Molecular Biology Organization #2006. This figure is available in colour online at www.interscience.wiley.com/journal/jmr


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Figure 6. Biosensor-based characterization of HIV-1 NC binding to (TG)4 oligodeoxynucleotide. (A)Responses for 1.7–200nM NC injected across low-density (5–6RU) surfaces of (TG)4 (left) and (A)8(right). In the left panel, responses are overlaid with the fit of a 1:1 interactionmodel (grey lines). (B)Responses for �1–1000nM NC injected across 155–160RU (TG)4 in buffer containing 150mM (left)and 250mM (right) NaCl. (C) Standard curve generation for oligonucleotide competition exper-iments. Left: responses for 1–200nM NC injected across a 340RU (TG)4 surface. Right: NC bindingrate as a function of concentration. Initial slopes from the left panel were plotted against NCconcentration and fit using linear regression. (D) Competition between oligonucleotides in solutionand immobilized (TG)4 for binding to NC. NC solutions (100nM) incubated with varying amounts ofdifferent oligonucleotideswere injected across a�330RU (TG)4 surface. Initial slopes of the bindingresponses were plotted against competitor concentration and fit to an isotherm to obtain IC50

values. Reproduced from Reference 828 with permission from the authors # 2006.


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injected NC concentration to generate the standard curve(Figure 6C, right panel). Then, aliquots of NC at oneconcentration were mixed with a range of concentrations ofa competing oligonucleotide and the mixture was injectedacross the (TG)4 surface. Figure 6D summarizes thecompetition data obtained for a panel of oligonucleotidesand demonstrates this method can be used to obtaininformation about both low- and high-affinity NC binders.

Using other biophysicalmeasurements (tryptophan fluore-scence quenching, fluorescence anisotropy, isothermalcalorimetry, and mass spectrometry), Fisher et al. validatedthe affinity, kinetics, and stoichiometric parameters deter-mined from the biosensor. From this multi-facetedcharacterization of the mechanism underlying NC/(TG)4complex formation, this research team developed a bindingmodel applicable to diverse multivalent interactions. Whilethis information should prove important to understandingHIV biology, it also serves as a well-outlined example ofhow to thoroughly examine complexes that exhibit non-equimolar stoichiometry.

Assay development

In this section we focus on novel approaches to improvingligand activity and increasing analyte throughput. We alsohighlight two alternatives to traditional biosensor analysesthat circumvent the need to regenerate the ligand surface.

Phosphatases are attractive therapeutic targets and thebiosensor’s capabilities in characterizing small-molecule/macromolecular interactions are well established. Theproblem that many groups have faced when setting up drugstudies against phosphatases and related proteins is that thebiosensor assay requires protein preparations that maintainhigh levels of activity after immobilization and throughout thedrug binding study. Usually this entails immobilizing veryhigh densities of the protein target (�1000RU) and mayrequire identifying which coupling condition best maintainsthe target’s integrity. Unfortunately, identifying conditions thatmaintain these enzymes’ activities has proven challenging.However, Stenlund et al. [937] developed a methodicalapproach to optimize enzyme activity and obtain reliable smallmolecule binding responses. To demonstrate the feasibility oftheir approach, this group examined small molecules bindingto PP1 and PP2B, serine/threonine phosphatases, and PTP1B,a tyrosine phosphatase.

First, Stenlund et al. investigated ways to maintainenzyme activity during and after immobilization. Since thethree phosphatases all remained active to some degree afterbeing amine-coupled to the surface, these researchersmodified the solution used in the ligand coupling. Theytested how a variety of additives and a range of pHs affectedeach immobilized enzyme’s activity. For example,Figure 7A shows the responses for a compound bindingto PP1 that was immobilized using four different couplingbuffers. In this example, the addition of DTT, Mn2þ, and anactive-site inhibitor, ocadaic acid, to the standard immobil-

ization buffer (10mM acetate) increased this enzyme’sactivity more than 10-fold. While the addition of DTT, areducing agent necessary in phosphatase studies, and Mn2þ,which serves as a cofactor, each improved the enzymeactivity slightly, addition of the inhibitor, which presumablyacted as a protecting compound to prevent amine coupling atthe binding site, made the most significant difference. Theseresearchers discovered the best immobilization conditionneeded to be identified individually for each phosphastaseand the three enzymes retained activity under slightlydifferent conditions.

By determining the effects of different buffering agentsand ionic strengths, as well as the addition of detergent,Stenlund et al. also optimized the assay buffer conditions toobtain reliable binding responses. For example, they did notconsider using phosphate buffers since precipitates mayform when the divalent cations required for enzyme activitywere added to the buffer. HEPES-based buffers producedunreliable, drifting baselines. In their experience, Tris-basedbuffers were best. In addition, at low ionic strengths(<150mM NaCl), the phosphatase inhibitors bound non-specifically to the unmodified reference surface. The toppanels in Figure 7B shows how the addition of a detergent,Tween-20, improved the data quality for one compound/phosphatase interaction. Again, these conditions weredetermined empirically for each of the three enzymes anddifferent assay buffers proved optimal for maintaining theactivity of each enzyme.

The bottom panels in Figure 7B illustrate how the kineticsof one enzyme/inhibitor pair changed as the assaytemperature increased from 25 to 378C. Since kinetics aretemperature dependent, Stenlund et al. suggested investi-gators consider using temperatures above 258C to increasesampling throughput (since the analyte will dissociate fasterfrom the enzyme surface at higher temperature) and usingtemperatures below 258C to slow down binding events whenthe kinetics are too fast to be determined under standardtemperature conditions.

Once the experimental conditions were optimized,Stenlund et al. examined small-molecule inhibitors bindingto the three phosphatases and identified different mechan-isms of binding. For example, Figure 7C shows theresponses (overlaid with the fit of 1:1 interaction model)for concentration series of two inhibitors binding to onephosphatase. The kinetic characterization of these two PP1inhibitors emphasizes the importance of determining rateconstants rather than simply measuring interaction affinities.Both the on- and off-rates of these compounds binding toPP1 vary by several orders of magnitude and, althoughocadaic acid has a higher affinity for PP1 compared tocantharidin (KD (ocadaic acid)¼ 12 nM vs. KD (canthar-idin)¼ 2.6mM), cantharidin forms a more stable complexwith PP1 (kd (cantharidin)¼ 2.1� 10�4 s�1 vs. kd (ocadaicacid)¼ 0.02 s�1)). Expanding these kinetic studies, theseresearchers examined the interactions of the inhibitor panelwith the three phosphatases. As shown in Figure 7D, somecompounds exhibited binding to all three enzymes whileothers were much more specific. In addition, somecompounds (e.g., calyculin A) bound with different kineticsto the three enzymes. Using this approach, Stenlund et al.could determine inhibitor specificity as well as discriminatebetween interaction mechanisms. This characterization may

Studies of small molecule interactions with proteinphosphatases using biosensor technologies. StenlundP et al. (2006) Anal. Biochem. 353: 217–225.


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Figure 7. Assay development for a small-molecule/protein target interaction. (A) Optimization of amine-couplingconditions. Responses (normalized for immobilization level) for 250nM okadaic acid binding to PP1 immobilized in(1) 10mM acetate pH 5.5, (2) 10mM acetate, 1mM DTT, 2mMMnCl2, pH 5.5, (3) 10mM acetate, 0.5mM TCEP, 2mMMnCl2, pH 5.5, and (4) 10mM acetate, 1mM DTT, 2mM MnCl2, 5 uM ocadaic acid, pH 5.5. (B) Optimization of assayconditions. Top: Responses for 0–50mM cantharidin binding to PP1 without (left) and with (right) Tween-20detergent added to the assay buffer. Bottom: Responses for 1.5–25mM cantharidin binding to PP1 at 258C (left) and378C (right). (C) Kinetic analyses of 8–250nM ocadaic acid (left) and 1.6–25mM cantharidin (right) binding to PP1.Responses are overlaid with the fit of a 1:1 interaction model. For okadaic acid, ka¼ 1.6� 106M�1 s�1, kd¼0.02 s�1,KD¼ 12nM; for cantharidin, ka¼8.0� 102M�1 s�1, kd¼ 2.1� 10�4 s�1, KD¼2.6mM. (D) Inhibitor specificity analyses.Inhibitors were tested in concentration series (or at single concentrations when the dissociation was slow) forbinding to three phosphatases. Reproduced from Reference 937 with permission from Elsevier # 2006.


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be used to develop highly specific, high-affinity phosphataseinhibitors, and in a broader context, serves as a template fordeveloping methodologies to optimize activity in othersmall-molecule/target analyses.

To streamline their phage display library screening pro-cess, Steukers et al. required a high-throughput assay to rankclones based on affinity and kinetics. Recognizing a biosensor-based approach would be suitable (since it requires verylittle material, is compatible with high-throughput analysis,and is amenable to characterizing interactions in crudepreparations), this group developed a two-step assay thatpermitted the kinetic parameters of clones expressed assoluble Fab fragments in Escherichia coli to be determined[1066]. Using Fabs against Tie-1 receptor kinase as a modelsystem, Steukers et al. first determined the Fab concen-tration in each bacterial extract and then determined bindingconstants for each Fab/Tie-1 interaction.

Knowing the Fab concentration was required for thisgroup’s detailed kinetic analysis of the Fab/receptor interac-tions. So, Steukers et al. tested the panel of Fab-containingextracts, as well as a concentration series of a well-charac-terized purified Fab, for binding to high-density (�2000 and�5000RU) Protein A/G surfaces. These high-densitysurfaces promoted mass transport, which produced linearbinding responses in the early part of the association phase(left panel of Figure 8A). The concentration of Fab in eachbacterial extract could be determined using a calibration plotconstructed for the purified Fab (right panel of Figure 8B).This methodical concentration determination proved invalu-able since the Fab concentrations in the extracts varied bymore than 10-fold across the panel.

Next, Tie-1 was immobilized at three densities and thebacterial extracts were diluted to 100 nM Fab and flowedacross these surfaces. Kinetic rate constants could bedetermined from the single sensorgram generated by eachFab (Figure 8B), revealing the Fabs in this screen hadaffinities spanning a>100-fold range (illustrated in the ka vs.kd plot shown in Figure 8C). Figure 8C also demonstrates theimportance of determining kinetic rate constants instead ofonly measuring affinity. The highest-affinity Fabs (circled inFigure 8C) all bound the receptor with affinities of �1 nM,but the kinetics (both the ka and kd) varied by more than10-fold. This kinetic information, which would not beobtainable from equilibrium-based assays, indicates arange of different binding mechanisms were observed forthese Fab/Tie-1 interactions. Finally, to demonstrate thebinding parameters obtained in this kinetic screen werevalid, these researchers purified a subset of the Fabs,performed a thorough kinetic analysis (Figure 8D), andobtained rate constants that agreed with those determinedfrom the kinetic screen.

Steukers et al.’s Fab characterization assay is both rapidand rigorous. They report that over 100 clones can bekinetically screened per week and the few high-affinityclones present in a large phage display panel can beidentified early in the development process. In addition,several features of these experiments demonstrate the carethis group took to obtain reliable binding responses. First,

they used two high-density Protein A/G surfaces in theconcentration determinations and three low-density receptorkinase surfaces in the kinetic analyses. Second, for thekinetic analyses, they collected association phase data longenough to observe curvature and dissociation phase datalong enough to detect significant decay in the responses.Also, the detailed kinetic characterizations (Figure 8D)incorporated a wide range of Fab concentrations (each testedin replicate) that were fit to a 1:1 interaction model (overlaidatop the responses in each panel of Figure 8D) and wereperformed at four different flow rates to assess the effects ofmass transport.

For a different biological system, Takeda et al. [295]developed a method that also examines interactions in crudematerial while increasing sampling throughput. This researchteam’s kinase assay is particularly novel since they trackedenzyme activity, an uncommon application of biosensortechnology; Takeda et al. indirectly detected enzymeactivity by monitoring phosphotyrosine antibody bindingafter the enzymatic tyrosine phosphorylation reaction.In vitro tyrosine phosporylation was performed off-line inmicrotiter plates and these plates, containing the crudereaction milieu, were transferred directly into the platestacker of a Biacore A100 biosensor, which automaticallyextracted the substrate out of the reaction, washed awaycontaminating material, monitored antibody binding, andstripped the antibody/substrate complex from the surface.

Figure 9A depicts a schematic of the three steps involvedin the biosensor phase of this strategy and Figure 9B showsthe sensorgram for a typical binding cycle. First, FLAG-tagged kinase substrate from each microtiter plate well wasstably captured on an anti-FLAG surface while othercomponents of the reaction mixture (including the tyrosinekinases) pass undetected through the flow cell. Second, thelevel of phosphotyrosine (pTyr) was detected with anti-pTyrantibody. Third, the anti-FLAG surfaces were regenerated.With this method, Takeda et al., observed only a slightdecrease in capture levels over hundreds of binding cycles.

To demonstrate the viability of their approach, Takedaet al. tracked the phosphorylation of poly(Glu4Tyr)10peptide (Figure 9C). The peptide was incubated withdifferent concentrations of a tyrosine kinase and evaluated inthe biosensor assay for the levels of phosphorylated tyrosineresidues (Figure 9C, top panel). The linear correlation in theamount of kinase added to the reaction and the anti-pTyrbinding level (Figure 9C, bottom panel) indicated that bothphosphorylation and its detection by SPR were quantitiative.With this biosensor assay, peptides can be screened toidentify the primary sequence recognized by specifickinases.

Using six different protein systems, Takeda et al. alsoestablished the utility of this assay for monitoring proteinphosphorylation; Figure 9D shows the responses theyobtained for phosphorylation of rabbit muscle enolase andbovine brain myelin basic protein (these biosensor-obtainedresults were confirmed by radioisotope studies). This protein

Rapid kinetic-based screening of human Fab frag-ments. Steukers M et al. (2006) J. Immunol. Meth.310: 126–135.

High-throughput kinase assay based on surfaceplasmon resonance suitable for native protein sub-strates. Takeda H et al. (2006) Anal. Biochem. 357:262–271.


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screening assay allows investigators to distinguish betweendifferent kinases’ substrate specificities.

Takeda et al.’s kinase screening method is extremelyefficient and flexible. By using Biacore A100 (which permits

eight samples to be analyzed in one binding cycle), the assaythroughput was almost 1000 samples per day. In addition,capturing the tagged substrate on the biosensor surface (1)reduced the background signal of potentially contaminating

Figure 8. Kinetic screening of Fabs from bacterial extracts. (A) Concentration analysis using immobilizedProtein A/G. Left: responses for 0–500nM control Fab (purified anti-streptavidin) binding to a high-density(5200RU) Protein A/G surface. Right: slope of the initial binding rate plotted against anti-streptavidin Fabconcentration and fit to a straight line to generate a standard curve. In this example, the data from two differenthigh-density Protein A/G surfaces demonstrate the reproducibility of the analysis. (B) Periplasmic extractsdiluted to an anti-Tie-1 Fab concentration of 100nMwere injected across immobilized Tie-1. Responses were fitto a 1:1 interaction model. (C) kon versus koff plot based on the kinetics determined from the crude extracts. Theaffinity (KD) is indicated by the dotted lines. The six highest-affinity clones (KD �1nM) are circled in green. (D)Kinetic analysis of four purified Fabs (3.1–100nM) binding to immobilized Tie-1. The black lines represent theglobal fit of a 1:1 interaction model. Reproduced from Reference 1066 with permission from Elsevier # 2006.This figure is available in colour online at www.interscience.wiley.com/journal/jmr


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materials (e.g., tyrosine kinases, which may be autopho-sphorylated) and (2) allowed the same chip and capturingagent to be used for hundreds of binding cycles. Similarassays could be developed to study serine/threoninephosphorylation, thereby further expanding its applicabilityin screening kinase activity, substrates, and inhibitors.

The classical approach to measuring kinetics is to injectseveral analyte concentrations over one ligand surface and

Figure 9. High-throughput kinase assay. (A) Schematic of the substrate capture, anti-pTyr binding, and regeneration events in eachbinding cycle. In vitro phosphorylation (Step 1) occurs in microtiter plates prior to loading the plates in the biosensor. (B) Typicalsensorgram for the detection of protein substrate phosphorylation. (C) Detection of peptide phosphorylation. Top: overlaid sensorgramsfor poly(Glu4Tyr)10 peptide phosphorylated by different amounts of EphB1 kinase. Bottom: plot of EphB1 concentration versus tyrosinephosphorylation of the substrate peptide. (D) Detection of protein phosphorylation. Top: overlaid sensorgrams for enolase and MBPphosphorylated by different concentration of EphB1 kinase. Bottom: plots of EphB1 concentration versus tyrosine phosphorylation ofthe substrate proteins. Reproduced from Reference 295 with permission from Elsevier# 2006. This figure is available in colour online atwww.interscience.wiley.com/journal/jmr

Analyzing a kinetic titration series using affinitybiosensors. Karlsson R et al. (2006) Anal. Biochem.349: 136–147.


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regenerate this surface after each binding cycle. This methodproduces binding profiles like those shown in the left panelof Figure 10A. By regenerating the surface, the samenumber of ligand binding sites is available for each analytebinding cycle. But, finding the appropriate regenerationcondition is sometimes difficult.

Karlsson et al. described the ‘kinetic titration’ method, inwhich an analyte is injected (usually from lowest to highestconcentration) without a regeneration step between eachinjection [194]. This method produces a sawtooth-shapedbinding profile since bound analyte from an injection has notcompletely dissociated from the surface before the nextinjection begins (Figure 10A, right panel). The depletion ofavailable binding sites can be accounted for mathematicallyand, as demonstrated by the examples highlighted inFigure 10, the responses can be fit to simple interactionmodels to obtain kinetic and affinity information. And, byperforming side-by-side analyses of several systems usingboth the classical and kinetic titration methods, theseresearchers confirmed the rate constants obtained by thisnovel approach are valid.

Kinetic titration can be used to examine a variety ofbiological systems that exhibit a wide range of binding rates.For example, Figure 10B shows the responses obtained fortwo slowly dissociating complexes, an antibody/antigen pair(left panel) and a small-molecule/target (right panel), whileFigure 10C shows the responses for rapidly dissociatingsmall-molecule/target interactions. Figure 10D depicts morerigorous analyses of two small-molecule/target interactions,in which the analyte was injected across two target surfacesof different densities. In addition, the right panel inFigure 10D illustrates that the analyte injection series doesnot need to not be sequential.

Also, kinetic titration is faster than the classical kineticmethod. Since the data are collected in a single bindingcycle, the time required to regenerate and wash the surface iseliminated. Also, it is not necessary to collect muchdissociation information for each analyte concentration.Monitoring the signal decay in the dissociation phase of onlythe highest analyte concentration often provides enoughinformation for data fitting, so the dissociation phases for thelower concentrations can be very short (e.g., Figure 10D, leftpanel). Because of its increased sampling throughput andlack of regeneration requirements, the kinetic titrationmethod is being adopted quickly by the biosensorcommunity [298,478].

Tackling throughput and regeneration issues via adifferent approach, Bravman et al. [1209] describe kineticanalyses performed using ProteOn XPR36, an opticalbiosensor recently released by Bio-Rad. This instrumentincorporates six parallel flow channels and flow path that canrotate 908. As illustrated in Figure 11A, up to six targets arefirst simultaneously immobilized in the six channels, andthen the flow path is rotated 908 and six analytes are injectedacross the surface at one time. In this ‘one-shot’ kineticsapproach, responses are collected for an array of 36

interactions (as well as from the unmodified regions betweentarget channels that serve as reference spots). Therefore,with the simultaneous injection of six analyte concen-trations, a user can collect data for a full kinetic analysiswithout regenerating the surface.

The data sets shown in Figure 11B demonstrate theProteOn XPR36’s reliability and sensitivity. An enzyme wasimmobilized at five densities and with six simultaneousanalyte injections, a small-molecule inhibitor (201Da) wastested at six concentrations. Binding of this small moleculewas detectable on every surface, the response intensitiescorrelated with immobilization density, and each analyteconcentration series could be fit to a simple 1:1 interactionmodel to obtain kinetic parameters.

Using this one-shot approach, Bravman et al. examinedthe kinetics of nine small-molecule inhibitors (mw¼ 157–341Da) binding to the enzyme surfaces (Figure 11C). Thedifferences in kinetics across the inhibitor panel are readilyapparent and the affinities determined for each of these nineinteractions agreed well with the values measured using theclassical kinetic approach with Biacore technology, as wellas with the values obtained from ITC studies. Theseresearchers also demonstrated the ProteOn XPR36’s utilityin thermodynamic studies by characterizing four of thecompounds at a range of temperatures (15–358C). Asillustrated in Figure 11D, this biosensor could track howchanges in temperature affected the kinetics of eachinhibitor/enzyme interaction differently. This figure alsoreveals the speed and reproducibility of the ProteOnXPR36’s data collection: the 1200 binding profiles shownin Figure 11D were obtained from only 40 injections. For themost part, the overlaid duplicate responses from eachanalyte concentration are indistinguishable.

The ProteOn XPR36 is poised to impact a wide variety ofapplications. In high-resolution one-shot kinetic analyses,the parallel collection of responses from different analyteconcentrations not only eliminates the need to regenerate thesurface, but it also decreases sampling time. In one-shotscreening assays, investigators can rapidly optimize both thetarget(s) and analytes. For example, a target could beimmobilized using six chemistries to compare how differentcoupling methods affect its activity. Alternatively, six analytescould be tested against six targets to identify binders andobtain preliminary kinetic and affinity information.

BIOSENSOR MYTHS

In the TV seriesMythBustersTM, a team of scientists tests thevalidity of various urban legends. By the end of each epi-sode, they separate fact from fiction and each myth isclassified ‘busted’, or ‘plausible’, or ‘confirmed’. Here wedo the same for several rumors we often encounter regardingbiosensor technology.

Myth #1: every biosensor signalis biologically meaningful

With today’s automated technologies, it is easy to obtainbiosensor data. The hard part, however, is interpreting theseresponses. Signals arising from experimental artifacts suchas non-specific interactions, bulk shifts, and instrument drift

Exploring ‘‘one-shot’’ kinetics and small moleculeanalysis using the ProteOn XPR36 array biosensor.Bravman Tet al. (2006) Anal. Biochem. 358: 281–288.


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Figure 10. Kinetic titration method. (A) Binding responses produced using classical (left) and titration(right) approaches to determining kinetic parameters. (B) Titration binding profiles for antibody/antigen(left) and small-molecule/protein target (right) interactions that did not completely dissociate during thedissociation phase. (C) Titration binding profiles for two small-molecule/protein target interactions thatrapidly dissociate. (D) Small-molecule/target titrations using two surface densities and sequentiallyincreasing analyte concentrations (left) and an irregular analyte concentration series (right). In eachpanel, the binding responses (black) are overlaid with the fit of a 1:1 interaction model (red). Reproducedfrom Reference 194 with permission from Elsevier # 2006. This figure is available in colour online atwww.interscience.wiley.com/journal/jmr


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Figure 11. ‘One-shot’ kinetics using Bio-Rad’s ProteOn XPR36. (A) Schematic of the instrument’scrisscrossing flow system that produces a 36-spot array. Left: Target is immobilized in six parallel flowchannels. Unmodified regions between the immobilization channels serve as reference spots. Right:After the flow system is rotated 908, six analytes are injected across the sensor surface (in this example,one analyte was injected at six concentrations). (B) Responses for six concentrations of a small-molecule inhibitor binding to an enzyme immobilized at five densities (labeled 1–5), as well as anunmodified surface (6). (C) Kinetic data sets for nine small-molecule inhibitors (labeled A–I) binding toone enzyme surface. (D) Temperature-dependent responses for four compounds. Each data set showsthe overlaid responses for duplicate analyses of each analyte concentration. In panels B–D,the responses (black) are overlaid with the fit of a 1:1 interaction model (red). Reproduced fromReference 1209 with permission from Elsevier # 2006. This figure is available in colour online atwww.interscience.wiley.com/journal/jmr


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are too often attributed to significant binding events. Forexample, the responses in panels A and B in Figure 12 arepredominantly due to bulk shift and drift. In addition, thenegative responses in the dissociation phase ofFigure 12A suggest the analyte bound more to the referencesurface than to the ligand. In Figure 12C, actual bindingevents are overwhelmed by the mismatch between sampleand running buffer. And, although binding is apparent inFigure 12D, these responses cannot be described by simpleexponentials, suggesting the quality of the binding partnersand/or the instrument maintenance were suboptimal. Thisyear’s literature is rife with other examples of biosensor datathat display inadequate assay optimization and/or dataprocessing, which led the authors to convoluted biologicalinterpretations. If the binding responses are unusual, it ismostly likely because of the experiment, not the biology.The bottom line is that a complex binding response may notbe the result of interesting biology. Rather, it is more likelydue to poor experimental design. We call this mythBUSTED.

Myth #2: ‘immobilizing one binding partner altersthe interaction’

In most biosensor experiments, the ligand is not actuallyimmobilized onto a flat surface. Instead, it is tethered to a

dextran (or dextran-like) layer that coats the sensor surface.The dextran layer essentially suspends the ligand in solution.Figure 13 depicts the correlation in affinities determined inseveral side-by-side biosensor and solution-based exper-iments, which demonstrates that a molecule immobilized onthe surface can interact like it would in solution.

Using Biacore and ITC, Papalia et al. [929] establishedthat the affinities of 10 small-molecule inhibitors binding toan enzyme were the same whether the enzyme wasimmobilized or free in solution. Similarly, Wear andWalkinshaw [719] demonstrated the Biacore- and ITC-determined affinities measured at a range of temperatures fora peptide/protein system agreed well (Figure 13B). Evenmore impressive is Navratilova et al.’s [591] comparison ofKDs determined using the biosensor and KIs from whole-cellexperiments (Figure 13C). The agreement between theKDs and KIs for 19 small-molecule inhibitors binding to aseven-transmembrane receptor indicates the receptor on thesensor surface behaves as it does embedded in the cellmembrane.

In Figure 13D, we compiled the affinities reported by fourother groups who performed both biosensor and solution-based experiments [527,629,828,829]. This figure re-affirmsthe correlation in affinities obtainable for a range ofbiological systems if, of course, both analyses are performedcorrectly. In addition, the agreement between the affinitiesobtained for two small-molecule inhibitor/enzyme pairs

Figure 12. Panels A–D depict examples of responses dominated by mismatches between sample and running buffer,instrument drift, and non-specific binding. Reproduced from References 850, 250, 320 and 663 with permission fromthe American Society for Biochemistry and Molecular Biology, Elsevier, Federation of European Biology, and ColdSpring Harbor Laboratory Press # 2006. This figure is available in colour online at www.interscience.wiley.com/journal/jmr


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reported by Li et al. using two surface-based biosensors(Biacore and Akubio, a resonant acoustic profiling biosensorequipped with a proprietary surface) indicates that neithersurface chemistry nor detection method altered the bindingevents [924]. In these examples (and many others publishedin the past few years), the correlation between the surface-and solution-based methods demonstrates the bindingconstants obtained from the biosensor were not affectedby ligand immobilization. This myth is BUSTED.

Myth #3: ‘you never get the same binding constantswhen you reverse the assay orientation’

Oftentimes, choosing which partner to immobilize is basedon the presence of capturing tags and/or the amounts of

material available. Other considerations include the valencyand size of each partner. Of course, avidity effects arisingfrom flowing a multivalent species (e.g., antibodies) across aligand surface can affect the reported rate constants. And,although testing small-molecule analytes will produce smallresponses, immobilizing small molecules can alter theirentropic properties. Today’s biosensor technology is sensi-tive enough to reliably measure responses for molecules ofless than 100Da so we recommend always testing smallmolecules in solution against immobilized targets.

For the majority of other systems, however, the bindingconstants should be independent of which partner isimmobilized on the surface. Examples of investigatorstesting interactions in both orientations are shown inFigure 14, demonstrating that both equilibrium and kineticsanalyses can be performed with either partner immobilized

Figure 13. Correlation in affinities determined using surface- and solution-based methods. (A) SPR- and ITC-determinedaffinities of 10 small-molecule inhibitors binding to an enzyme [929]. The dashed line represents a correlation coefficient of 1.(B) Affinities determined at six temperatures for a peptide/protein interaction using a Biacore T100 biosensor and ITC [719]. (C)Affinity constants (KDs) measured using a Biacore biosensor versus inhibition constants (KIs) obtained by whole cell-basedexperiments for 19 small-molecule inhibitors binding to a seven-transmembrane receptor [591]. (D) Affinities obtained usingBiacore and solution-based methods for a variety of interactions: oligonucleotide/protein by fluorescence anisotropy andtryptophan fluorescence quenching (squares) [828], small molecule/oligonucleotide by ITC (triangle) [829], and protein/proteinby ITC (filled circles from Reference [527], unfilled circles from Reference 629). The dashed line represents a correlationcoefficient of 1. Panels A–C were reproduced from References 929, 719 and 591 with permission from Elsevier # 2006. Thisfigure is available in colour online at www.interscience.wiley.com/journal/jmr


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Figure 14. Interactions examined in both orientations. (A) Equilibrium analysis ofthe HLA-G (a MHCI molecule) monomer/leukocyte Ig-like receptor B1 (LILRB1)interaction. Left: responses and binding isotherm (black) for the HLA-G monomerbinding to the immobilized receptor. Right: responses and binding isotherm(green) for the receptor in solution binding to immobilized HLA-G. The isothermsincluded in the bottom panels for other constructs are explained in Reference 612.(B) Kinetic analysis of the human serum albumin (HSA)/MHC-related Fc receptorfor IgG (FcRn) using immobilized receptor (top) or immobilized HSA (bottom).Responses overlaid with the fit of a 1:1 interaction model are shown in the leftpanels and the effect of pH on the rate constants is summarized in the right panels.Reproduced from References 527 and 612 with permission from the AmericanChemical Society and The American Society for Biochemistry and MolecularBiology # 2006. This figure is available in colour online at www.interscience.wiley.com/journal/jmr


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to obtain similar binding parameters (and, the overlay of thefit and responses in Figure 14B suggest that this experimentcould be further optimized so that the rate constantsdetermined in the two orientations may agree even more)[527,612]. Whenever feasible, we encourage researchers toexamine interactions in both orientations (and explore avariety of different immobilization methods). We declarethis myth BUSTED.

Myth #4: ‘kinetics can provide detailed informationabout binding interactions’

In addition to the example shown in Figure 8C, Figure 15provides additional evidence that supports the importance ofdetermining rate constants. For example, Arnold and

Fremont [518] kinetically characterized the chemokineCCL3 binding to wild type and 12 mutant EVM1(Ectromelia virus, Moscow strain) proteins (two data setswith overlaid fits of a 1:1 interaction model are shown in theinset of Figure 15A). A kinetic distribution plot of theseinteractions revealed two kinetically distinct classes thatcluster based on kd (Figure 15A main panel). Since the twoclasses of interactions display similar (and relatively narrow)ranges in ka, the differences in affinity (clustered at18–140 pM and 0.72–10 nM) can be attributed to how eachmutation affected the stability of the CCL3/EVM1 complex.

Panels B–D in Figure 15 further emphasize the wealth ofinformation available from kinetic analyses. In Figure 15B,several compounds cluster along the KD¼ 10�6M iso-affinity line, but display different rate constants. Likewise,the kinetic distribution plots of two antibody/antigen studies

Figure 15. Kinetic distribution plots. (A) ka versus kd plotted for the chemokine CCL3 binding towild type andmutant EVM1proteins. Thetwo classes of interactions are shown in green and red. The insets show the responses and fits of example interactions (corresponding tothe unfilled circles in the main panel) from the two kinetic classes. Rate constants plotted in the main panel were published in Reference518. (B) ka versus kd plotted for 15–19 replicate studies of 10 small-molecule inhibitors (labeled 1–10) binding to an enzyme. (C) ka versuskd plotted for 90 positivemAbs selected froma screenof 384 hybridoma samples. (D) ka versus kd plotted for Fab showing the progressivekinetic effects during affinity maturation. Panels B–D and the insets in panel A were reproduced from References 518, 929, 455 and 435with permission from the American Society of Microbiology and Elsevier # 2006. This figure is available in colour online at www.interscience.wiley.com/journal/jmr


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(Figure 15C and 15D) emphasize the mechanistic differ-ences between antibodies displaying similar antigenaffinities (e.g., the data points marked with an asterisk inFigure 15C). These differences in kinetics should not beoverlooked when selecting which antibodies and com-pounds to pursue as therapeutic candidates and in drugdevelopment. These examples are only a few of the manykinetic experiments that turn this myth into a FACT.

Myth #5: ‘optical biosensors cannot measure theaffinities of weak interactions’

Much of the interaction technology at our disposal (e.g.,ELISA, pull-downs, fluorescence, and radio-ligand assays)is geared toward measuring high-affinity systems. Unfortu-nately, these technologies often have restrictions when itcomes to measuring low affinities. These separation assaysrequire washing steps before a signal is recorded. When theinteraction is weak, the complex may dissociate and bewashed away before being detected. And, although othersolution methods (e.g., NMR, AUC, and ITC) measure theamount of complex formed in the presence of free materialand therefore can be used to study weak interactions, theytypically require large amounts of sample and/or have lowthroughput.

Optical biosensors, on the other hand, are well suited toanalyzing weak interactions since they directly measure theamount of complex in the presence of free material, requireno labels, consume little sample, and have comparativelyhigh throughput. Also, the biosensor surface itself can beused to extract a binding partner from crude samples, whichdramatically reduces sample preparation time. The factorsthat may limit biosensor-based measurements of weakinteractions (i.e., high analyte concentrations and lownon-specific binding) are not inherent to this technology;instead, these are limiting factors for any method used toexamine transient binding events.

Figure 16 depicts two group’s biosensor-based charac-terization of very weak (KDs� 250–2000mM) interactions.In two separate experiments, Kato et al. [661,662] measuredthe responses for a protein (at concentrations up to 482mM)binding to a peptide surface to obtain KD¼ 260� 20mM(Figure 16A). Figure 16B shows the responses and isothermsfor a protein, ubiquitin (at concentrations up to 2500mM),binding to a panel of immobilized protein binding partners(the wild type partner and four mutants) [108]. Since theresponses for ubiquitin binding by the wild type protein andthe L10D mutant provide information about surfacecapacity, the affinities of each of the interactions could bedetermined. References 114 and 213 provide other examplesof Sundquist and co-workers’ studies of particularlylow-affinity interactions. This myth is BUSTED.

Myth #6: ‘mass transport makes it impossible toresolve kinetics’

For years now, modeling tools have been available thatextract binding parameters from mass transport-influencedresponses. Perhaps this myth arises when users do notrecognize mass transport effects in their data sets and try to

fit the responses to a simple 1:1 interaction model. In bindingresponse profiles, mass transport’s most apparent effect isthe early portion of the association phase becomes morelinear. In addition, the dissociation of the complex willappear slower than expected based on the kd determined bymodel fitting. These effects are illustrated in Figure 17A:mass transport-influenced responses are shown in the leftpanels while the responses in the right panels do not displaymass transport effects [432].

Figure 17B shows two examples of responses obtainedunder particularly mass transport-limiting conditions [897].To illustrate what the responses would look like if masstransport was not occurring, these data sets are overlaid witha set of simulated responses (shown in green) we modeledusing the reported rate constants. This overlay emphasizeshow mass transport slows down the apparent rate in both theassociation and dissociation phases. (Hint: we recommendalways fitting responses to a 1:1 interaction model thatincludes a mass transport term. If the interaction is notinfluenced by mass transport, the mass transport parameterin the fit will approach infinity and the intrinsic rateconstants will be unaffected.) This myth is so BUSTED.

Myth #7: optical biosensors can be used to detectwhole cells binding to immobilized targets

Traditionally, we have been leary about using the biosensorto examine whole cells binding to immobilized targets forseveral reasons. First, the delivery of cells to the sensorsurface is difficult since under flow larger particles will befocused midstream rather than diffuse to the edges of thechannel. Second, quantitative cell binding analyses wouldneed to account for avidity effects since cells will bindmultivalently to the surface. And, the biosensor datapublished in most of the reports of cell binding experimentshas been dubious at best. Too often the responses were oddlyshaped and/or very low (<20RU). Since cells are very largeanalytes, we expect the binding signals produced to be verylarge. Three studies published in 2006 suggest thatbiosensor-based cell binding studies may in fact be feasible.Each of the experiments shown in Figure 18 showreasonably shaped binding profiles and the responses aremore on the order of what we would expect for largeanalytes. In addition, each analysis included several controlexperiments to confirm the specificity and reproducibility ofthe cell/ligand interaction.

Using the biosensor, Oli et al. [1043] compared how threeStreptococcus mutans preparations recognize salivaryagglutinin. Cells expressing the surface adhesion P1 boundto the immobilized agglutinin, while a P1-negative mutantand a sonicated supernatant did not (Figure 18A). Byregenerating the ligand surface and retesting cell bindingseveral times, these researchers demonstrated the reprodu-cibility of these cell binding responses.

To investigate the structure of bacterial outer-membraneproteins (Omps), Visudtiphole et al. [1049] examined apanel of E. coli expressing various OmpG mutants thatcontained inserted FLAG sequences. Biosensor analysesrevealed which FLAG sequences (and neighboring OmpGregions) were exposed to the environment or inaccessible ineither the membrane or the periplasmic space (Figure 18B).


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To confirm these findings, this group compared the whole-cell responses with those obtained after disrupting the cells.

Hearty et al. [1041] developed an assay to characterizeListeria monocytogenes binding to an antibody that recog-nizes a surface-localized protein expressed in L. monocyto-

genes but not in non-L. monocytogenes cells (Figure 18C).As control experiments, this group showed that heatinactivation of the cells eliminated binding, a panel ofnon-L. monocytogenes cells did not bind to the antibodysurface, and the L. monocytogenes responses were concen-

Figure 16. Biosensor-determined affinities of weak interactions. (A) Two independent bio-sensor measurements of the affinity of a WW domain protein binding to its immobilizedpeptide binding partner. Reproduced from References 661 and 662with permission from TheAmerican Society for Biochemistry and Molecular Biology and Bentham Science PublishersLtd.# 2006. (B) Response data and isotherms for ubiquitin binding to GST-tagged wild typeand fourmutant constructs derived from the ESCRT-II protein captured on the sensor surface.This figure was provided by the authors (personal communication) and are summarized inSupplemental Table 2 of Reference 108. This figure is available in colour online at www.interscience.wiley.com/journal/jmr


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tration dependent. These studies served as the basis foridentifying and isolating the L. monocytogenes surfaceprotein that may serve as a marker in analyses to detectL. monocytogenes and other pathogens in food samples.

Based on these reports, we may be witnessing theearly stages of assay development to obtain reliable cellbinding data using the biosensor. We find this mythPLAUSIBLE.

Figure 17. Binding responses influenced by mass transport. (A) Responses for concentration series of two polyamides binding toimmobilized double-stranded hairpin DNA containing the sequences TGCACA (left) and AGCACA (right). Each data set is overlaidwith the fit of a 1:1 interaction model (the model used to fit data in the left panels included a mass transport term). (B) Responses(black lines) for a concentration (HEL) binding to immobilized anti-HEL mAbs HC1 (left), HC2 (right). The red lines depict the fit of a1:1 interaction model that includes a mass transport term. Green lines are simulations that depict the expected responses if masstransport did not occur. Reproduced from References 432 and 897 with permission from Blackwell Publishing and the AmericanChemical Society # 2006. This figure is available in colour online at www.interscience.wiley.com/journal/jmr


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Myth #8: ‘always immobilize the multivalentbinding partner’

Most of the time we caution against testing the multivalentbinding partner in solution since this can complicate theanalysis by introducing avidity. However, there are timeswhen we may want to mimic the avidity events that occurin vivo; for example, when antibodies bind to receptorsexposed on the cell surface (Figure 19A). The biosensorsurface can be used to mimic this behaviour by testingantibody in solution binding to immobilized receptors(Figure 19B). We look forward to seeing good examplespublished that describe this approach to using biosensors andwe call this myth BUSTED.

Myth #9: ‘optical biosensors can be used to measureconformational changes’

Actually, this myth began in 1995 when we first introducedthe concept of using numerical integration, which permitsone to create any reaction mechanism (Anal. Biochem. 227:176–185). Then, we showed this two-step conformational-change model (AþB¼AB¼AB�) could describe a variety

Figure 18. Intact cells binding to ligand surfaces. (A) S. mutans(wild type, mutant, and sonicated) binding to immobilized sali-vary agglutinin. (B) Responses for E. coli expressing a panel ofFLAG-tagged Omp mutants binding to immobilized anti-FLAGantibody. (C) Responses for L. monocytogenes cells binding toan immobilized L.monocytogenes-specificmAb before and afterheat inactivation of the cells. Reproduced from References 1041and 1043, and 1049 with permission from Elsevier # 2006.

Figure 19. Example of using the biosensor to mimic biologicallyrelevant avidity events. (A) In vivo, a bivalent antibody wouldbind simultaneously to two receptors presented on the cell sur-face. (B) Biosensor assay design thatmimics antibody binding tocell surface-presented receptors: antibody in solution is testedfor binding to immobilized receptors. This figure is available incolour online at www.interscience.wiley.com/journal/jmr

Figure 20. Monitoring self-association events using optical bio-sensors. (A) Assay design for a biosensor experiment intended tomonitor self-association between molecules in solution andimmobilized on the sensor surface. (B) Since the dextran layeris non-rigid and analyte should be tested at concentrations nearKD, the immobilized molecules, as well as those in solution, canassociate, thereby blocking any surface/solution interaction. Thisfigure is available in colour online at www.interscience.wiley.com/journal/jmr


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Figure 21. Kinetic analyses of an antibody/antigen interaction performed by 22 study participants (A–V). (A) Except for V, each panelincludes the responses obtained for antigen binding to antibody immobilized at three densities. The three left data sets depict antigenconcentration series examined using a relatively short dissociation time and the right three data sets show responses collected for thehighest antigen concentration using amuch longer dissociation time. The responses (black) are overlaid with the fit a of a 1:1 interactionmodel (red). (B) Distribution of the kinetic rates and equilibrium binding constants determined by the participants. The average value ineach histogram ismarked by a dotted line and the standard deviation is shown in gray. Reproduced from Reference 395 with permissionfrom Elsevier # 2006. This figure is available in colour online at www.interscience.wiley.com/journal/jmr


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Figure 22. Kinetic analyses of 10 smallmolecules (labeled 1–10 across the top row) binding toone protein target interaction performed by 26 study participants (B–AA). The responses(black) are overlaid with the fit a of a 1:1 interactionmodel (red). Some data sets could not befit to obtain binding parameters; individual data sets omitted from the analysis are indicatedby red Xs and data from participants A, J, K, N, O, P, R, and S were excluded entirely.Reproduced from Reference 929 with permission from Elsevier # 2006. This figure isavailable in colour online at www.interscience.wiley.com/journal/jmr


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of complex response profiles—not necessarily only thosewith a conformational change. Unfortunately, we often findgroups applying this model just because ‘it fits the data’.

In 2006, several authors used the conformational-changemodel. If you look carefully at their results, the confor-mational change was predicted to occur on a time scale ofminutes to hours. We find it hard to believe that abiologically important conformational change would takethis long to complete. (When you look at classically studiedconformational-change systems, you see that these eventsoccur on the nanosecond to millisecond time scale, which istoo fast for biosensors to measure). In today’s literature wefind too many groups go fishing to find the model that fitstheir data and become somehow fixated on the conforma-tional-change model. Instead, it is much more likely thatwhat they are seeing in their sensor data is a reflection ofaggregation, avidity, or heterogeneity. Until we seeconvincing evidence that a half-life of 60min is biologicallyrelevant, we recommend you stay away from this model andcall this myth BUSTED.

Myth #10: ‘optical biosensors can be usedto monitor self-association’

We think biosensor technology is powerful but we admit thatit does have some limits. Nothing makes us chuckle morethan seeing groups apply sensor technology to study aself-associating events like dimerization. This will not workfor several reasons because, under the conditions used in abiosensor assay, the molecules on the surface (and those insolution) will self-associate even before the analyteinjection. First, let us assume you immobilize a proteinonto a dextran surface (which is the most commonapproach). Numerous groups have shown how the dextransurface is fluid and mobile. This means that one immobilizedprotein molecule can easily find another immobilizedbinding partner to self-associate with. The next problemis that once immobilized, the protein’s local concentration isvery high. So, the immobilized protein is mobile and itsbinding partner is very close by, which helps drive complexformation. Therefore, the most thermodynamically stablestate is for the surface-immobilized molecules to self-associate. Finally, for a significant population of ligand/analyte complexes to form, the analyte needs to be injectedat concentrations near (or above) the KD of the interaction. Ifthe self-associating protein is in solution at a concentrationnear its KD, by definition half of the protein molecules willbe self-associated and therefore not available to interact withthe sensor surface. So, in this example you would be in-jecting a self-associated protein over a surface that is alreadyself-associated and you will see no binding. (Figure 20)

In the 17 years of commercial biosensor literature, youwill not find a single example of where someone has taken awell-characterized dimeric system and used the biosensor toextract the correct binding constants. Our point is that thereare times when other technologies are better suited to answera question (Hint: to characterize self-association, useanalytical ultracentrifugation), so we call this myth BUSTED.

Myth #11: ‘there must be some trickto getting good data’

We suspect that much of the bad biosensor data we see resultfrom dirty instruments, poorly behaved reagents, suboptimalassay design, and/or sloppy experimental technique. Todemonstrate that biosensor users ranging in expertiselevel and using a variety of biosensors could obtainhigh-quality data, we oversaw two studies that determinedthe reliablity of biosensor data obtained by differentexperimentalists. For these comparative studies, 22 partici-pants examined a high-affinity antibody/antigen interactionand 26 participants examined 10 small molecules bindingto one protein target. To gauge only the variability betweenthe participants’ techniques, we provided each with thematerials required for the analyses as well as a detailedprotocol.

The data obtained from these studies are summarized inFigures 21 and 22. As shown in Figure 21A, each of the 22participants obtained interpretable responses for theantibody/antigen interaction (monitored at three surfacedensities of antibody). Each data set was well described by a1:1 interaction model and the variability in the reported rateconstants was�14% (Figure 21B). In the second study, 19 ofthe 26 participants were able to measure interactionparameters for most, if not all, of the 10 compoundsbinding to the protein target (Figure 22). The experimentalstandard error averaged 31% for the small-molecule study.The higher standard error in this study was not unexpectedsince this was a more challenging experiment (the variabilityin these rate constants is demonstrated in the kineticdistribution plot shown in Figure 15B.).

These studies, together with other similar comparativestudies, emphasize that anyone using a well-maintainedinstrument, characterizing a well-behaved biological sys-tem, and employing careful experimental design can obtaindata of similar quality to that shown in Figures 21 and 22.This myth is BUSTED.

SUMMARY

Perhaps we will look back on 2006 as a major turning pointin the optical biosensor field. The technology’s breadth ofapplications has expanded in ways we could not havepredicted a few years ago. Recent innovations in instru-mentation have increased sampling throughput and sensi-tivity while the results obtained from SPR array platformssuggest we will see widespread adoption of this imagingformat in the near future. Now we are approaching a criticalmass of users. As the skill level within the communityimproves, we foresee more investigators publishing high-quality data and more manuscript reviewers recognizing theelements required in a well-performed biosensor exper-iment. Without a doubt, the increasing contributions fromoptical biosensors will continue to advance basic researchand drug discovery, making sure none of us are left home inthe dark.


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Theory

93. Andersson K, Hamalainen MD. 2006. Replacing affinity withbinding kinetics in QSAR studies resolves otherwise con-founded effects. J. Chemometrics 20: 370–375.

94. Edwards DA. 2006. Convection effects in the BIAcore dextranlayer: surface reaction model. Bull. Math. Biol. 68: 627–654.

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101. Zhu XD. 2006. Comparison of two optical techniques forlabel-free detection of biomolecular microarrays on solids.Opt. Commun. 259: 751–753.

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1038. Zezza F, Pascale M, Mule G, Visconti A. 2006. Detection ofFusarium culmorum in wheat by a surface plasmon reso-nance-based DNA sensor. J. Microbiol. Meth. 66: 529–537.

Membranes, viruses, and cells1039. Chai Z-F, ZhuM-M, Bai Z-T, Liu T, TanM, Pang X-Y, Ji Y-H.

2006. Chinese-scorpion (Buthus martensi Karsch) toxinBmK aIV, a novel modulator of sodium channels: fromgenomic organization to functional analysis. Biochem. J.399: 445–453.

1040. Chai Z-F, Bai Z-T, Liu T, PangX-Y, Ji Y-H. 2006. The bindingof BmK IT2 on mammal and insect sodium channels bysurface plasmon resonance assay. Pharmacol. Res. 54:85–90.

1041. Hearty S, Leonard P, Quinn J, O’Kennedy R. 2006. Pro-duction, characterisation and potential application of anovel monoclonal antibody for rapid identification ofvirulent Listeria monocytogenes. J. Microbiol. Meth. 66:294–312.

1042. Kang CD, Lee SW, Park TH, Sim SJ. 2006. Performanceenhancement of real-time detection of protozoan parasite,Cryptosporidium oocyst by a modified surface plasmonresonance (SPR) biosensor. Enzyme Microbial Technol.39: 387–390.

1043. Oli MW, McArthur WP, Brady LJ. 2006. A whole cellBIAcore assay to evaluate P1-mediated adherence ofStreptococcus mutans to human salivary agglutinin andinhibition by specific antibodies. J. Microbiol. Meth. 65:503–511.

1044. Parker AL, Waddington SN, Nicol CG, Shayakhmetov DM,Buckley SM, Denby L, Kemball-Cook G, Ni S, Lieber A,McVey JH, Nicklin SA, Baker AH. 2006. Multiple vitaminK-dependent coagulation zymogens promote adenovirus-mediated gene delivery to hepatocytes. Blood 108:2554–2561.

1045. PettigrewDM,Williams DT, Kerrigan D, Evans DJ, Lea SM,Bhella D. 2006. Structural and functional insights into theinteraction of echoviruses and decay-accelerating factor.J. Biol. Chem. 281: 5169–5177.

1046. Shemesh M, Steinberg D. 2006. In vitro binding inter-actions of oral bacteria with immobilized fructosyltrans-ferase. J. Appl. Microbiol. 100: 871–877.

1047. Throsby M, Geuijen C, Goudsmit J, Bakker AQ, Korimbo-cus J, Kramer RA, Clijsters-van der Horst M, de Jong M,Jongeneelen M, Thijsse S, Smit R, Visser TJ, Bijl N, Mar-issen WE, Loeb M, Kelvin DJ, Preiser W, ter Meulen J,de Kruif J. 2006. Isolation and characterization of humanmonoclonal antibodies from individuals infected withWest Nile virus. J. Virol. 80: 6982–6992.

1048. Uchida H, Kinoshita H, Kawai Y, Kitazawa H, Miura K,Shiiba K, Horii A, Kimura K, Taketomo N, Oda M. 2006.Lactobacilli binding human A-antigen expressed in intes-tinal mucosa. Res. Microbiol. 157: 659–665.

1049. Visudtiphole V, Chalton DA, Hong Q, Lakey JH. 2006.Determining OMP topology by computation, surface plas-mon resonance and cysteine labelling: the test case ofOMPG. Biochem. Biophys. Res. Commun. 351: 113–117.

1050. Waswa JW, Debroy C, Irudayaraj J. 2006. Rapid detectionof Salmonella enteritidis and Escherichia coli using sur-face plasmon resonance biosensor. J. Food Process Eng.29: 373–385.

Other applications1051. Camors E, Charue D, Trouve P, Monceau V, Loyer X,

Russo-Marie F, Charlemagne D. 2006. Association ofannexin A5 with Naþ/Ca2þ exchanger and caveolin-3 in

non-failing and failing human heart. J. Mol. Cell. Cardiol.40: 47–55.

1052. Dey ES, Szewczyk E, Wawrzynczyk J, Norrlow O. 2006.A novel approach for characterization of exopolymericmaterial in sewage sludge. J. Residuals Sci. Technol. 3:97–103.

1053. Dong G-C, Chuang P-H, Forrest MD, Lin Y-C, Chen HM.2006. Immuno-suppressive effect of blocking the CD28signaling pathway in T-cells by an active component ofEchinacea found by a novel pharmaceutical screeningmethod. J. Med. Chem. 49: 1845–1854.

1054. Duce JA, Smith DP, Blake RE, Crouch PJ, Li Q-X, MastersCL, Trounce IA. 2006. Linker histone H1 binds to diseaseassociated amyloid-like fibrils. J. Mol. Biol. 361: 493–505.

1055. Fukui M, Hinode D, Yokoyama M, Tanabe S, Yoshioka M.2006. Salivary immunoglobulin A directed to oralmicrobial GroEL in patients with periodontitis and theirpotential protective role. Oral Microbiol. Immunol. 21:289–295.

1056. Germain M, Balaguer P, Nicolas J-C, Lopez F, Esteve J-P,Sukhorukov GB, Winterhalter M, Richard-Foy H, FournierD. 2006. Protection of mammalian cell used in biosensorsby coating with a polyelectrolyte shell. Biosens. Bioelec-tron. 21: 1566–1573.

1057. Gobbi M, Colombo L, Morbin M, Mazzoleni G, Accardo E,VanoniM, Del Favero E, Cantu L, Kirschner DA,Manzoni C,BeegM, Ceci P, Ubezio P, Forloni G, Tagliavini F, SalmonaM. 2006. Gerstmann-Straussler-Scheinker disease amy-loid protein polymerizes according to the ‘dock-and-lock’model. J. Biol. Chem. 281: 843–849.

1058. Hayashida O, Kitaura A. 2006. Synthesis of water-solubletris(cyclophane) hosts and surface plasmon resonancestudy on guest-binding interaction with immobilizedguests. Chem. Lett. 35: 808–809.

1059. Jin G-b, Unfricht DW, Fernandez SM, Lynes MA. 2006.Cytometry on a chip: cellular phenotypic and functionalanalysis using grating-coupled surface plasmon reson-ance. Biosens. Bioelectron. 22: 200–206.

1060. Larsericsdotter H, Jansson O, Zhukov A, Areskoug D,Oscarsson S, Buijs J. 2006. Optimizing the surfaceplasmon resonance/mass spectrometry interface forfunctional proteomics applications: how to avoid andutilize nonspecific adsorption. Proteomics 6: 2355–2364.

1061. Lojou E, Bianco P. 2006. Assemblies of dendrimers andproteins on carbon and gold electrodes. Bioelectrochem-istry 69: 237–247.

1062. Moon H, Na H-Y, Chong KH, Kim TJ. 2006. P2X7 receptor-dependent ATP-induced shedding of CD27 in mouselymphocytes. Immunol. Lett. 102: 98–105.

1063. NedelkovD, TubbsKA, NelsonRW. 2006. Surface plasmonresonance-enabled mass spectrometry arrays. Electro-phoresis 27: 3671–3675.

1064. Niu G, Huang L, Wang Q, Jiang L, Huang M, Shen P, Fei J.2006. A novel strategy to identify the regulatoryDNA-organized cooperations among transcription factors.FEBS Lett. 580: 415–424.

1065. Perdereau C, Godat E, Maurel M-C, Hazouard E, Diot E,LalmanachG. 2006. Cysteine cathepsins in human silicoticbronchoalveolar lavage fluids. Biochim. Biophys. Acta1762: 351–356.

1066. SteukersM, Schaus J-M, vanGool R, HoyouxA, Richalet P,Sexton DJ, Nixon AE, Vanhove M. 2006. Rapid kinetic-based screening of human Fab fragments. J. Immunol.Meth. 310: 126–135.

Affinity Sensors

1067. Aoki K, Saito H, Itzstein C, Ishiguro M, Shibata T, BlanqueR, Mian AH, Takahashi M, Suzuki Y, YoshimatsuM, Yama-guchi A, Deprez P, Mollat P, Murali R, Ohya K, Horne WC,Baron R. 2006. A TNF receptor loop peptide mimic blocksRANK ligand-induced signaling, bone resorption, andbone loss. J. Clin. Invest. 116: 1525–1534.


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1068. Atalay EO, Ustel E, Yildiz S, Atalay A. 2006. Surface plas-mon resonance-based molecular detection of Hb S[b6(A3)Glu ! Val, GAG ! GTG] at the gene level. Hemo-globin 30: 385–391.

1069. Azuma N, Maeta A, Fukuchi K, Kanno C. 2006. A rapidmethod for purifying osteopontin from bovine milk andinteraction between osteopontin and other milk proteins.Int. Dairy J. 16: 370–378.

1070. Brittingham R, Uitto J, Fertala A. 2006. High-affinity bind-ing of the NC1 domain of collagen VII to laminin 5 andcollagen IV. Biochem. Biophys. Res. Commun. 343:692–699.

1071. Cerutti ML, Ferreiro DU, Sanguineti S, Goldbaum FA,de Prat-Gay G. 2006. Antibody recognition of a flexibleepitope at the DNA binding site of the human papilloma-virus transcriptional tegulator E2. Biochemistry 45:15520–15528.

1072. Chen Q, Huang J, Yin H, Chen K, Osa T. 2006. The appli-cations of affinity biosensors: IAsys biosensor and quartzcrystal microbalance to the study on interaction betweenPaeoniae radix 801 and endothelin-1. Sens. Actuat. B 115:116–122.

1073. Desimone MF, De Marzi MC, Copello GJ, Fernandez MM,Pieckenstain FL,Malchiodi EL, Diaz LE. 2006. Production ofrecombinant proteins by sol-gel immobilized Escherichiacoli. Enzyme Microbial Technol. 40: 168–171.

1074. Fujimoto N, Terlizzi J, Aho S, Brittingham R, Fertala A,Oyama N, McGrath JA, Uitto J. 2006. Extracellular matrixprotein 1 inhibits the activity ofmatrixmetalloproteinase 9through high-affinity protein/protein interactions. Exp.Dermatol. 15: 300–307.

1075. Hess MT, Mendillo ML, Mazur DJ, Kolodner RD. 2006.Biochemical basis for dominant mutations in the Sacchar-omyces cerevisiae MSH6 gene. Proc. Natl Acad. Sci. USA103: 558–563.

1076. Ito A, HattoriM, Yoshida T,Watanabe A, Sato R, TakahashiK. 2006. Regulatory effect of amino acids on thepasting behavior of potato starch is attributable to itsbinding to the starch chain. J. Agric. Food Chem. 54:10191–10196.

1077. Jennings NS, Smethurst PA, Knight CG, O’Connor MN,Joutsi-Korhonen L, Stafford P, Stephens J, Garner SF,Harmer IJ, Farndale RW, Watkins NA, Ouwehand WH.2006. Production of calmodulin-tagged proteins in Droso-phila Schneider S2 cells: a novel system for antigen pro-duction and phage antibody isolation. J. Immunol. Meth.316: 75–83.

1078. Kato T, Higuchi M, Endo R, Maruyama T, Haginoya K,Shitomi Y, Hayakawa T, Mitsui T, Sato R, Hori H. 2006.Bacillus thuringiensis Cry1Ab, but not Cry1Aa or Cry1Ac,disrupts liposomes. Pesticide Biochem. Physiol. 84:1–9.

1079. Kiba A, Ohgawara T, Toyoda K, Inoue-Ozaki M, Takeda T,Rao US, Kato T, Ichinose Y, Shiraishi T. 2006. A bindingprotein for fungal signal molecules in the cell wall ofPisum Sativum. J. Gen. Plant Pathol. 72: 228–237.

1080. Kiba A, Toyoda K, Yoshioka K, Tsujimura K, Takahashi H,Ichinose Y, Takeda T, Kato T, Shiraishi T. 2006. A peaNTPase, PsAPY1, recognizes signalmolecules frommicro-organisms. J. Gen. Plant Pathol. 72: 238–246.

1081. Kim K-P, Jagadeesan B, Burkholder KM, Jaradat ZW,Wampler JL, Lathrop AA, Morgan MT, Bhunia AK. 2006.Adhesion characteristics of Listeria adhesion protein(LAP)-expressing Escherichia coli to Caco-2 cells and ofrecombinant LAP to eukaryotic receptor Hsp60 asexamined in a surface plasmon resonance sensor. FEMSMicrobiol. Lett. 256: 324–332.

1082. Kobayashi S, Uchiyama S, Sone T, Noda M, Lin L, MizunoH, Matsunaga S, Fukui K. 2006. Calreticulin as a newhistone binding protein in mitotic chromosomes. Cyto-genet. Genome Res. 115: 10–15.

1083. KovalevaM, Bussmeyer I, Rabe B, Grotzinger J, SudarmanE, Eichler J, Conrad U, Rose-John S, Scheller J. 2006.Abrogation of viral interleukin-6 (vIL-6)-induced signalingby intracellular retention and neutralization of vIL-6 with

an anti-vIL-6 single-chain antibody selected by phage dis-play. J. Virol. 80: 8510–8520.

1084. Leung MY-K, Ho WK-K. 2006. Production, characterizationand applications of mouse anti-grass carp (Ctenopharyn-godon idellus) growth hormone monoclonal antibodies.Compar. Biochem. Physiol. B 143: 107–115.

1085. Li M, Zhang W, Liu S, Liu Y, Zheng D. 2006. v-Fos trans-formation effector binds with CD2 cytoplasmic tail. Chin.Sci. Bull. 51: 38–47.

1086. Medvedev A, Buneeva O, Fedchenko V, Medvedeva M,Ivanov Y, Glover V, Sandler M. 2006. Isatin interactionwith glyceraldehyde-3-phosphate dehydrogenase, aputative target of neuroprotective drugs: partial agonismwith deprenyl. J. Nueural Transm. 71 (Suppl.): 97–103.

1087. Mochizuki T, Sakai K, Iwashita M. 2006. Effects of insulin-like growth factor (IGF) binding protein-3 (IGFBP-3) onendometrial cancer (HHUA) cell apoptosis and EGF stimu-lated cell proliferation in vitro. Growth Horm. IGF Res. 16:202–210.

1088. Mozzicafreddo M, Cuccioloni M, Eleuteri AM, Fioretti E,Angeletti M. 2006. Flavonoids inhibit the amidolyticactivity of human thrombin. Biochimie 88: 1297–1306.

1089. Mysliwy J, Dingley AJ, Sedlacek R, Grotzinger J. 2006.Structural characterization and binding properties of thehemopexin-like domain of the matrix metalloproteinase-19. Prot. Expr. Purif. 46: 406–413.

1090. Nunomura W, Takakuwa Y. 2006. Regulation of protein4.1R interactions with membrane proteins by Ca2þ andcalmodulin. Front. Biosci. 11: 1522–1539.

1091. Ogino M, Tanaka R, Hattori M, Yoshida T, Yokote Y,Takahashi K. 2006. Interfacial behavior of fatty-acylatedsericin prepared by lipase-catalyzed solid-phase syn-thesis. Biosci. Biotechnol. Biochem. 70: 66–75.

1092. Pazos M-J, Alfonso A, Vieytes MR, Yasumoto T, BotanaLM. 2006. Study of the interaction between different phos-phodiesterases and yessotoxin using a resonant mirrorbiosensor. Chem. Res. Toxicol. 19: 794–800.

1093. Shao H, Xu X, JingN, Tweardy DJ. 2006. Unique structuraldeterminants for Stat3 recruitment and activation by thegranulocyte colony-stimulating factor receptor at phos-photyrosine ligands 704 and 744. J. Immunol. 176:2933–2941.

1094. Shen R, Wheeler LJ, Mathews CK. 2006. Molecular inter-actions involving Escherichia coli nucleoside diphosphatekinase. J. Bioenerg. Biomembr. 38: 255–259.

1095. Shim K-S, Tombline G, Heinen CD, Charbonneau N,Schmutte C, Fishel R. 2006. Magnesium influences thediscrimination and release of ADP by human R AD51.DNA Repair 5: 704–717.

1096. Shim K-S, Schmutte C, Yoder K, Fishel R. 2006. Definingthe salt effect on human RAD51 activities. DNA Repair 5:718–730.

1097. Shitomi Y, Hayakawa T, Hossain DM, Higuchi M, Miya-moto K, Nakanishi K, Sato R, Hori H. 2006. A novel 96-kDaaminopeptidase localized on epithelial cell membranes ofBombyx mori midgut, which binds to Cry1Ac toxin ofBacillus thuringiensis. J. Biochem. 139: 223–233.

1098. SigurdssonHH, Loftsson T, Lehr C-M. 2006. Assessment ofmucoadhesion by a resonant mirror biosensor. Int. J.Pharmaceu. 325: 75–81.

1099. Stahl D, HoembergM, Cassens U, PachmannU, SibrowskiW. 2006. Influence of isotypes of disease-associatedautoantibodies on the expression of natural autoantibodyrepertoires in humans. Immunol. Lett. 102: 50–59.

1100. Tanner JAW,WrightM, Christie EM, PreussMK,Miller AD.2006. Investigation into the interactions between diade-nosine 50,5000;-P1,P4-tetraphosphate and two proteins: mol-ecular chaperone GroEL and cAMP receptor protein.Biochemistry 45: 3095–3106.

1101. Wang J, ZhouH, Zheng J, Cheng J, LiuW, DingG,Wang L,Luo P, Lu Y, Cao H, Yu S, Li B, Zhang L. 2006. Theantimalarial artemisinin synergizes with antibiotics to pro-tect against lethal live Escherichia coli challenge by


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decreasingproinflammatory cytokine release.Antimicrob.Agents Chemother. 50: 2420–2427.

1102. Wang Z-p, Cai S-x, Liu D-b, Xu X, Liang H-p. 2006. Anti-inflammatory effects of a novel peptide designed to bindwith NF-kB p50 subunit. Acta Pharmacol. Sin. 27:1474–1478.

1103. Watanabe A, Miyazawa S, Kitami M, Tabunoki H, Ueda K,Sato R. 2006. Characterization of a novel C-type lectin,Bombyx mori multibinding protein, from the B. morihemolymph: mechanism of wide-range microorganismrecognition and role in immunity. J. Immunol. 177:4594–4604.

1104. Wu B-y, Wang Y-y, Li J, Song Z, Huang J-d, Wang X-s,Chen Q. 2006. An optical biosensor for kinetic analysis ofsoluble interleukin-1 receptor I binding to immobilizedinterleukin-1 a. Talanta 70: 485–488.

1105. Wu B-Y, Li J, Huang J-D, Wang Y-Y, Yin H-J, Chen K-J,Chen Q. 2006. Real time kinetic analysis of the interactionbetween interleukin-1a and soluble interleukin-1 receptor Iusing a resonant mirror biosensor. Anal. Chim. Acta 557:106–110.

1106. Zhao J, Huang J, Chen H, Cui L, He W. 2006. Vd1 T cellreceptor binds specifically to MHC I chain related A: mol-ecular and biochemical evidences.Biochem.Biophys. Res.Commun. 339: 232–240.

1107. Zhou Y-L, Liao J-M, Chen J, Liang Y. 2006. Macromolecu-lar crowding enhances the binding of superoxide dismu-tase to xanthine oxidase: implications for protein-proteininteractions in intracellular environments. Int. J. Biochem.Cell Biol. 38: 1986–1994.

Texas Instruments

1108. Balasubramanian S, Revzin A, Sirnonian A. 2006. Electro-chemical desorption of proteins from gold electrode sur-face. Electroanalysis 18: 1885–1892.

1109. Cho SH, Jeong JH, Yang SR, Kim BY, Kim J-D. 2006.Binding evaluation of targeted microbubbles with bioti-n-avidin interaction by surface plasmon resonance bio-sensor. Japan J. Appl. Phys. Part 1 45: 421–425.

1110. Chung JW, Bernhardt R, Pyun JC. 2006. Sequentialanalysis of multiple analytes using a surface plasmonresonance (SPR) biosensor. J. Immunol. Meth. 311:178–188.

1111. Chung JW, Bernhardt R, Pyun JC. 2006. Additive assay ofcancer marker CA 19-9by SPR biosensor. Sens. Actuat.B-Chem. 118: 28–32.

1112. Chung JW, Kim SD, Bernhardt R, Pyun JC. 2006. Additiveassay for the repeated measurements of immunosensorwithout regeneration step. Sens. Actuat. B 114: 1007–1012.

1113. Chung JW, Park JM, Bernhardt R, Pyun JC. 2006. Immu-nosensor with a controlled orientation of antibodies byusing NeutrAvidin-protein A complex at immunoaffinitylayer. J. Biotechnol. 126: 325–333.

1114. Craig I, McLaughlin JA. 2006. SPR and AFM study ofengineered biomolecule immobilisation techniques.Eng. Med. Biol. Soc. 28(Suppl.): 6720–6731.

1115. Gil B, Chang Y-K, Cho Y-J. 2006. An application of surfaceplasmon resonance to evaluation of quality parameters ofsoybean oil during frying. Food Sci. Biotechnol. 15:404–408.

1116. Owega S, Poitras D, Faid K. 2006. Solid-state opticalcoupling for surface plasmon resonance sensors. Sens.Actuat. B 114: 212–217.

1117. Paloniemi H, Lukkarinen M, Aaritalo T, Areva S, Leiro J,Heinonen M, Haapakka K, Lukkari J. 2006. Layer-by-layerelectrostatic self-assembly of single-wall carbon nanotubepolyelectrolytes. Langmuir 22: 74–83.

1118. Roper DK, Nakra S. 2006. Adenovirus type 5 intrinsicadsorption rates measured by surface plasmon reson-ance. Anal. Biochem. 348: 75–83.

1119. Viskari PJ, Landers JP. 2006. Unconventional detectionmethods for microfluidic devices. Electrophoresis 27:1797–1810.

1120. Wang C, Li Y, Xiong J, Tan Y, Yu J. 2006. Using of thesurface plasmon resonance cytosensor for real-time andnon-invasive monitoring of cellular effects in living C6cells induced by PMA. Chin. Sci. Bull. 51: 927–933.

1121. Wang Y, Du X. 2006. Miscibility of binary monolayers atthe air-water interface and interaction of protein withimmobilized monolayers by surface plasmon resonancetechnique. Langmuir 22: 6195–6202.

1122. Yang T, Zhong P, Qu L, Wang C, Yuan Y. 2006. Preparationand identification of anti-2, 4-dinitrophenyl monoclonalantibodies. J. Immunol. Meth. 313: 20–28.

1123. Zucolotto V, Pinto APA, Tumolo T, Moraes ML, BaptistaMS, Riul A Jr, Araujo APU, Oliveira ON Jr. 2006. Catecholbiosensing using a nanostructured layer-by-layer filmcontaining Cl-catechol 1,2-dioxygenase. Biosens. Bioelec-tron. 21: 1320–1326.

EcoChemie

1124. Arya SK, Solanki PR, Singh RP, Pandey MK, Datta M,Malhotra BD. 2006. Application of octadecanethiol self-assembled monolayer to cholesterol biosensor based onsurface plasmon resonance technique. Talanta 69:918–926.

1125. Cho HS, Kim T-J, Lee J-I, Park N-Y. 2006. Serodiagnosticcomparison of enzyme-linked immunosorbent assay andsurface plasmon resonance for the detection of antibodyto porcine circovirus type 2. Can. J. Vet. Res. 70: 263–268.

1126. Damos FS, de Cassia Silva Luz R, Tanaka AA, Kubota LT.2006. Investigations of nanometric films of doped polyani-line by using electrochemical surface plasmon resonanceand electrochemical quartz crystal microbalance.J. Electroanal. Chem. 589: 70–81.

1127. Damos FS, Luz RCS, Kubota LT. 2006. Investigations ofultrathin polypyrrole films: formation and effects of dop-ing/dedoping processes on its optical properties by elec-trochemical surface plasmon resonance (ESPR).Electrochim. Acta 51: 1304–1312.

1128. Dondapati SK, Montornes JM, Sanchez PL, Sanchez JLA,O’Sullivan C, Katakis I. 2006. Site-directed immobilizationof proteins through electrochemical deprotection on elec-troactive self-assembled monolayers. Electroanalysis 18:1879–1884.

1129. Gu H, Ng Z, Deivaraj TC, Su X, Loh KP. 2006. Surfaceplasmon resonance spectroscopy and electrochemistrystudy of 4-nitro-1,2-phenylenediamine: a switchable redoxpolymer with nitro functional groups. Langmuir 22:3929–3935.

1130. Kim TJ, Cho HS, Park NY, Lee JI. 2006. Serodiagnosticcomparison between two methods, ELISA and surfaceplasmon resonance for the detection of antibody titresofMycoplasma hyopneumoniae. J. Vet. Med. B 53: 87–90.

1131. Lupu S. 2006. In-situ combined electrochemical surfaceplasmon resonace study of ultra-thin prussian blue depos-ited on gold electrodes. Rev. Roum. Chim. 51: 527–532.

1132. Su X, Lin C-Y, O’Shea SJ, Teh HF, Peh WYX, Thomsen JS.2006. Combinational application of surface plasmonresonance spectroscopy and quartz crystal microbalancefor studying nuclear hormone receptor-response elementinteractions. Anal. Chem. 78: 5552–5558.

1133. Szunerits S, Boukherroub R. 2006. Preparation and charac-terization of thin films of SiO(x) on gold substrates forsurface plasmon resonance studies. Langmuir 22:1660–1663.

1134. Szunerits S, Boukherroub R. 2006. Electrochemical inves-tigation of gold/silica thin film interfaces for electroche-mical surface plasmon resonance studies. Electrochem.Commun. 8: 439–444.

1135. Szunerits S, Coffinier Y, Janel S, Boukherroub R. 2006.Stability of the gold/silica thin film interface: electroche-mical and surface plasmon resonance studies. Langmuir22: 10716–10722.

1136. Tang D-P, Yuan R, Chai YQ. 2006. Novel immunoassay forcarcinoembryonic antigen based on protein A-conjugated


DOI: 10.1002/jmr


immunosensor chip by surface plasmon resonance andcyclic voltammetry. Bioprocess. Biosyst. Eng. 28: 315–321.

1137. Tang DP, Yuan R, Chai YQ. 2006. Electrochemical immu-nosensing strategies based on immobilization of anti-IgCon mixed self-assembly monolayers carrying surfaceamide or carboxyl groups. Anal. Lett. 39: 1809–1821.

1138. Vega VB, Lin C-Y, Lai KS, Kong SL, Xie M, Su X, Teh HF,Thomsen JS, Yeo AL, Sung WK, Bourque G, Liu ET. 2006.Multiplatform genome-wide identification and modelingof functional human estrogen receptor binding sites.Gen-ome Biol. 7: R82.

Nippon Laser & Electronics

1139. Endo S, Kurihara K. 2006. Ethanol macrocluster formationon gold substrate modified with mercapto alcohol. JapanJ. Appl. Phys. 45: 502–504.

1140. Fujimura Y, Umeda D, Kiyohara Y, Sunada Y, Yamada K,Tachibana H. 2006. The involvement of the 67 kDa lamininreceptor-mediated modulation of cytoskeleton in thedegranulation inhibition induced by epigallocatechi-n-3-O-gallate. Biochem. Biophys. Res. Commun. 348:524–531.

1141. Ito M, Imae T. 2006. Self-assembled monolayer of carbox-yl-terminated poly(amido amine) dendrimer. J. Nanosci.Nanotechnol. 6: 1667–1672.

1142. Kim SJ, Gobi KV, Tanaka H, Shoyama Y, Miura N. 2006.Enhanced sensitivity of a surface-plasmon-resonance(SPR) sensors for 2,4-D by controlled functionalizationof self-assembled monolayer-based immunosensor chip.Chem. Lett. 35: 1132–1133.

1143. Kim SJ, Gobi KV, Harada R, Shankaran DR, Miura N. 2006.Miniaturized portable surface plasmon resonance immu-nosensor applicable for on-site detection of low-molecular-weight analytes. Sens. Actuat. B 115: 349–356.

1144. Kumbhat S, Ravi Shankaran D, Kim SJ, Gobi KV, Joshi V,Miura N. 2006. A novel receptor-based surface-plasmon-resonance affinity biosensor for highly sensitiveand selective detection of dopamine. Chem. Lett 35:678–679.

1145. Nakamura F, Ito E, Hayashi T, Hara M. 2006. Fabrication ofCOOH-terminated self-assembled monolayers for DNAsensors. Colloid Surf. A 284: 495–498.

1146. Ravi Shankaran D, Kawaguchi T, Kim SJ, Matsumoto K,Toko K, Miura N. 2006. Evaluation of the molecular recog-nition of monoclonal and polyclonal antibodies for sensi-tive detection of 2,4,6-trinitrotoluene (TNT) by indirectcompetitive surface plasmon resonance immunoassay.Anal. Bioanal. Chem. 386: 1313–1320.

1147. Ravi Shankaran D, Matsumoto K, Toko K, Miura N. 2006.Performance evaluation and comparison of four SPRimmunoassays for rapid and label-free detection ofTNT. Electrochemistry 74: 141–144.

1148. Ravi Shankaran D, Matsumoto K, Toko K, Miura N. 2006.Development and comparison of two immunoassays forthe detection of 2,4,6-trinitrotoluene (TNT) based on sur-face plasmon resonance. Sens. Actuat. B 114: 71–79.

1149. Suda Y, Arano A, Fukui Y, Koshida S,WakaoM, NishimuraT, Kusumoto S, Sobel M. 2006. Immobilization and clus-tering of structurally defined oligosaccharides for sugarchips: an improved method for surface plasmon reson-ance analysis of protein-carbohydrate interactions. Bio-conjug. Chem. 17: 1125–1135.

1150. Ujihara M, Imae T. 2006. Adsorption behaviors of poly(-amido amine) dendrimerswith an azacrown core and longalkyl chain spacers on solid substrates. J. Colloid InterfaceSci. 293: 333–341.

Analytical m-systems

1151. KalininaMA, Golubev NV, RaitmanOA, Selector SL, Arsla-nov VV. 2006. A novel ultra-sensing composed Langmuir-Blodgettmembrane for selective calciumdetermination inaqueous solutions. Sens. Actuat. B 114: 19–27.

1152. Kalinina MA, Raitman OA, Selector SL, Turygin DS, Arsla-nov VV. 2006. Conformational tuning of sensing Lang-muir-Blodgett membranes for selective determination ofmetal ions, anions,andmolecular fragments. IEEE Sens. J.6: 450–457.

1153. Phillips KS, Han J-H, MartinezM, Wang Z, Carter D, ChengQ. 2006. Nanoscale glassification of gold substrates forsurface plasmon resonance analysis of protein toxinswith supported lipid membranes. Anal. Chem. 78:596–603.

1154. Phillips KS, Wilkop T, Wu J-J, Al-Kaysi RO, Cheng Q. 2006.Surface plasmon resonance imaging analysis of protein-receptor binding in supported membrane arrays on goldsubstrateswith calcinated silicate films. J. Am. Chem. Soc.128: 9590–9591.

1155. Riskin M, Basnar B, Chegel VI, Katz E, Willner I, Shi F,Zhang X. 2006. Switchable surface properties throughthe electrochemical or biocatalytic generation of Ag0

nanoclusters on monolayer-functionalized electrodes.J. Am. Chem. Soc. 128: 1253–1260.

1156. Riskin M, Basnar B, Katz E, Willner I. 2006. Cycliccontrol of the surface properties of a monolayer-functionalized electrode by the electrochemicalgeneration of Hg nanoclusters. Chem. Eur. J. 12:8549–8557.

1157. Snopok B, Yurchenko M, Szekely L, Klein G, Kashuba E.2006. SPR-based immunocapture approach to creating aninterfacial sensing architecture: mapping of the MRS 18-2binding site on retinoblastoma protein. Anal. Bioanal.Chem. 386: 2063–2073.

1158. Snopok BA, Boltovets PN, Rowell FJ. 2006. Effectof the local environment and state of the immobilizedligand on its reaction with a macromolecular receptor.Theor. Exptl Chem. 42: 217–223.

1159. Snopok BA, Boltovets PN, Rowell FJ. 2006. Simple ana-lytical model of biosensors competition analysis for detec-tion of low-molecular-weight analytes. Theor. Exp. Chem.42: 106–112.

Optrel

1160. Baba A, Knoll W, Advincula R. 2006. Simultaneous in situelectrochemical, surface plasmon optical, and atomicforce microscopy measurements: Investigation of conju-gated polymer electropolymerization. Rev. Sci. Instrum.77: 064101.

1161. Bae YM, Park K-W,OhB-K, Choi J-W. 2006. Immunosensorfor detection of Escherichia coli O157:H7 using imagingellipsometry. J. Microbiol. Biotechnol. 16: 1169–1173.

1162. Choi J-W, Lee W, Oh B-K, Lee H-J, Lee D-B. 2006. Appli-cation of complement 1q for the site-selective recognitionof immune complex in protein chip. Biosens. Bioelectron.22: 764–767.

1163. Choi J-W, Nam Y-S, Lee BH, Ahn DJ, Nagamune T. 2006.Charge trap in self-assembled monolayer of cytochromeb562-green fluorescent protein chimera. Curr. Appl. Phys.6: 760–765.

1164. Choi J-W, Nam YS, Jeong S-C, Lee WH, Petty MC. 2006.Molecular rectifier consisting of cytochrome c/GFP hetero-layer by using metal coated optical fiber tip. Curr. Appl.Phys. 6: 839–843.

1165. Jyoung J-Y, Hong S, Lee W, Choi J-W. 2006. Immunosen-sor for the detection of Vibrio cholerae O1 using surfaceplasmon resonance. Biosen. Bioelectron. 21: 2315–2319.

1166. NamYS, Choi J-W. 2006. Fabrication and electrical charac-teristics of ferredoxin self-assembled layer for biomole-cular electronic device application. J. Microbiol.Biotechnol. 16: 15–19.

1167. Sakellariou G, Park M, Advincula R, Mays JW, Hadjichris-tidis N. 2006. Homopolymer and block copolymerbrushed on gold by living anionic surface-initiatedpolymerization in a polar solvent. J. Polym. Sci. 44:769–782.


DOI: 10.1002/jmr


GWC Technologies

1168. Baac H, Hajos JP, Lee J, Kim D, Kim SJ, Shuler ML. 2006.Antibody-based surface plasmon resonance detection ofintact viral pathogen. Biotechnol. Bioeng. 94: 815–819.

1169. D’Agata R, Grasso G, Iacono G, Spoto G, Vecchio G. 2006.Lectin recognition of a new SOD mimic bioconjugatestudied with surface plasmon resonance imaging. Org.Biomol. Chem. 4: 610–612.

1170. Grasso G, Fragai M, Rizzarelli E, Spoto G, Yeo KJ. 2006.In situ AP/MALDI-MS characterization of anchored matrixmetalloproteinases. J. Mass Spectrom. 41: 1561–1569.

1171. Lee HJ, Nedelkov D, Corn RM. 2006. Surface plasmonresonance imaging measurements of antibody arraysfor the multiplexed detection of low molecular weightprotein biomarkers. Anal. Chem. 78: 6504–6510.

1172. Lee HJ, Wark AW, Corn RM. 2006. Creating advancedmultifunctional biosensors with surface enzymatic trans-formations. Langmuir 22: 5241–5250.

1173. Li Y, Lee HJ, Corn RM. 2006. Fabrication and characteriz-ation of RNA aptamer microarrays for the study of protei-n-aptamer interactions with SPR imaging. Nuc. Acids Res.34: 6416–6424.

1174. Peelen D, Kodoyianni V, Lee J, Zheng T, Shortreed MR,Smith LM. 2006. Specific capture of mammalian cells bycell surface receptor binding to ligand immobilized ongold thin films. J. Proteome Res. 5: 1580–1585.

Sensia

1175. Barcelo D, Brix R, FarreM. 2006.Monitoring andmanagingriver pollutants. TrAC 25: 743–747.

1176. Mauriz E, Calle A, Abad A, Montoya A, Hildebrandt A,Barcelo D, Lechuga LM. 2006. Determination of carbaryl innatural water samples by a surface plasmon resonanceflow-through immunosensor. Biosens. Bioelectron. 21:2129–2136.

1177. Mauriz E, Calle A, Lechuga LM, Quintana J, Montoya A,Manclus JJ. 2006. Real-time detection of chlorpyrifos atpart per trillion levels in ground, surface and drinkingwater samples by a portable surface plasmon resonanceimmunosensor. Anal. Chim. Acta 561: 40–47.

1178. Mauriz E, Calle A, Manclus JJ, Montoya A, Escuela AM,Sendra JR, Lechuga LM. 2006. Single and multi-analytesurface plasmon resonance assays for simultaneousdetection of cholinesterase inhibiting pesticides. Sens.Actuat. B 118: 399–407.

1179. Mauriz E, Calle A, Montoya A, Lechuga LM. 2006.Determination of environmental organic pollutants witha portable optical immunosensor. Talanta 69: 359–364.

Artificial Sensing

1180. Adanyi N, Nemeth J, Halasz A, Szendro I, Varadi M. 2006.Application of electrochemical optical waveguide light-mode spectroscopy for studying the effect of differentstress factors on lactic acid bacteria. Anal. Chim. Acta573–574: 41–47.

1181. Adanyi N, Varadi M, Kim N, Szendro I. 2006. Developmentof new immunosensors for determination of contami-nants in food. Curr. Appl. Phys. 6: 279–286.

1182. Grandin HM, Stadler B, Textor M, Voros J. 2006. Wave-guide excitation fluorescence microscopy: a new tool forsensing and imaging the biointerface. Biosens. Bioelec-tron. 21: 1476–1482.

1183. Zhen GL, Falconnet D, Kuennemann E, Voros J, SpencerND, Textor M, Zurcher S. 2006. Nitrilotriacetic acid func-tionalized graft copolymers: a polymeric interface forselective and reversible binding of histidine-taggedproteins. Adv. Funct. Mat. 16: 243–251.

Genoptics

1184. Cherif B, Roget A, Villiers CL, Calemczuk R, Leroy V,Marche PN, Livache T, Villiers MB. 2006. Clinically relatedprotein-peptide interactions monitored in real time onnovel peptide chips by surface plasmon resonance ima-ging. Clin. Chem. 52: 255–262.

1185. Cherif B, Villiers CL, Paranhos-Baccala G, Calemczuk R,Marche PN, Livache T, Villiers M-B. 2006. Design andapplication of a microarray for fluorescence and surfaceplasmon resonance imaging analysis of peptide-antibodyinteractions. J. Biomed. Nanotechnol. 2: 29–35.

1186. Kerdiles YM, Cherif B, Marie JC, Tremillon N, Blanquier B,Libeau G, Diallo A, Wild TF, Villiers MB, Horvat B. 2006.Immunomodulatory properties of morbillivirus nucleo-proteins. Viral Immunol. 19: 324–334.

1187. Mannelli I, Courtois V, Lecaruyer P, Roger G, Millot MC,Goossens M, Canva M. 2006. Surface plasmon resonanceimaging (SPRI) system and real-time monitoring of DNAbiochip for human genetic mutation diagnosis of DNAamplified samples. Sens. Actuat. B 119: 583–591.

Reichert Analytical Instruments

1188. Kim YR, Paik H-j, Ober CK, Coates GW, Mark SS, Ryan TE,Batt CA. 2006. Real-time analysis of enzymatic surfa-ce-initiated polymerization using surface plasmon reson-ance (SPR). Macromol. Biosci. 6: 145–152.

1189. Subramanian A, Irudayaraj J, Ryan T. 2006. A mixed self-assembled monolayer-based surface plasmon immuno-sensor for detection of E. coli O157:H7. Biosens. Bioelec-tron. 21: 998–1006.

1190. Subramanian A, Irudayaraj J, Ryan T. 2006. Mono anddithiol surfaces on surface plasmon resonance biosensorsfor detection of Staphylococcus aureus. Sens. Actuat. B114: 192–198.

1191. Subramanian AS, Irudayaraj JM. 2006. Surface plasmonresonance based immunosensing of E. coli O157:H7 inapple juice. Trans. ASABE 49: 1257–1262.

Resonant Probes

1192. Crespo-Biel O, Lim CW, Ravoo BJ, Reinhoudt DN, HuskensJ. 2006. Expression of a supramolecular complex at amultivalent interface. J. Am. Chem. Soc. 128: 17024–17032.

1193. Gandubert VJ, Lennox RB. 2006. Surface plasmon reson-ance spectroscopy study of electrostatically adsorbedlayers. Langmuir 22: 4589–4593.

1194. Kujawa P, Sanchez J, Badia A, Winnik FM. 2006. Probingthe stability of biocompatible sodium hyaluronate/chito-san nanocoatings against changes in salinity and pH.J. Nanosci. Nanotechnol. 6: 1565–1574.

1195. Ludden MJ, Peter M, Reinhoudt DN, Huskens J. 2006.Attachment of streptavidin to b-cyclodextrin molecularprintboards via orthogonal host-guest and protein-ligandinteractions. Small 2: 1192–1202.

Toyobo

1196. Adachi H, Takahashi Y, Kyo M, Sato T, Nishimura Y. 2006.Synthesis and evaluation of aminoglycosides as inhibitorsfor rev binding to rev responsive element. Lett. Drug Des.Discov. 3: 71–75.

1197. Itoi Y, Horinaka M, Tsujimoto Y, Matsui H, Watanabe K.2006. Characteristic features in the structure and collagen-binding ability of a thermophilic collagenolytic proteasefrom the thermophileGeobacillus collagenovoransMO-1.J. Bacteriol. 188: 6572–6579.

1198. Kanoh N, Kyo M, Inamori K, Ando A, Asami A, Nakao A,Osada H. 2006. SPR imaging of photo-cross-linkedsmall-molecule arrays on gold. Anal. Chem. 78:2226–2230.


DOI: 10.1002/jmr


1199. Yamamoto T, Kyo M, Kamiya T, Tanaka T, Engel JD,Motohashi H, Yamamoto M. 2006. Predictive base substi-tution rules that determine the binding and transcriptionalspecificity of Maf recognition elements. Genes Cells 11:575–591.

Corning

1200. Fang Y, Ferrie AM, Fontaine NH, Mauro J, Balakrishnan J.2006. Resonantwaveguide grating biosensor for living cellsensing. Biophys. J. 91: 1925–1940.

1201. Fang Y, Ferrie AM, Li G. 2006. Cellular functions of cho-lesterol probedwith optical biosensors.Biochim. Biophys.Acta 1763: 254–261.

1202. WuM, Coblitz B, Shikano S, Long S, Spieker M, Frutos AG,Mukhopadhyay S, Li M. 2006. Phospho-specific recog-nition by 14-3-3 proteins and antibodies monitored by ahigh throughput label-free optical biosensor. FEBS Lett.580: 5681–5689.

Farfield

1203. Lin S, Lee C-K, Lin Y-H, Lee S-Y, Sheu B-C, Tsai J-C, HsuS-M. 2006. Homopolyvalent antibody-antigen interactionkinetic studies with use of a dual-polarization interfero-metric biosensor. Biosens. Bioelectron. 22: 715–721.

1204. Ricard-Blum S, Peel LL, Ruggiero F, Freeman NJ. 2006.Dual polarization interferometry characterization of carbo-hydrate-protein interactions.Anal. Biochem. 352: 252–259.

1205. Terry CJ, Popplewell JF, Swann MJ, Freeman NJ, FernigDG. 2006. Characterisation of membrane mimetics on adual polarisation interferometer. Biosens. Bioelectron. 22:627–632.

SRU Biosystems

1206. Chan LL, Cunningham BT, Li PY, Puff D. 2006. A self-referencing method for microplate label-free photonic-crystal biosensors. IEEE Sens. J. 6: 1551–1556.

1207. Cunningham BT, Laing L. 2006. Microplate-based, label-free detection of biomolecular interactions: applications inproteomics. Expert Rev. Proteomics 3: 271–281.

1208. Lin B, Li P, Cunningham BT. 2006. A label-free biosensor-based cell attachment assay for characterization of cellsurface molecules. Sens. Actuat. B 114: 559–564.

Bio-Rad

1209. Bravman T, Bronner V, Lavie K, Notcovich A, Papalia GA,Myszka DG. 2006. Exploring ‘one-shot’ kinetics and smallmolecule analysis using the ProteOn XPR36 array biosen-sor. Anal. Biochem. 358: 281–288.

1210. Slutzki M, Jaitin DA, Ben Yehezkel T, Schreiber G. 2006.Variations in the unstructured C-terminal tail of interferons

contribute to differential receptor binding and biologicalactivity. J. Mol. Biol. 360: 1019–1030.

DKK-TOA

1211. Li Y, Kobayashi M, Furui K, Soh N, Nakano K, Imato T.2006. Surface plasmon resonance immunosensor for his-tamine based on an indirect competitive immunoreaction.Anal. Chim. Acta 576: 77–83.

1212. Masadome T, Yano Y. 2006. Response of surface-plasmonresonance sensor based on gold surfaces modified byself-assembled monolayer to nonionic surfactants. Anal.Lett. 39: 2169–2177.

Lumera

1213. Boozer C, Chen S, Jiang S. 2006. Controlling DNA orien-tation on mixed ssDNA/OEG SAMs. Langmuir 22:4694–4698.

1214. Boozer C, Ladd J, Chen S, Jiang S. 2006. DNA-directedprotein immobilization for simultaneous detection ofmultiple analytes by surface plasmon resonance biosen-sor. Anal. Chem. 78: 1515–1519.

Nanofilm

1215. Klenkar G, Valiokas R, Lundstrom I, Tinazli A, Tampe R,Piehler J, Liedberg B. 2006. Piezo dispensedmicroarray ofmultivalent chelating thiols for dissecting complex pro-tein-protein interactions. Anal. Chem. 78: 3643–3650.

1216. Valiokas R, Klenkar G, Tinazli A, Tampe R, Liedberg B,Piehler J. 2006. Differential protein assembly on micro-patterned surfaces with tailored molecular and surfacemultivalency. Chem. Bio. Bhem. 7: 1325–1329.

Axela Biosensors

1217. Borisenko V, Hu W, Tam P, Chen I, Houle J-F, Ausserer W.2006. Diffractive optics technology: a novel detection tech-nology for immunoassays. Clin. Chem. 52: 2168–2170.

HSS Systeme

1218. Meyer MHF, Hartmann M, Keusgen M. 2006. SPR-basedimmunosensor for the CRP detection—a new method todetect a well known protein. Biosens. Bioelectron. 21:1987–1990.

K-mac

1219. Lee JY, Ko HJ, Lee SH, Park TH. 2006. Cell-basedmeasure-ment of odorant molecules using surface plasmon reson-ance. Enzyme Microbial Technol. 39: 375–380.


DOI: 10.1002/jmr


Survey of the year 2006 commercial optical biosensor literature

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