Rebecca L Rich and David G Myszka- Advances in surface plasmon resonance biosensor analysis

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The number and diversity of surface plasmon resonance (SPR)biosensor applications continue to increase. Evolutions ininstrument and sensor chip technology, experimentalmethodology, and data analysis are making it possible to examinea wider variety of biomolecular interactions in greater mechanisticdetail. SPR biosensors are poised to make a significant impact inbasic research and pharmaceutical discovery.

AddressesHuntsman Cancer Institute, University of Utah, 50 North Medical Drive,#4A417, Salt Lake City, UT 84132, USA*e-mail: [email protected]†e-mail: [email protected]

Current Opinion in Biotechnology 2000, 11:54–61

0958-1669/00/$ — see front matter © 2000 Elsevier Science Ltd. All rights reserved.

AbbreviationsMS mass spectroscopyRU response unitSPR surface plasmon resonance

IntroductionSurface plasmon resonance (SPR) biosensors have becomean established method of measuring molecular interac-tions. Biosensor experiments involve immobilizing onereactant on a surface and monitoring its interaction with asecond component in solution. SPR biosensors measurethe change in refractive index of the solvent near the sur-face that occurs during complex formation or dissociation[1]. These instruments are capable of characterizing bind-ing reactions in real-time without labeling requirements.Consequently, SPR biosensors can be used to study theinteractions of any biological system from proteins,oligonucleotides, oligosaccharides, and lipids to small mol-ecules, phage, viral particles, and cells.

Table 1 provides a list of the most common biosensorapplications. Qualitative applications range from followinga molecule through purification [2] to identifying smallmolecule leads in a screening mode [3••]. Quantitativeapplications include determining the active concentrationof molecules [4] and measuring reaction kinetics [5••] andaffinity constants [6]. Thermodynamic information can be

obtained by measuring reaction rates and equilibrium con-stants at different temperatures [7]. When experiments areperformed carefully, biosensors can be used to determinethe stoichiometry and mechanism of the interaction [5••].This report reviews the current state of biosensor technol-ogy and highlights recent advances in data acquisition andanalysis. Particular attention is paid to the importance ofmeasuring low binding responses.

Biosensor technologiesThe best indicator of the success of optical biosensor tech-nology is the growing number of commercially availableinstruments. Six companies (listed in Table 2) currentlymanufacture a variety of biosensor hardware. The reader isencouraged to visit each manufacturer’s website to obtaindetailed information about the different systems.

Biacore AB (Uppsala, Sweden) released the first commer-cial instrument in 1990 [8]. Approximately 90% of the 1998[9••] and 1999 commercial biosensor publications cite theuse of BIACORE instruments. The recently released BIA-CORE 3000 incorporates increased sensitivity and asmaller flow cell compared to BIACORE 2000, as well asonline data subtraction and micro-sample recovery.

Affinity Sensors (Franklin, MA) manufactures the IAsysline of instruments, which utilize evanescent wave tech-nology [10]. Samples are delivered into a cuvette-basedsystem, where mixing is accomplished through a movingpaddle. Approximately 10% of the past two years’ publica-tions report using IAsys instruments.

A few recent publications cite SPR biosensors by othermanufacturers [11•,12•]. Windsor Scientific Limited(Berks, UK) markets the IBIS system, which can be con-figured as a flow- or cuvette-based instrument. NipponLaser and Electronics Lab’s (Hokkaido, Japan) SPR-CELLIA systems can be configured for either wholecells or macromolecules, contain two parallel flow paths,and can operate from 5–95°C. Texas Instruments’ (Dal-las, TX) integrated SPR detector Spreeta can be

Advances in surface plasmon resonance biosensor analysisRebecca L Rich* and David G Myszka†

Table 1

SPR biosensor applications.

Qualitative Quantitative

Follow purification Active concentrationSpecificity Kinetics (ka, kd)Epitope mapping Equilibrium constants (KD)Molecular assembly Thermodynamics (ΔHvant Hoff)Ligand fishing StoichiometrySmall molecule screening Mechanism

Table 2

Commercial biosensors.

Company Web site

Biacore AB http://www.biacore.comAffinity Sensors http://www.affinity-sensors.comWindsor Scientific Limited http://www.windsor-ltd.co.ukBioTul AG http://www.biotul.comNippon Laser and Electronics Lab http://www.rikei.comTexas Instruments http://www.ti.com/spr

Advances in surface plasmon resonance biosensor analysis Rich and Myszka 55

configured for industrial, environmental, and biologicalapplications [13•]. The detector comes with an operatingprogram that allows maximum sensor versatility andadvanced data analysis algorithms. BioTul AG (Munich,Germany) launched its Plasmoon SPR instrument inAugust 1999; it is an automated eight-channel cuvette-based system.

One of the most important parts of the biosensor is the sur-face where the molecules interact. The commercialavailability of robust and reproducible surfaces makesbiosensors convenient to use. Table 3 provides a list of thesurfaces currently available from BIACORE and AffinitySensors along with their general applications. New andadvanced surface chemistries that are tailored to specificapplications continue to increase the utility of biosensors,as will be discussed below.

Other advances in biosensor applications include the cou-pling of SPR instruments with mass spectrometers. Thistandem analysis results in a powerful technology that couldprovide for the isolation and identification of unknownprotein binding partners from crude sources as well as‘hits’ from mixed compound libraries. Typical BIA/MSexperiments require removal of the sensor chip from theBIACORE instrument and loading the chip into MALDImass spectrometers [14,15]. Sönksen et al. [16••] evolvedthe technology by, firstly, developing a unique series ofwashing steps to minimize nonspecific binding to biosen-sor flow system elements and, secondly, eluting capturedmolecules in a small volume (3 μL). Sandwiching therecovery solution between air bubbles keeps the elutedmaterial concentrated, allowing it to be directly loadedonto templates and analyzed by MALDI mass spectrome-try. This micro-elution procedure is available onBIACORE 3000 instruments.

Improving biosensor data There are a number of experimental factors that usersmust consider when setting up a biosensor experiment forkinetic analysis [17]. Failure to fit kinetic binding datawith simple interaction models is most often the result ofpoorly designed and executed experiments [9••]. Severalrecent papers describe the importance of improving theexperimental design by employing a low-capacity surface(minimizing the amount of ligand immobilized), orientingthe immobilization of the ligand to the flow-cell surface,using high flow rates of the analyte over the surface, andcarefully regenerating the immobilized ligand after pas-sage of each analyte [5••,17,18••,19•].

Low-capacity surfaces are particularly important for min-imizing mass transport effects, aggregation, andcrowding. Measuring reactions on low-capacity surfaces,however, inherently leads to a lower signal-to-noise ratio.There are two types of noise in SPR analysis: systematicartifacts arising from the sample application and short-term random noise inherent in the detector. Systematicartifacts can be removed from BIACORE data using atwo-step data correction technique as demonstratedin Figure 1.

Step 1: subtracting data from a reference surfaceFigure 1a shows the responses of analyte injected simul-taneously over two flow cells. Flow cell 1 (Fc1) containeda low level of immobilized ligand and flow cell 2 (Fc2) wasactivated and deactivated (using similar reaction chem-istry) as a reference surface [18••]. In this experiment, theartifacts associated with the injection (which include thejumps in signal due to the injection needle positioning,bulk refractive index change, and drift during the associa-tion phase) are as large as the signal attributed to theanalyte binding response. These systematic artifacts areessentially equal between the reaction and reference flowcells, however, and can be removed by subtracting the ref-erence surface data (Fc2) from the reaction surface data(Fc1), as shown in Figure 1b.

Step 2: subtracting blank injectionsAlthough the first referencing step dramatically improvesthe quality of the reaction surface data, it does not accountfor systematic differences between flow cells. For exam-ple, repeating an analyte injection four times and carefullyexamining the binding data revealed a drift in theresponse occurring a few seconds into the reaction, asshown in Figure 1c. When working with low bindingresponses it is common to see small deviations in the data.These deviations are unique to individual flow cells, sen-sor chips, and experimental conditions. The driftobserved during the analyte injections also occurred whenrunning buffer was injected over the ligand surface (seerunning buffer data in Figure 1c). This drift can be elimi-nated by subtracting the average of the running bufferdata from all binding responses collected under the sameconditions (Figure 1d).

Table 3

SPR surfaces.

Chemistry General application

BIACORE surfacesCM5 – carboxymethyl dextran Routine analysisSA – streptavidin Biotin conjugationNTA – nickel chelation His-tagged conjugationHPA – hydrophobic monolayer Create hybrid lipid bilayersB1 – low charge Reduces nonspecific bindingC1 – flat carboxymethylated No dextranF1 – short dextran Large analytesJ1 – gold surface User-defined surfacesL1 – lipophilic dextran Capture liposomes

Affinity sensors surfacesCM – carboxymethyl dextran Routine analysisHydrophobic planar Create lipid monolayersAmino planar Alternative coupling chemistryCarboxylate planar No dextranBiotinylated planar Streptavidin conjugation

As shown in Figure 2, this double referencing proceduremakes it possible to collect reproducible kinetic data onBIACORE 2000 below 2 RU (response unit). The fact thatrepeated analyte responses overlaid well demonstrates thatthe data were reliable, even with a significant level of ran-dom noise. The association and dissociation phase data forall the analyte injections were fit globally to a simplebimolecular reaction model (A+B ⇀↽ AB) [5••]. The short-term random noise had minimal effect on parameter valueswhen the data were fit globally, as illustrated in the highprecision of the returned rate constants (see Figure 2 leg-end) [18••]. The determination of rate constants is abenefit of global analysis.

Global analysis, which is available in BIAevaluation soft-ware 3.0 and CLAMP [20] (www.hci.utah.edu/groups/interaction), is the definitive method for determining kinet-ic constants from SPR experiments [4,5••,18••,21–24]. Useof low-capacity surfaces minimizes many experimentalartifacts; therefore, data collected on these surfaces oftenfits simple models [5••,18••,25–27], which validates BIA-CORE as a tool for interaction analysis.

Mass transport effectsTo accurately interpret rate constants of binding reactions,it is important to minimize mass transport. These effectsoccur when the binding rate of analyte to the ligand is faster

than diffusion of analyte to the surface. As shown in Fig-ure 3, this leads to analyte concentration gradients withinthe flow cell during the association phase [28••]. Therefore,under mass-transport-limited conditions the analyte con-centration is not uniform in position or over time, resultingin binding data that cannot be described by the simplebimolecular reaction model [29••]. During the dissociationphase, the analyte concentration over the reaction surfacedoes not immediately drop to zero due to rebinding events,resulting in a slower apparent dissociation rate.

Surface capacity and flow rate are two experimental para-meters that can be controlled to minimize mass transporteffects. Use of low-capacity surfaces decreases thedemand for analyte, minimizing concentration gradientsin the flow cell. High flow rates increase the transportrate of analyte to the surface and within the dextran layer[30]. Even under optimal experimental conditions, how-ever, it might not be possible to eliminate mass transporteffects due to intrinsically fast association rates. In thesecases, mass transport can be incorporated as a step in thebinding reaction. Response data were simulated underthe conditions presented in Figure 3 to demonstrate thata simple two-compartment model, (Ao ⇀↽ A+B ⇀↽ AB),could be used to accurately recover rate constants whenthe data are influenced by mass transport [29••]. Goldstein and co-workers [28••,31] recently described

56 Analytical biotechnology

Figure 1

Referencing of BIACORE 2000 biosensordata. (a) Sensorgrams of analyte (233 nM ofIL-2) injected over a ligand surface, Fc1 (IL-2α-receptor) and a reference flow cell, Fc2.Data were zeroed on the x- and y-axes at apoint immediately prior to the injection.(b) Subtraction of reference data (Fc2) fromboth Fc1 and Fc2. (c) Overlay of fourreplicate injections of analyte and runningbuffer over immobilized ligand surface.(d) Corrected response after subtraction ofthe average of running buffer data from boththe analyte and running buffer data sets.Reproduced with permission from [18••].

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the theoretical underpinnings of this simple transportmodel in which binding occurs in two steps: transport ofthe analyte in the bulk solution to the sensor surface(Ao ⇀↽ A), followed by reaction of the analyte with theimmobilised ligand (A+B ⇀↽ AB). This model has beenused to analyze several experimental systems on BIA-CORE [5••,25,26] making it possible to accuratelyresolve association rate constants faster than 107 M–1 s–1.

Recent theoretical and experimental work has establishedthat mass transport in the BIACORE flow cell is wellunderstood. This effect is easy to identify by varying theflow rate [5••] and can be minimized by employing low-capacity surfaces and high flow rates. When mass transportis inescapable, it can be modeled as a parameter in dataanalysis. In fact, a benefit of mass transport is that it maybe used to determine the active concentration of analyte[4]. This is done by measuring analyte-binding rates at dif-ferent flow rates. Detailed description of the theory andexperimental design of active concentration determinationby SPR is well described in [32,33].

Detecting small-molecule binding The high sensitivity and reproducibility of BIACOREinstruments make it possible to directly monitor the bind-ing of low molecular mass compounds to immobilizedmacromolecules [3••,34–37] (see Figure 4). Markgren et al.[3••] monitored the binding of an inhibitor to HIV-1 pro-teinase (Figure 4a) and demonstrated how BIACOREinstruments can be used in a screening mode to identifybinders. Kampranis et al. [35] measured the kinetics ofnovobiocin binding to DNA gyrase (Figure 4b). Malmqvist[36] reported the ability to monitor the weak-affinity inter-action of lactose binding to an immobilized antibody(dissociation constant KD = 120 μM; Figure 4c). Strandh

et al. [37] demonstrated the possibility of characterizingeven lower affinity interactions with KDs of ~1 mM.

The ability to monitor small-molecule binding will have asignificant impact on pharmaceutical discovery. The lackof labeling requirements and low sample consumption


Figure 2

Global analysis of biosensor data. Black lines represent analyte (IL-2)injected over a low capacity ligand surface (IL-2 α-receptor). Theanalyte concentrations were 233, 78, 26, 8.6, 2.9, and 0 nM in 10 mMsodium phosphate, 150 mM sodium chloride, 0.005% P20, 0.1 mg/mLbovine serum albumin, pH 7.4 at 5°C. Binding data were collected at aflow rate of 100 μL/min. Four replicates of each analyte concentrationwere injected in random order. Red curves represent the best fit of thebinding responses to a simple bimolecular reaction model (A+B ⇀↽ AB)using CLAMP [5••]. The association (ka) and dissociation (kd) rateconstants were 4.66 ± 0.04 × 106 M–1 s–1 and 0.0420 ± 0.0002 s–1,respectively. Reproduced with permission from [18••].

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Figure 3

Snapshots of concentration gradients in theBIACORE 2000 flow cell under masstransport-limited conditions. The panels showthe spatial variation in the analyteconcentration during the association (top) anddissociation (bottom) at 0.1, 1.0 and 10.0 sinto each phase. The colored gradients (C/CT)shown on the right represent the fraction ofinjected analyte concentration (CT). Forconvenience, the reaction surface wasmodeled on the bottom of the flow cell (inactuality, the ligand is immobilized on the topsurface). The panels are not drawn to scale.The height/length ratio of the BIACORE 2000flow cell is actually 50 μm × 2.4 mm.Reproduced with permission from [28••].

makes the currently available BIACORE technologyimmediately applicable as a secondary screening tool.This technology provides high-resolution functional char-acterization of hits from primary screens. Ulike otheranalytical instruments, BIACORE technology is

amenable to analyzing interactions in the presence ofdimethylsulfoxide (DMSO; a critical feature as manysmall molecules are aqueous-soluble only at very low con-centrations). BIACORE 2000 and 3000 instruments havea throughput of 100–300 samples per day depending onthe assay conditions. As such, BIACORE is an ideal com-plement to current secondary screening technologies. Ittherefore provides binding information not obtainablefrom normal screening methods. With a single injection itis possible to rank binders based on affinity as well as asso-ciation and dissociation kinetics [3••]. With multiple flowcells, assays may be designed with either binding partnerimmobilized on a surface and the screen run to determinewhat protein the compound is binding. Direct compar-isons can also be made between different isotypes ofreceptors or enzymes in order to identify selectivebinders. Beyond discovery, small-molecule detection willbe useful in pharmacological studies. For example, drugcandidates could be screened for binding to carrier plasmaproteins and lipoproteins as well as for membrane perme-ability to monitor the oral adsorption to, and transferacross, the gastrointestinal tract and exchange across theblood/organ barrier.

Membrane surfaces Advanced biosensor surfaces are making it possible tomonitor protein interactions with lipid surfaces and mem-brane-associated proteins. The commercially availableHPA (hydrophobic) and L1 (lipophilic) sensor chips fromBIACORE are designed to create stable membrane sur-faces within the flow cell. The HPA chip contains aself-assembled monolayer of alkylthiols covalently linkedto the gold surface. Liposomes or vesicles of the user’schoice can be fused to this surface to form a hybrid lipidmonolayer [38•]. Several researchers have described theuse of these monolayer surfaces to monitor protein–lipidinteractions (e.g. glycolipids and phospholipids) [39–44].In contrast, the L1 chip surface contains an alkyl chainimmobilized to the dextran matrix. Liposomes can be cap-tured, forming a lipid bilayer that closely mimics biologicalmembranes. These surfaces are stable and have the poten-tial to incorporate transmembrane receptors.

In addition to the examination of membrane-bound pro-teins, lipid surfaces provide two advantages over dextransurfaces: firstly, proteins may be specifically oriented with-in the membrane; and secondly, multivalent interactionscan occur due to membrane fluidity and the resulting pro-tein diffusion throughout the membrane (mimicking in vivobehavior). Implementation of this strategy has beendescribed in two recent publications [45,46]. In both, histi-dine-tagged proteins were sequestered within the nickelsalt-containing liposome monolayers on HPA sensor chips.Celia et al. [45] noted minor disadvantages of this lipid/pro-tein/protein construction, including the narrow pH rangeamenable for analysis and the requsite exclusion of cationchelators from the buffer. Even with these restrictions, thebinding of soluble T cell receptors (TCRs) to major histo-


Figure 4

Concentration-dependent analysis of small-molecule–proteininteractions. (a) A dihydroxyhexanediamine analog (1 nM–36 μM)binding to immobilized HIV-1 proteinase [3••]. (b) Novobiocin(molecular mass = 612.7 g/mole) (20–100 nM) binding to animmobilized DNA gyrase truncate [35]. (c) Replicate injections oflactose (molecular mass = 342.3 g/mole) (5.8 μM–1.5 mM) binding toan immobilized antibody [36]. Reproduced with permission from[3••,35,36].


compatibility complex (MHC) molecules anchored on themembrane was observable and the global fits of the datarevealed that the optimal TCR–MHC molecule interactionrequired a specific orientation [45]. Under similar experi-mental conditions, Dorn et al. [46] captured 20 Sproteosome, a multifunctional protease, on a membranesurface and demonstrated functionality by enzymaticdegradation of insulin and the specific inhibition of the pro-teosome–insulin interaction by an inhibitor.

The application of SPR biosensors to the study of the inter-action of membrane-associated proteins will continue toevolve. Future advances might include inserting ion chan-nels, seven-transmembrane receptors, and cell-signalingmolecules within the immobilized lipid surface. Reconstitu-tion of native membranes on a sensor chip is the penultimatesurface approaching the analysis of in vivo systems.

ConclusionsCareful experimental design and data processing methodscan dramatically improve the quality of biosensor data,making it possible to fit responses to simple models. There-fore, when properly utilized, SPR biosensors can bepowerful biophysical tools for determining kinetic andequilibrium constants of molecular interactions. Figure 5

illustrates that affinities garnered from BIACORE analysesagree well with those determined by solution-based meth-ods (e.g. titration calorimetry [26,47,48], analyticalultracentrifugation [49], and electromobility shift assay [6])across a wide concentration range, demonstrating that inmany cases immobilizing a ligand does not significantlyalter the equilibrium dissociation constant for the reaction.

The ability to accurately measure low binding responsesminimizes mass-transport effects and permits the monitor-ing of small-molecule interactions. SPR biosensors willmake a significant impact in pharmaceutical discovery anddevelopment by allowing high-resolution functional analy-sis of small-molecule interactions. In addition, theavailability of stable lipid surfaces will provide a means tostudy a host of membrane-associated biological processesfor which analytical resources have been lacking. The limitof SPR biosensor applications is yet to be reached.

References and recommended readingPapers of particular interest, published within the annual period of review,have been highlighted as:

• of special interest••of outstanding interest

1. Markey F: What is SPR anyway? BIA J 1999, 6:14-17.

2. Lackmann M, Bucci T, Mann RJ, Kravets LA, Viney E, Smith F, MoritzRL, Carter W, Simpson RJ, Nicola NA et al.: Purification of aligand for the EPH-like receptor HEK using a biosensor-basedaffinity detection approach. Proc Natl Acad Sci USA 1996,93:2523-2527.

3. Markgren P-O, Hämäläinen M, Danielson U: Screening of•• compounds interacting with HIV-1 proteinase using optical

biosensor technology. Anal Biochem 1999, 265:340-350.This paper demonstrates the potential to use BIACORE technology to mon-itor the interactions of small compounds with macromolecules and experi-mental procedures are presented that describe the analysis of compoundsin dimethylsulfoxide. In addition, the authors illustrate how to present dataobtained from screens.

4. Zeder-Lutz G, Neurath AR, Van Regenmortel MH: Kinetics ofinteraction between 3-hydroxyphthaloyl-beta-lactoglobulin andCD4 molecules. Biologicals 1999, 27:29-34.

5. Morton TA, Myszka DG: Kinetic analysis of macromolecular•• interactions using surface plasmon resonance biosensors.

Methods Enzymol 1998, 295:268-294.This paper describes methods for improving experimental design. Data fromtwo different capacity surfaces were simultaneously fit to a mass transport-limited model, yielding an association rate of 8 × 107 M–1 s–1.

6. Myszka DG, Jonsen MD, Graves BJ: Equilibrium analysis of highaffinity interactions using BIACORE. Anal Biochem 1998,26:326-330.

7. Roos H, Karlsson R, Nilshans H, Persson A: Thermodynamicanalysis of protein interactions with biosensor technology. J MolRecognit 1998, 11:204-210.

8. Jönsson U, Fägerstam L, Ivarsson B, Johnsson B, Karlsson R, Lundh K,Löfås S, Persson B, Roos H, Rönnberg I et al.: Real-time biospecificinteraction analysis using surface plasmon resonance and asensor chip technology. BioTechniques 1991, 11:620-627.

9. Myszka DG: Survey of the 1998 optical biosensor literature. J Mol•• Recognit 1999, 12:in press.This paper reviews the entire 1998 surface plasmon resonance literatureand suggests experimental parameters to include in future publications.

10. Lowe PA, Clark TJ, Davies RJ, Edwards PR, Kinning T, Yeung D: Newapproaches for the analysis of molecular recognition using theIAsys evanescent wave biosensor. J Mol Recognit 1998,11:194-199.

Figure 5

Comparison of SPR- and solution-determined affinity constants.Staphylococcal enterotoxin mutants (�) binding to immobilized T cellreceptor 14.3.d β chain, as measured by analytical ultracentrifugationand SPR [49]. HIV-1 capsid (�) binding to immobilized cyclophilin A,as measured by isothermal titration calorimetry and SPR [47]. Fabfragment of antibody CE9.1 binding to mutant CD4 receptor (F43V)(�), as measured by isothermal titration calorimetry and SPR [48].Wild-type CD4 binding to CE9.1 antibody surface (�) as measured byisothermal titration calorimetry and SPR [26]. Transcription factor Ets-1binding to immobilized DNA (�), as measured by electromobility shiftassay and SPR [6].

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11. Wink T, de Beer J, Hennink WE, Bult A, van Bennekom WP:• Interaction between plasmid DNA and cationic polymers studied

by surface plasmon resonance spectroscopy. Anal Chem 1999,71:801-805.

This paper describes the use of an IBIS cuvette-based biosensor, empha-sizing novel immobilization of synthetic polymers on the sensor surface andthe polymers’ interactions with plasmid DNA.

12. Kondoh M, Usui T, Nishikiori T, Osada H: Apoptosis induction via• microtubule disassembly by an antitumor compound, pironetin.

Biochem J 1999, 340:411-416.This paper reports using the Nippon SPR 670 biosensor to examine drugbinding to immobilized tubulin.

13. Kukanskis K, Elkind J, Melendez J, Murphy T, Garner H: Detection of• DNA hybridization using the Texas Instruments, Inc. TISPR-1

surface plasmon resonance biosensor. Anal Biochem 1999,274:7-17.

This paper provides a detailed description of the Texas Instruments’ inte-grated detector, surface preparation chemistry, and optimization of experi-mental parameters.

14. Nelson RW, Jarvik JW, Taillon BE, Tubbs KA: BIA/MS of epitope-tagged peptides directly from E. coli lysate: multiplex detectionand protein identification at low-femtomole to subfemtomolelevels. Anal Chem 1999, 71:2858-2865.

15. Nelson RW, Krone JR: Advances in surface plasmon resonancebiomolecular interaction analysis mass spectrometry (BIA/MS).J Mol Recognit 1999, 12:77-93.

16. Sönksen C, Nordhoff E, Jansson O, Malmqvist M, Roepstorff P:•• Combining MALDI mass spectrometry and biomolecular

interaction analysis using a biomolecular interaction analysisinstrument. Anal Chem 1998, 70:2731-2736.

This paper describes new BIA/MS techniques, including improvedwashing steps and eluting concentrated, small-volume samples from thesensor chip.

17. Myszka DG: Kinetic analysis of macromolecular interactions usingsurface plasmon resonance biosensors. Curr Opin Biotechnol1997, 8:50-57.

18. Myszka DG: Improving biosensor analysis. J Mol Recognit 1999,•• 12:279-284.This paper details the common approaches to improving experimentaldesign. Double referencing procedures make it possible to collect reliabledata on BIACORE 2000 below 2 RU.

19. Andersson K, Hämäläinen M, Malmqvist M: Identification and• optimization of regeneration conditions for affinity-based

biosensor assays. A multivariate cocktail approach. Anal Chem1999, 71:2475-2481.

This paper outlines a cocktail approach to removing the guesswork involvedin sensor chip regeneration and describes a series of stock solutions, whichin various combinations should disrupt the forces between immobilized lig-and and bound species.

20. Myszka DG, Morton TA: CLAMP: a biosensor kinetic dataanalysis program. Trends Biochem Sci 1998, 23:149-150.

21. Cunningham SA, Tran TM, Arrate MP, Brock TA: Characterizationof vascular endothelial cell growth factor interactions with thekinase insert domain-containing receptor tyrosine kinase. Areal time kinetic study. J Biol Chem 1999, 274:18421-18427.

22. MacKenzie CR, Hirama T, Buckley JT: Analysis of receptor bindingby the channel-forming toxin aerolysin using surface plasmonresonance. J Biol Chem 1999, 274:22604-22609.

23. Martin WL, Bjorkman PJ: Characterization of the 2:1 complexbetween the class I MHC-related Fc receptor and its Fc ligandin solution. Biochemistry 1999, 38:12639-12647.

24. Zou W, Mackenzie R, Thérien L, Hirama T, Yang Q, Gidney MA,Jennings HJ: Conformational epitope of the type III group BStreptococcus capsular polysaccharide. J Immunol 1999,163:820-825.

25. Myszka D, Morton T, Doyle M, Chaiken I: Kinetic analysis of aprotein antigen–anitbody interaction limited by mass transporton an optical biosensor. Biophys Chem 1997, 64:127-137.

26. Myszka D, Arulanantham P, Sana T, Wu Z, Morton T, Ciardelli TL:Kinetic analysis of ligand binding to interleukin-2 receptorcomplexes created on an optical biosensor surface. Protein Sci1996, 5:2468-2478.

27. Stuart JK, Myszka DG, Joss L, Mitchell RS, McDonald SM, Xie Z,Takayama S, Reed JC, Ely KR: Characterization of interactions

between the anti-apoptotic protein BAG-1 and Hsc70 molecularchaperones. J Biol Chem 1998, 273:22506-22514.

28. Goldstein B, Coombs D, He X, Pineda AR, Wofsy C: The influence•• of transport on the kinetics of binding to surface receptors:

applications to cells and BIACORE. J Mol Recognit 1999,12:293-299.

These authors model the concentration gradients that occur in the flow cellunder mass transport-limited conditions and discuss why the simple two-compartment model adequately describes these data.

29. Myszka DG, He X, Dembo M, Morton TA, Goldstein B: Extending the•• range of rate constants available from BIACORE: interpreting

mass transport-influenced binding data. Biophys J 1998,75:583-594.

This paper demonstrates that a simple two-compartment model(Ao ⇀↽ A+B ⇀↽ AB) can accurately describe mass-transport-limited data andreturns correct rate constants for the binding reaction.

30. Witz J: Kinetic analysis of analyte binding by optical biosensors:hydrodynamic penetration of the analyte flow into the polymermatrix reduces the influence of mass transport. Anal Biochem1999, 270:201-206.

31. Mason T, Pineda AR, Wofsy C, Goldstein B: Effective rate modelsfor the analysis of transport-dependent biosensor data. MathBiosci 1999, 159:123-144.

32. Christensen LLH: Theoretical analysis of protein concentrationdetermination using biosensor technology under conditions ofpartial mass transport limitation. Anal Biochem 1997,249:153-164.

33. Richalet-Secordel PM, Rauffer-Bruyere N, Christensen LL, Ofenloch-Haehnle B, Seidel C, van Regenmortel MH: Concentrationmeasurement of unpurified proteins using biosensor technologyunder conditions of partial mass transport limitations.Anal Biochem 1997, 249:165-173.

34. Boger DL, Saionz KW: DNA binding properties of key sandramycinanalogues: systematic examination of the intercalationchromophore. Bioorg Med Chem 1999, 7:315-321.

35. Kampranis SC, Gormley NA, Tranter R, Orphanides G, Maxwell A:Probing the binding of coumarins and cyclothialidines to DNAgyrase. Biochemistry 1999, 38:1967-1976.

36. Malmqvist M: BIACORE: an affinity biosensor system forcharacterization of biomolecular interactions. Biochem Soc Trans1999, 27:335-340.

37. Strandh M, Persson B, Roos H, Ohlson S: Studies of interactionswith weak affinities and low-molecular-weight compounds usingsurface plasmon resonance technology. J Mol Recognit 1998,11:188-190.

38. Evans SV, MacKenzie CR: Characterization of protein–glycolipid•· recognition at the membrane bilayer. J Mol Recognit 1999,

12:155-168.This review of glycolipid–protein interactions described different methods ofconstructing monolayers and bilayers on the sensor chip, as well as some ofthe recent applications of this technology.

39. Ariga T, Yu RK: GM1 inhibits amyloid beta-protein-inducedcytokine release. Neurochem Res 1999, 24:219-226.

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Rebecca L Rich and David G Myszka- Advances in surface plasmon resonance biosensor analysis