drug-nucleic acid interactions

Accordingly, we have solicited from experts in a variety of disciplines articles that concisely but completely describe useful methods and strategies for studying small molecule binding

P r e f a c e

Progress in molecular biology and studies of small molecule binding to nu- cleic acids have been inextricably linked A testament to that fact is the inclusion of eight papers directly concerned with drug-DNA interactions among the recently published list of the 100 most cited articles in the Journal of Molecular Biology

Few other scientific areas are as well represented on that list Small molecules have perhaps taught us more about DNA than DNA has taught us about small molecules Watson, for example, notes in the Molecular Biology of the Gene that the "fact that intercalation occurs so readily indicates that it is energetically favored [and] is additional evidence for the metastability of the double-helical structure its ability

to assume many inherently unstable configurations that normally revert quickly back to the standard B conformation." From that point of view, intercalation pro- vided one of the very first indications of the plasticity of DNA, an area that has blossomed to reveal an incredible diversity of structural forms Perhaps the most widespread interest in small molecules that bind to nucleic acids stems from their potential as useful pharmaceutical agents Indeed, some of the very best anticancer drugs are well-documented DNA binders While interest in drug-DNA interactions has at times waned, recent advances in chemical synthesis, analytical instrumenta- tion to measure binding, and structural biology have greatly enhanced the potential for rational design of new therapeutic compounds Accordingly, studies on the in- teraction of small molecules with nucleic acids have taken on new life and have helped spawn several emergent biotechnology companies dedicated to exploiting the promise of making new types of pharmaceuticals targeted at nucleic acids The aim of this volume is to consolidate key methods for studying ligand- nucleic acid interactions, both old and new, into a convenient source Accordingly,

we have solicited from experts in a variety of disciplines articles that concisely but completely describe useful methods and strategies for studying small molecule binding to nucleic acids Techniques that are useful now range from biophysical and chemical approaches to methods rooted in molecular and cell biology We hope that this volume will serve as a useful compendium of methods both to newcomers entering the field as well as to scientists already actively engaged in research in this area

JONATHAN B CHAIRES MICHAEL J WARING

xiii

C o n t r i b u t o r s to V o l u m e 3 4 0

Article numbers are in parentheses following the names o f contributors

Affiliations listed are current

CHRISTIAN BAILLY (24, 31), INSERM

U-524, and Laboratoire de Pharmaco-

logie Antitumorale du Centre Oscar

Lambret IRCL, 59045 Lille, France

ALBERT S BENIGHT (8), Department

of Chemistry, University of Illinois,

Chicago, Illinois 60607 and DNA Codes

LLC, Chicago, Illinois 60601

LAWRENCE A BOTTOMLEY (11), School of

Chemistry and Biochemistry, Georgia In-

stitute of Technology, Atlanta, Georgia

30332

SOPHIA Y B REUSEGEM (10), Laboratoryfi~r

Fluorescence Dynamics, Department of

Physics, University of Illinois, Urbana,

Illinois 61801

JONATHAN B CHAIRES (1, 5, 27), De-

partment of Biochemistry, University of

Mississippi Medical Center, Jackson,

Mississippi 39216

YEN CHOO (30), Gendaq Limited, London

NW7 lAD, United Kingdom

BABUR Z CHOWDHRY (6), School of Chem-

ical and Life Sciences, University of

Greenwich, London SE18 6PF, United

Kingdom

ROBERT M CLEGG (10), Laboratory for

Fluorescence Dynamics, Department of

Physics, University of Illinois, Urbana,

Illinois 61801

DONALD M CROTHERS (3, 23), Depart-

ment of Chemistry, Yale University, New

Haven, Connecticut 06520-8107

MARK S CUBBERLEY (28), Department of

Chemistry and Biochemistry, University

of Texas, Austin, Texas 78712

CARLEEN M CULL1NANE (23), Pharma- cology and Developmental Therapeutics Unit, Peter MacCallum Cancer Institute, Victoria 3002, Australia

SUZANNE M CUTrS, (23), Department of Biochemistry, La Trobe University, Bun- doora, Victoria 3083, Australia

JAMES C DABROWIAK (21), Department of Chemistry, Center for Science and Tech- nology, Syracuse University, Syracuse, New York 13244

TINA M DAVIS (2), Department of Chem- istry, Georgia State University, Atlanta, Georgia 30303

PETER B DERVAN (22), Department of Chemistry, California Institute of Tech- nology, Pasadena, California 91125

MAGDALENA ERIKSSON (4), Department of Physical Chemistry, Chalmers University

of Technology, Gothenburg SE-41296, Sweden, and Department of BiD- chemistry, University of Gothenburg, Gothenburg SE-40530, Sweden

CHRISTOPHE ESCUDI~ (16), Laboratoire

de Biophysique, INSERM U201, CNRS UMR 8646, Museum National d'Histoire Naturelle, 75231 Paris Cedex 05, France

IZABELA FOKT (27), M D Anderson Can- cer Center, University of Texas, Houston, Texas 77030

KEITH R Fox (20), Division of Biochemistry and Molecular Biology, School of Bio- logical Sciences, University of Southamp- ton, Southampton S016 7PX, United Kingdom

x CONTRIBUTORS TO VOLUME 340

THI~RI~SE GARESTIER (16), Laboratoire

de Biophysique, 1NSERM U201, CNRS

UMR 8646, Museum National d'Histoire

Naturelle, 75231 Paris Cedex 05, France

JERRY GOOD1SMAN (21), Department of

Chemistry, Center for Science and Tech-

nology, Syracuse University, Syracuse,

New York 13244

DAVID E GRAVES (18), Department of

Chemistry, University of Mississippi,

University, Mississippi 38677

KEITH A GRIMALDI (17), CRC Drug-DNA

Interactions Research Group, Royal Free

and University College Medical School,

University College London, London WI P

8BT, United Kingdom

VLAD1M1R M GUELEV (28), Department of

Chemistry and Biochemistry, Universi~"

of Texas, Austin, Texas 78712

IHTSHAMUL HAG (6), Krebs Institute

for Biomolecular Science, Department

of Chemisto, University of Sheffield,

Sheffield $3 7HF, United Kingdom

JOHN A HARTLEY (17), CRC Drug-DNA

Interactions Research Group, Royal Free

and University College Medical School,

University College London, London W1P

8BT, United Kingdom

PAUL B HOPKINS (19), Department of

Chemistry, University of Washington,

Seattle, Washington 98195

LAURENCE H HURLEY (29), College of

Pharmacy, University of Arizona, Tucson,

Arizona 85721 and Arizona Cancer Cen-

ter, Tucson, Arizona 85724

MARK ISALAN (30), Gendaq Limited,

London NW7 laD, United Kingdom

BRENT L IVERSON (28), Department of

Chemistry and Biochemistry, University

of Texas, Austin, Texas 78712

TERENCE C JENKINS (6), Yorkshire Cancer

Research Laboratory of Drug Design,

Cancer Research Group, University of

Bradford, Bradford BD7 1DP, United

Kingdom

BESIK I KANKIA (7), Department of Pharmaceutical Sciences, University ~f Nebraska Medical Center, Omaha, Nebraska 68198

ASMITA KUMAR (33), Department of Bio- chemistry, University of Mississippi, Jackson, Mississippi 39216

DONALD W KUPKE (7), Department of Chemistrry, University of Virginia, Char- lottesville, Virginia 22901

ANDREW N LANE (12), Division of Molecu- lar Structure, National Institute for Med- ical Research, London NW7 IAA, United Kingdom

GREGORY H LENO (33), lnfgen Incorpo- rated, DeForest, Wisconsin 53532

PETER T LILLEHEI (l l), School of Chem- istry and Biochemistry, Georgia Institute

of Technology, Atlanta, Georgia 30332

R SCOTT LOKEY (28), Department of Chemistry and Biochemistr); University

of Texas, Austin, Texas 78712

FRANK G LOONTIENS (10), Laboratory for Biochemistry, WEVIO, University of Gent, Gent 9000, Belgium

RYAN A LUCE (19), Department of Chem- istr); University of Washington, Seattle, Washington 98195

CHRISTOPHE MARCHAND (32), Laboratory

of Molecular Pharmacology, Division

of Basic Sciences, National Cancer In- stitute, National Institutes of Health, Bethesda, Maryland 20892

LUIS A MARKY (7), Department of Pharmaceutical Sciences, University of Nebraska Medical Center, Omaha, Nebraska 68198

CLAIRE J MCGURK (17), CRC Drug-DNA Interactions Research Group, Royal Free and University College Medical School, University College London, London WI P 8BT, United Kingdom

CONTRIBUTORS TO VOLUME 340 xi

PETER J MCHUGH (17), CRC Drug-DNA

Interactions Research Group, Royal Free

and University College Medical School,

University College London, London W1P

8BT, United Kingdom

MARK P MCPIKE (21), Department of

Chemistry, Center for Science and Tech-

nolog); Syracuse University, Syracuse,

New York 13244

MEREDITH M MURR (28), Department of

Chemistry and Biochemistry, Universi~

of Texas, Austin, Texas 78712

NOURI NEAMATI (32), Laboratory of Molec-

ular Pharmacology, Division of Basic

Sciences, National Cancer Institute, Na-

tional Institutes of Health, Bethesda,

Maryland 20892

JAROSLAV NESETI~IL (8), Department of

Applied Mathematics, Faculty of Math-

ematics and Physics, Charles Universi~,

118 O0 Praha 1, Czech Republic

PETER E NIELSEN (15), Department of

Medical Biochemistry and Genetics, The

Panum Institute, University of Copen-

hagen, Copenhagen DK-2200, Denmark

BENGT NORD~N (4), Department of Phys-

ical Chemistry, Chalmers University

of Technology, Gothenburg SE-41296,

Sweden

of Chemistry, University of Illinois,

Chicago, Illinois 60607, and Integrated

DNA Technologies, Coralville, Iowa

52241

PETR PAN(~OSKA (8), Department of Chem-

istry, University of Illinois, Chicago,

Illinois 60607, and Center for Discrete

Mathematics, Applied Computer Science

and Applications DIMAT1A, Charles

University, Prague, Czech Republic, and

DNA Codes LLC, Chicago, Illinois

60601

MARY ELIZABETH PEEK (13), School of

Chemistry and Biochemistry, Georgia In-

stitute of Technology, Atlanta, Georgia

30332

DON R PHILLIPS (23), Department

of Biochemistry, LaTrobe Universit3; Bundoora, Victoria 3083, Australia

YVES POMMIER (32), Laboratory of Molec- ular Pharmacolog); Division of Basic Sciences, National Cancer Institute, Na- tional Institutes of Health, Bethesda, Maryland 20892

JOSl~ PORTUGAL (25, 27), Departamento de Biologia Molecular y Celular, lnstituto de Biologia Molecular de Barcelona, CSIC, Barcelona 08034, Spain

WALDEMAR PRIEBE (27), M D Ander- son Cancer Center, University of Texas, Houston, Texas 77030

TERESA PRZEWLOKA (27), M D Ander- son Cancer Center, University of Texas, Houston, Texas 77030

PETER REGENFUSS (10), Laboratory for Fluorescence Dynamics, Department of Physics, Universi~' of Illinois, Urbana, Illinois 61801

JINSONG REN (5), Department of Biochem- istry, University of Mississippi Medical Center, Jackson, Mississippi 39216

PETER V RICCELLI (8), Department

of Chemistry, University of Illinois, Chicago, Illinois 60607, and DNA Codes LLC, Chicago, Illinois 60601

RICHARD D SHEARDY (26), Department of Chemistry and Biochemistry, Seton Hall Universit); South Orange, New Jersey

DAEKYU SUN (29), Institute for Drug De- velopment, San Antonio, Texas 78245

JIAN-SHENG SUN (16), Laboratoire de Biophysique, INSERM U201, CNRS UMR 8646, Museum National d'Histoire Naturelle, 75231 Paris Cedex 05, France

xii CONTRIBUTORS TO VOLUME 3 4 0

MICHAEL J TILBY (17), Cancer Research

Unit, Medical School, University of New-

castle Upon Tyne, Newcastle NE2 4HH,

United Kingdom

JOHN W TRAUGER (22), Department

of Chemistry, California Institute

of Technology, Pasadena, California

91125

JOHN O TRENT (14, 27), James Gra-

ham Brown Cancer Center, Department

of Medicine, University of Louisville,

Louisville, Kentucky 40202

PETER M VALLONE (8), Department

of Chemistry, University of Illinois,

Chicago, Illinois 60607 and National

Institute of Standards" and Technology,

Biotechnology Division, Gaithersburg,

Mao, land 20899

MICHAEL J WARING (20, 24), Department

of Pharmacology, University of Cam-

bridge, Cambridge CB2 IQJ, United Kingdom

SUSAN E WELLMAN (9), Department

of Pharmacology and Toxicolog), Uni- versity of Mississippi Medical Center, Jackson, Mississippi 39216

LOREN DEAN WILLIAMS (13), School of Chemistry and Biochemistry, Georgia In- stitute of Technology, Atlanta, Georgia

30332

W DAVID WILSON (2), Department of Chemistry, Georgia State University, Atlanta, Georgia 30303

HONGZH1 XU (33), Department of Biochem- istry, University of Mississippi, Jackson, Mississippi 39216

STEVEN M ZEMAN (3), Department of Chemistry, Yale University, New Haven, Connecticut 06520

[1] ANALYSTS OF LIGAND-DNA BINDING ISOTHERMS 3

[1] Analysis and Interpretation of Ligand-DNA

"How many? How tightly? Where? Why? What of it?" The first two Questions (and

in part the third) can be answered by equilibrium binding studies, and are the pri- mary focus of this chapter The remaining questions concisely express the concerns

of structural and functional studies, and may be addressed by X-ray crystallogra- phy, nuclear magnetic resonance (NMR) techniques, molecular modeling, and a variety of chemical and molecular biological methods Macromolecular binding

is a phenomenon of general interest, and the underlying general principles are the same for ligand binding to proteins or to nucleic acids A number of excellent general treatments of macromolecular binding are available that explain the un- derlying physical chemistry in detail 2-6 What distinguishes the binding of small molecules to DNA from their binding to proteins is the need to account for behav- ior arising from the lattice properties of linear DNA molecules Various neighbor exclusion models have evolved to cope with that complexity, and are described

An excellent discussion of the principles of nucleic acid binding interactions is provided by Bloomfield et al 7

Determination of the binding constant K allows the binding free energy change,

AG, to be calculated by the standard Gibbs equation, AG = - R T In K, where R

is the gas constant and T is the temperature in degrees Kelvin From studies of the temperature dependence of the binding constant, or (preferably) by calorimetric studies, the binding enthalpy (AH) may be obtained The binding free energy may then be partitioned into its enthalpic and entropic components, AG = AH TAS,

where AS is the entropy change Knowledge of these thermodynamic parameters

I G Scatchard, Ann N.Y Acad Sci 51,660 (1949)

2 j T Edsall and J Wyman, "Biophysical Chemistry." Academic Press, New York, 1958

3 j Wyman and S J Gill, "Binding and Linkage." University Science Books, Mill Valley, California,

1990

41 M Klotz, "Ligand Receptor Energetics." John Wiley & Sons, New York, 1997

5 E diCera, "Thermodynamic Theory of Site-Specific Binding Processes in Biological Macro- molecules." Cambridge University Press, Cambridge, 1995

6 G Weber, "Protein Interactions." Chapman & Hall, New York, 1992

7 V A Bloomfield, D M Crothers, and J Ignacio Tinoco, "Nucleic Acids: Structures, Properties and Functions," 1st Ed University Science Books, Sausalito, California, 2000

Copyright © 2001 by Academic Press All rights of reproduction in any form reserved

4 BIOPHYSICAL APPROACHES [ 11 provides a firm foundation for understanding the molecular forces that govern the binding reaction, allowing one to begin to address Scatchard's question "Why?" Details of attempts to parse binding free energies for ligand-DNA interactions in order to understand the contribution of various molecular forces are described in publications from this and other laboratories, s- 12

The aim of this chapter is to offer a concise guide for the analysis and interpre- tation of ligand-DNA binding isotherms Methods for experimentally obtaining binding data are not discussed because detailed, practical descriptions of experi- mental protocols are available 13 15 In this chapter, examples of binding data are taken from results obtained in the author's laboratory with the anticancer agent daunomycin (daunorubicin) Daunomycin is perhaps the best-characterized DNA intercalator, and its binding to a wide variety of DNA sequences and structures has been thoroughly investigated ~ 6,17

M o d e l - I n d e p e n d e n t A p p r o a c h e s

Figure 1 shows the results from two types of binding experiments, each of which addresses one of Scatcbard's queries as directly as possible The method of continuous variations Is-a1 may be used to construct a so-called Job plot (Fig 1A) Binding stoichiometries may be determined from such plots without recourse to any assumed binding model For the data shown in Fig 1A for the interaction

of daunomycin with calf thymus DNA, an inflection near 0.2 mol fraction ligand indicates a binding stoichiometry of one ligand per 3 or 4 base pairs The exact stoichiometry from the inflection at 0.21 mol fraction is (1.0 - 0.21)/0.21 = 3.76 base pairs This value represents the predominant binding mode, although an

s j B Chaires, Anticancer Drug Des 11,569 (1996)

9 j B Chaires, Biopolymers 44, 201 (1997)

l01 Haq, J E Ladbury, B Z Chowdhry, T C Jenkins, and J B Chaires, J Mol Biol 271,244 (1997)

II j Ren, T C Jenkins, and J B Chaires, Biochemistry 39, 8439 (2000)

12 S Mazur, F A Tanious, D Ding, A Kumar, D W Boykin, I J Simpson, S Neidle, and W D

Wilson, J Mol Biol 300, 321 (2000)

13 X Qu and J B Chaires, Methods Enzymol 321, 353 (2000)

L4 T C Jenkins, in "Drug-DNA Interaction Protocols" (K R Fox, ed.), Vol 90, pp 195-218 Humana

Press, Totowa, New Jersey, 1997

15 p C Dedon, in "Current Protocols in Nucleic Acid Chemistry" (S L Beaucage, D E Bergstrom,

G D Glick, and R A Jones, eds.), Vol 1, pp 8.2.1-8.2.8 John Wiley & Sons, New York, 2000

16 j B Chaires, in "Advances in DNA Sequence Specific Agents" (L H Hurley, ed.), Vol 2, pp 141-

167 JAI Press, Greenwich, Connecticut, 1996

17 j B Chaires, Biophys Chem 35, 191 (1990)

18 E Job, Ann Chim (Paris) 9, 113 (1928)

19 C Y Huang, Methods Enzymol 87, 509 (1982)

2o A Waiters, Biomed Biochim Acta 44, 132t (1985)

21 E G Loontiens, E Regenfuss, A Zechel, L Dumortier, and R M Clegg, Biochemistry 29, 9029

[1] ANALYSIS OF L I G A N D - D N A BINDING ISOTHERMS 5

-20 -18 -16 -14 -12 -t0

In Cf FIG I Daunomycin binding to calf thymus DNA (A) Job plot obtained from fluorescence titration studies A F is the difference in fluorescence emission intensity between solutions of daunomycin alone and in the presence of DNA The minimum indicates a binding stoichiometry of 3 or 4 base pairs (B) Binding isotherm for the daunomycin calf thymus DNA interaction The fractional saturation was calculated assuming a 3-bp binding site The abscissa is the natural logarithm of the free daunomycin concentration

inflection near 0.5-0.6 mol fraction indicates an additional binding mode at higher drug concentrations The results shown here, based on fluorescence data, agree well with data based on absorbance changes 2° The Job plot thus answers the question

"How many?" directly In studies of ligand-DNA interactions, this method has been underutilized and its advantages largely unappreciated In the case of multiple binding modes, the method of continuous variations is particularly valuable, and clearly reveals complexities in the binding process Published examples for the groove-binder Hoechst 3325821 and for the bisintercalating anthracycline WP63122 illustrate the value of the method in cases of complicated, multimode binding interactions

Figure 1B shows a titration binding isotherm for the daunomycin-calf thymus DNA interaction In this form, the fractional occupancy of binding sites is shown as

a function of the natural logarithm of the free daunomycin concentration (Ct-) The fractional occupancy was calculated from the experimentally determined binding

22 F Leng, W Priebe, and J B Chaires, Biochemistry 37, 1743 (1998)

6 BIOPHYSICAL APPROACHES [ 1]

ratio r (moles daunomycin bound per mole base pair) and the binding stoichio- metry was determined from the Job plot shown in Fig 1A The form of the plot shown in Fig 1B is regarded by some 3 as the most fundamental representation of binding data because the logarithm of the free ligand activity is proportional to the chemical potential of the ligand For simple binding to identical, noninteracting sites, titration binding curves should be symmetric about a midpoint located at

a ligand concentration that is the reciprocal of the association binding constant, and should cover a span of 1.8 lOgl0 units (4.14 In units) in going from 0.1 to 0.9 fractional saturation 3'4'6 The data shown in Fig 1B cover a span of 5.4 in units (2.4 log~0 units) and represent an essentially complete binding titration curve The span

is greater than expected for simple binding, which indicates negative cooperativity, neighbor exclusion, or heterogeneity of binding sites Perhaps the main advantage

of the data shown in Fig 1B is that they may be analyzed in a model-independent way by using the Wyman concept of median ligand activity 3'5 The free energy of ligation (AGx) to go from a state where no ligand is bound to a degree of saturation

of k? is given by Eq (1):

2

P

where RT has its usual meaning The pronounced advantage of Eq (1) is that it

provides a free energy estimate to attain any degree of saturation without recourse

to any specific binding model Numerical integration of the data in Fig 1 B yields an estimate of AGx = - 7 8 kcal mol I for the full ligation of a daunomycin binding site Free energies derived from binding constants obtained by curve fitting to specific models must agree with this model-independent value if the model is reasonable

N e i g h b o r E x c l u s i o n M o d e l s

Figure 2 shows data for the daunomycin-calf thymus DNA interaction in the form of a Scatchard plot, J by far the most common representation of binding data for ligand-DNA interactions To explain the curvature in such plots, a variety

of neighbor exclusion models were proposed, 23'24 and these have become the most commonly used models for the interpretation of binding isotherms Neighbor exclusion models assume (in their simplest form) that the DNA lattice consists of

an array of identical and noninteracting potential binding sites The base pair is commonly defined as the lattice binding site for duplex DNA Ligand binding

to any one site occludes neighboring sites from binding as defined by the site size n As the lattice approaches saturation, the probability of finding a stretch

23 D M Crothers, Biopolymers 6, 575 (1968)

24 j D McGhee and R H yon Hippel, J Mol Biol 86, 469 (1974)

[ 1] ANALYSIS OF L I G A N D - D N A BINDING ISOTHERMS 7

in Table I The dashed line is the best fit with the exclusion parameter constrained to an integral value

of 3

of unoccupied DNA n base pairs long decreases, producing the curvature seen

in Fig 2 The curvature does not result from a decrease in the intrinsic binding affinity, but rather arises from the decreased probability of finding a free site of the appropriate size McGhee and von Hippe124 derived a closed form equation that embodies the neighbor exclusion model [Eq (2)]:

the neighbor exclusion parameter, and r is the binding ratio Since its publication

in 1974, the McGhee and von Hippel article has been cited more than 1350 times, and is certainly the most commonly used model for the interpretation of ligand binding to DNA It should be noted that Crothers 23 originally derived a neighbor exclusion model 6 years before McGhee and von Hippel, using the statistical mechanics matrix method, but did not offer a convenient closed form equation for use in fitting experimental data However, starting with Crother's characteristic equations, 23 it is straightforward to obtain an equation identical to Eq (2) by simple algebraic rearrangement The two models are therefore equivalent

Equation (2) is commonly used as a fitting function for nonlinear least-squares analysis of ligand-DNA binding isotherms 13'25 In fact, Eq (2) is not appropri- ate for such purposes, because nonlinear fitting by most methods assumes that the independent variable is error free, and that all of the experimental uncertainty resides in the dependent variable 26 Because the binding ratio r is experimen- tally determined, it contains error Worse, the dependent variable r/Cf is a derived quantity so that error in r is propagated into the dependent variable Nonethe- less, nonlinear fitting methods are inevitably used to extract K and n value from experimental data Two excellent software packages are routinely used in this lab- oratory for nonlinear curve fitting, FitAll (MTR Software, Toronto, Canada) and Origin (Microcal, Northampton, MA) FitAll now contains a module for Monte Carlo analysis z7 of the error in parameter estimates Origin contains a module for

a rigorous evaluation of parameter error by determination of their upper and lower bounds at any chosen confidence interval.28 Table I shows the results of fits of the data shown in Fig 2 to the neighbor exclusion model The binding constant, K = 6.6 x 105 M 1, may be used to calculate A G = - 7.8 kcal mol -I That value

is in excellent agreement with that obtained for the model-independent approach described above The exclusion parameter, n = 3.3, agrees well with the estimate

of the site size obtained by the method of continuous variations

The nonintegral value of the exclusion parameter (n) poses a problem For neighbor exclusion models, n should strictly be an integer quantity A fractional value makes no physical sense for a DNA lattice composed of identical, noninter- acting sites] Table I shows, however, that if n is constrained to an integer value (either 3 or 4), the standard deviation of the fit degrades significantly Figure 2 shows the best fit obtained with n = 3 Systematic deviation between the fit and the data are clear, with the data having more curvature than the calculated func- tion This results in nonrandom residuals, indicating an inadequate fit of the model

to the data 28 The fractional values of n that are required to obtain a statistically

25 j j Correia and J B Chaires, Methods Enzymol 240, 593 (1994)

26 M L Johnson, Methods Enzymol 210, 106 (1992)

27 M Straume and M L Johnson, Methods Enzymol 210, 117 (1992)

Methods Enzymol

TABLE I NONLINEAR LEAST-SQUARES FITS OF DAUNOMYCIN BINDING DATA TO

NEIGHBOR EXCLUSION MODELS a

Model K/IOS(M I) N (bp) Standard deviation

a McGhee-von Hippel refers to the neighbor exclusion model speci-

fied by Eq (2) Friedman-Manning refers to the model specified by

Eq (3) K is the association constant and n is the neighbor exclusion

parameter expressed in base pairs The standard deviation is of the

29 R A Friedman and G S Manning, Biopolymers 23, 2671 (1984)

3o R A G Friedman, G S Manning, and M A Shahin, in "Chemistry and Physics of DNA-Ligand Interactions" (N R Kallenbach, ed.), pp 37~64 Adenine Press, Schenectady, New York, 1988

31 M T Record, Jr., C E Anderson, and T M Lohman, Q Rev Biophys 11, 103 (1978)

32 G S Manning, Q Rev Biophys 11, 179 (1978)

10 BIOPHYSICAL APPROACHES [ 1 ]

average phosphate spacing, resulting in a systematic decrease in the polyelectrolyte contribution to the binding free energy A closed form equation was derived 29,3° to embody this model for a univalent intercalator binding to B-form DNA in excess univalent salt solutions:

r K(2+r]-(2+~)lO_[c~i~2o2,1~+~](l_nr)[ 1-nr ],,-1

In Eq (3) K and n have the same meaning as given above, and the added exponential terms describe the decrease in polyelectrolyte contribution to the binding free energy over the course of lattice saturation The parameter ff 0 is the dimensionless charge density parameter and is a function of the structure of duplex DNA Specifi- cally, ~'0 = qZ/EkTb, where q is the charge of an electron, e is the bulk dielectric constant of the liquid, k is the Boltzmann constant, T is the temperature in degrees kelvin, and b is the charge spacing on the DNA chain For standard B-form duplex DNA, b = 1.7 A, and ~'o = 4.2

The results of fits of the data shown in Fig 2 to the Friedman-Manning model are listed in Table I In all cases examined, the fits are statistically worse than those obtained with the simpler McGhee-von Hippel model The standard deviations of the fits shown in Table I are all larger for the Friedman-Manning model than for the McGhee-von Hippel model when both K and n are allowed to vary, even though the former contains an additional parameter The curvature imposed on the fitting function by the added exponential terms in Eq (3) evidently makes it more difficult

to match the curvature in the data Statistically, therefore, there is no justification for use of the Friedman-Manning model instead of the simpler McGhee-von Hippel neighbor exclusion model However appealing the underlying theory, the reality of the experimental data provides the ultimate test of the model In this case, the inclusion of added complexity of polyelectrolyte effects is statistically unwarranted

Use of the neighbor exclusion model has become the standard practice in stud- ies of ligand-DNA interactions The preceding discussion, however, poses some serious questions about its use Fractional values for the neighbor exclusion param- eters are inevitably required to accurately describe the curvature in experimental data, yet have no meaning in the context of the model because integral values were assumed in the derivation of the model Fractional neighbor exclusion values signify that the model is not an appropriate description of the actual data The specific assumption of neighbor exclusion models that is violated is most likely that lattice sites are homogeneous Chemical and enzymatic footprinting methods have shown unambiguously that such is not the case, and that most ligands bind to DNA sites with a wide distribution of affinities 33'34 For the specific case of dauno- mycin, for examples, footprinting studies revealed a strong preference for triplet binding sites with the sequence 5'-(A/T)GC or 5'-(A/T)CG, where the notation

[1] ANALYSIS OF L I G A N D - - D N A BINDING ISOTHERMS 11

(A/T) means that either A or T can occupy the position 35'36 Further, sequences containing runs of AT base pairs were revealed by footprinting not to bind dauno- mycin with appreciable affinity 16 All DNA lattice sites are clearly not identical, in which case a central tenet of the neighbor exclusion model is violated, vitiating its use as an appropriate model for the analysis of binding isotherms This conclusion was strongly supported in the single example available, where both macroscopic binding studies and footprinting studies were carried out on the same homoge- neous fragment, the 165-bp tyrT DNA fragment 37 In that study, the macroscopic binding isotherm was complex in shape, could not be fit to the neighbor exclusion model, and clearly revealed a class of high-affinity binding sites whose number was consistent with the number of high-affinity sites visualized by the companion footprinting titration study

The neighbor exclusion principle has also been questioned on other grounds Rao and Kollman 38 carried out molecular mechanics and molecular dynamics stud- ies from which they concluded that there was no stereochemical basis for neighbor exclusion, at least for the intercalation of 9-aminoacridine into DNA NMR studies from the Wilson laboratory 39'4° showed conclusively that actinomycin D can bind

to adjacent 5'-GpC dinucleotide sites within an oligonucleotide, a finding that con- trasts with the 5- or 6-bp exclusion parameter normally associated with the drug While these are limited and perhaps specialized cases, they do raise questions about the exact physical basis for the neighbor exclusion phenomenon

33 j C Dabrowiak, A A Stankus, and J Goodisman, in "Nucleic Acid Targeted Drug Design C"

(L Propst and T J Perun, eds.) Marcel Dekker, New York, 1992

34 M J Waring and C Bailly, J Mol Recognir 7, 109 (1994)

35 j B Chaires, K R Fox, J E Henera, M Britt, and M J Waring, Biochemist~ 26, 8227 (1987)

36 j B Chaires, J E Herrera, and M J Waring, Biochemistry 29, 6145 (1990)

37 C Bailly, D Suh, M J Waring, and J B Chaires, Biochemistry 37, 1033 (1998)

38 S N Rao and P A Kollman, Proc Natl Acad Sci U.S.A 84, 5735 (1987)

39 W D Wilson, R L Jones, G Zon, E V Scott, D L Banville, and L G Marzilli, J Am Chem Soc

108, 7113 (1986)

40 E V Scott, R L Jones, D L Banville, G Zon, L G Marzilli, and W D Wilson, Biochemist~ 27,

12 BIOPHYSICAL APPROACHES [ 11

the curvature in Scatchard plots entirely to heterogeneity We have explored the simplest case in this scenario, a model in which a dinucleotide binding site is assumed There are 16 possible dinucleotide combinations, 10 of which are unique These are (5' -+ 3'): AT, AA ( TT), TA, AC ( = GT), CA ( = TG), GC, GG ( = CC), CG, GA ( = TC), and AG ( = CT) Because intercalators insert between adjacent base pairs and make contact with both, dinucleotide selectivity is not an unreasonable starting point For binding to dinucleotides (MN), each of which has

a unique affinity (KMN), the binding isotherm is described by

rD

1 q'- KMNCf where the binding ratio ro is now expressed as moles of ligand per mole of di- nucleotide The remaining variables are the dinucleotide frequency (fMN) and the free ligand concentration (C0 Dinucleotide frequencies were experimentally de- termined and tabulated for a numerous natural DNA samples, 41 and may be fixed

as constants for a given DNA Because there are 10 unique dinucleotide steps, the equation has 10 terms, and 10 binding constants must be obtained by nonlinear least-squares fitting of experimental data Although at first glance the exercise of resolving 10 parameters may seem hopeless or even ludicrous, we show that it can in fact be done with the return of reasonable results Figure 3 shows the fit

of daunomycin-calf thymus DNA binding data to the dinucleotide model Note that the binding ratio is now expressed in terms of total dinucleotide concentration rather than the usual base pair concentration The solid line represents the best fit

to the dinucleotide binding model Estimates of 10 binding constants are obtained, and are summarized in Fig 4 The data in Fig 4 are average values of KMN esti- mates obtained from fitting daunomycin binding data obtained with eight different natural DNA samples with known and widely varying dinucleotide frequencies These samples ranged from Clostridium perfringens DNA (31% GC content) to

Micrococcus lysodeikticus DNA (72% GC content) Unique KMN values are re- turned that range over two orders of magnitude Figure 4 shows that AA, AT, and

TG steps represent low-affinity sites TA, GC, and CG have intermediate affinity High-affinity sites are TC, AC, AG, and GG steps

Does this analysis make sense? Is it valid? Several observations suggest that the answer is "yes" to both questions First, footprinting studies of the daunomycin- DNA interaction showed that sequences protected from DNase I cleavage by the bound drug were enriched in the dinucleotides AC, AG, GG, GC, and CG relative

to the tyrT DNA fragment alone that was used for footprinting 35 These are among the very dinucleotide steps with the highest KMN values In contrast, analysis of unprotected cleavage sites from the footprinting experiments showed that such

41 G D Fasman, "Handbook of Biochemistry and Molecular Biology," 3rd Ed CRC Press, Cleveland, Ohio, 1976

[ l ] ANALYSIS OF LIGAND-DNA BINDING ISOTHERMS 1 3

in excellent agreement with the model-independent approach described earlier in this chapter The total free energy (AGT) is specified by

AGT = ~ fMNAGMN = Z fMN(-RT In KMN) (5) where A G M N is the free energy for binding to the dinucleotide step MN Using the known fMN values for calf thymus DNA and the KMN values shown in Fig 4,

a value of AGT = 7.8 kcal mol I is obtained, in excellent agreement with that obtained by model-independent analysis Finally, the binding constants shown in Fig 4 may be used in combination with the known dinucleotide frequency of calf thymus DNA to simulate a binding isotherm, as shown in Fig 5 If these simulated data are then fit to the neighbor exclusion model, values o f K = 6.5 x 10 5 M - I

and n 3.3 bp are obtained These values are in excellent agreement with those obtained by fits to actual experimental data (Fig 2) The key point of this exercise is

a decade of footprinting studies, which show that ligands inevitably bind with a wide range of affinities along the DNA lattice In one sense, we have come full circle Early binding isotherms for proflavin 42 were curved and were interpreted in term of site heterogeneity, namely with two classes of sites later attributed to inter- calation and "outside" binding The dinucleotide model assumes a different kind

42 A R Peackocke and J N H Skerren, Trans Faraday Soc 52, 261 (1956)

of heterogeneity involving a larger number of sites derived from a fundamental property of any DNA, its nearest neighbor dinucleotide frequency

C o p i n g w i t h C o o p e r a t i v i t y

In some cases, ligand-DNA binding isotherms exhibit evidence of positive cooperativity In these cases, data cast into the form of a Scatchard plot show positive slopes at low binding ratios (Figs 6 and 7) Analysis and interpretation of such isotherms become even more complicated McGhee and yon Hippe124 (and

Crothers earlier 23) presented an extended form of the neighbor exclusion model that included an additional term to account for ligand-ligand interactions The equation for this model is

at isolated sites into proximity, such that they occupy contiguous lattice sites If

~o > 1.0, positive cooperativity results and ligands bind preferentially next to one another If ~o < 1.0, negative cooperativity results, and ligand binding at adjacent sites is hampered A distinctive feature of this model is that the DNA lattice remains and inert array of identical sites All cooperativity results from ligand- ligand interactions of an unspecified nature

A contrasting model that can account for positive cooperativity is the Crothers allosteric model 43 The underlying concept is radically different from the

M c G h e e - v o n Hippel model The allosteric model is analogous to the classic

M o n o d - W y m a n - C h a n g e u x model for allostery 44 derived to explain the cooperative

43 N Dattagupta, M Hogan, and D M Crothers, Biochemistry 19, 5998 (1980)

[ l ] ANALYSIS OF LIGAND DNA BINDING ISOTHERMS 17

At the start of the binding isotherm, the polymer is in the left-handed Z form Beyond the maximum near r = 0.3, daunomycin binding has allosterically converted the polymer to the right-hand form

b i n d i n g o f ligands to proteins, such as the b i n d i n g o f o x y g e n to h e m o g l o b i n Crothers built on the basic concept o f allostery, but i n c l u d e d specific details o f the

m a c r o m o l e c u l a r c o n f o r m a t i o n a l transition and l i g a n d b i n d i n g that were appropri- ate for a D N A lattice The allosteric m o d e l a s s u m e s that the D N A lattice c a n exist

in o n e o f two c o n f o r m a t i o n a l forms (1 a n d 2) T h e transition o f the lattice b e t w e e n these forms proceeds by a n u c l e a t i o n step, followed b y propagation steps:

18 BIOPHYSICAL APPROACHES [ 1 ] with unique neighbor exclusion binding parameters, (K1, n l, o~1) and ( K 2 , t/2,092)

Cooperativity arises when the ligand binds selectively or preferentially to one of the conformational forms Binding then drives an allosteric conformational transition

to the form with higher ligand affinity Eight parameters are needed to compute binding isotherms There is no closed form, analytic equation for the allosteric model Crothers et al wrote a Fortran program that calculates binding isotherms according to the derived statistical mechanical model, 43 a program that has seen modest circulation 45 47 and that has been modified on occasion 4s'49 for easier use

No nonlinear fitting routine has yet been developed that incorporates the allosteric model, and users must optimize binding parameters by successive approximation along with judicious constraint of those selected parameters that can be estimated

by independent methods 5° Nonetheless, use of the allosteric model is unavoidable

in some cases, as examples will show

It is important to contrast the key features of the McGhee-von Hippel and allosteric models In the McGhee-von Hippel model, positive cooperativity arises from ligand-ligand interactions while the DNA lattice remains in a single con- formation For protein binding to DNA, such ligand-ligand interactions might be visualized as protein-protein contacts formed when adjacent lattice sites are occu- pied For small molecule ligands, although such interactions could also occur, it is less easy to ascribe a molecular picture to the process In contrast, positive cooper- ativity arises in the Crothers allosteric model from an underlying conformational transition in the DNA lattice to a form with higher ligand binding affinity The allosteric model was perhaps the first clear statement of the possibility of struc- tural selectivity in ligand-DNA interactions Two examples will illustrate positive cooperativity in ligand-DNA interactions

Figure 6 shows the binding of daunomycin to poly(dA) • poly(dT) Independent studies have shown that poly(dA), poly(dT) undergoes a premelting transition between two helical forms 51-53 The binding data in Fig 6 show a positive slope at low r values, and pass through a maximum near r = 0.05 Attempts to fit these data

to the extended McGhee-von Hippel model yield unsatisfactory results The best fit to that model (with K = 1.4 × 104M -j , n = 2.3 bp, ~o = 2.4) is shown by the

45 j B Chaires, J Biol Chem 261, 8899 (1986)

46 G Y Walker, M E Stone, and T R Krugh, Biochemistry 24, 7462 (1985)

47 G T Walker, M E Stone, and T R Krugh, Biochemistry 24, 7471 (1985)

48 G T Walker, Ph.D Thesis, University of Rochester, 1986

49 D Snh, Ph.D Thesis, University of Mississippi Medical Center, 1993

5o j B Chaires, J Biol Chem 261, 8899 (1986)

51 j E Herrera and J B Chaires, Biochemistry 28, 1993 (1989)

52 S S Chan, K J Breslauer, M E Hogan, D J Kessler, R H Austin, J Ojemann, J M Passner, and N C Wiles, Biochemistry 29, 6161 (1990)

53 S S Chan, K J Breslauer, R H Austin, and M E Hogan, Biochemistry 32, 11776 (1993)

[1] ANALYSIS OF L I G A N D - D N A BINDING ISOTHERMS 19

TABLE II PARAMETER ESTIMATES FOR ALLOSTERIC MODEL "FIT" TO DAUNOMYCIN BINDING TO

POLY(dA).POLY(dT) AND Z-DNA Parameter Description Poly (dA).Poly(dT) a Z-DNA ~'

Neighbor exclusion parameter (form 1) 2.0 2.0 Neighbor exclusion parameter (form 2) 2.0 2.3

a Daunomycin binding to poly(dA).poly(dT) in buffered 0.2 M NaCI solutions (pH 7.0) Parameters result from analysis of the binding data shown in Fig 7

b Daunomycin binding to Z-form [poly(dG-dC)]2 in buffered 3.0 M NaCI solutions (pH 7.0) Parameters result from analysis of the binding data shown in Fig 6

curve with the (+) symbols; no value of ~o can be found that can produce a curve that matches the steep positive slope at the start of the isotherms The model, with

an assumed inert DNA lattice, cannot match the experimental data In contrast, the allosteric model can match the shape of the binding data well, as shown by the solid curve in Fig 6 The best estimates of the parameters for the allosteric model that describe the binding data are collected in Table II The key driving force for the allosteric conversion of poly(dA), poly(dT) is the >4-fold preference for binding to form 2 of the polynucleotide A variety of additional physical and enzymatic tools was used to demonstrate that daunomycin binding was indeed coupled to a conformational change in poly(dA), poly(dT) 51 the essential feature

of the allosteric model

Figure 7 shows daunomycin binding to poly(dG-dC) under solution conditions that initially favor the left-handed Z conformation of the polynucleotide 45'54 The allosteric model with the parameters listed in Table II was used to obtain the solid curve matching the experimental data In this case, the underlying conformational transition to which binding is coupled is the Z-to-B conversion Compared with the first example of binding to poly(dA) • poly(dT), this system is much more coopera- tive The reason for that is the greater preference of daunomycin for the right-hand form relative to the left-handed form (with K z / K I -~ 44; Table II), compared with its preference for the two right-handed helical forms of poly(dA) • poly(dT) (with

K 2 / K I = 4; Table II)

54 X Qu, J O Trent, I Fokt, W Priebe, and J B Chaires, Proc Natl Acad Sci U.S.A 97, 12032

(2O0O)

20 BIOPHYSICAL APPROACHES [ 1 ]

L i g a n d B i n d i n g to O l i g o n u c l e o t i d e s

Advances in synthetic methods have made it relatively easy to prepare DNA

or RNA oligonucleotides of precisely defined length and sequence, and it is now fashionable to use such molecules for ligand binding studies The significant advan- tage of oligonucleotides is their homogeneity There are, however, disadvantages First, end effects may become a consideration in oligonucleotide studies Neigh- bor exclusion models typically assume an "infinite lattice" specifically to avoid end effects, and therefore such models become inapplicable to oligonucleotide systems Polyelectrolyte theory and experiment have shown that significant end effects exist for oligonucleotides less than about 24 bp in length 55'56 Second, problems of appropriate representation of all possible sequence elements may arise in oligonucleotide systems There are 10 unique dinucleotide combinations,

as discussed above, and it is rare that a given oligonucleotide is appropriately designed to contain all possible dinucleotide steps It is always possible that a high-affinity interaction may go undetected because the appropriate site is absent

in the oligonucleotide chosen for study Binding constants may thus be biased by the choice of sequence unless a large number of oligonucleotides is studied For trinucleotides, the situation becomes even worse, because there are 64 possible triplets, 32 of which are unique These concerns should not be taken as a call

to abrogate oligonucleotide studies, but rather to acknowledge their appropriate role Complete binding studies should be comprehensive and should systemat- ically move from long natural DNA samples through synthetic polynucleotides

of defined simple repeating sequences to oligonucleotides of precisely defined sequence Oligonucleotide systems are of particular value for the study of the energetics of binding when other experiments have defined the sequence of the preferred binding site for a given ligand

Neighbor exclusion models are generally inapplicable for the analysis of ligand binding to oligonucleotides because they are based on an "infinite lattice" assump- tion Binding isotherms in these cases are best analyzed by the classic and simple stoichiometric binding models that are described in any number of texts and mono- graphs 2-4 Figure 8 shows an example of daunomycin binding to a 24-bp duplex oligonucleotide with the sequence (5'-TGCATGCATGCATGCATGCATGCA)2 This oligonucleotide was designed and synthesized to contain a repetitive motif containing the preferred daunomycin binding site that emerged from footprinting studies [5'-(A/T)GC; where (A/T) means A or T] Binding data were fit to the simple expression for multiple identical, noninteracting sites:

~tK Cf

1 + K C f

55 M C Olmsted, C E Anderson, and M T Record, Jr., Proc Natl Acad Sci U.S.A 86, 7766 (1989),

56 M C Olmsted, C E Anderson, and M T Record, Jr., Biopolymers 31, 1593 (1991)

[ 1] ANALYSIS OF L I G A N D - D N A BINDING ISOTHERMS 21

FIG 8 Binding of daunomycin to a 24-bp duplex oligonucleotide Data (solid circles) are presented

as a direct plot, with the best fit to Eq (7) shown as the solid line

where r is now expressed as moles of daunomycin bound per mole of oligonu- cleotide, n is the number of sites, and Kis the association constant Nonlinear least- squares fitting of the data yields K = 4.0 (4-0.1) × 106M - 1 and n = 4.3 (-t-0.3) Evidently only four molecules of daunomycin are binding to each oligonucleotide, even though there are six potential sites that contain the preferred triplet sequence

It is possible that the sites near the ends are disfavored, or that there is anticooper- ativity that disfavors binding to adjacent sites In the latter case, more complicated models with added parameters would need to be invoked and implemented An excellent example of such an analysis applied to daunomycin binding to hexanu- cleotide sequences was provided by Rizzo and co-workers 57

S u m m a r y

Binding studies provide information of fundamental and central importance for the complete understanding of ligand-DNA interactions Studies of ligand binding to long natural DNA samples, to synthetic deoxypolynucleotides of simple repeating sequence, and to oligonucleotides of defined sequence are all needed to

57 V Rizzo, C Battistini, A Vigevani, N Sacchi, G Razzano, F Arcamone, A Garbesi, E P Colonna,

Mol Recognit 2, 132 (1989)

22 BIOPHYSICAL APPROACHES [21

begin to understand the interaction in detail Binding studies provide entry into the thermodynamics of the DNA interactions, which in turn provides great insight into the molecular forces that drive the binding process This chapter summarizes both model-dependent and -independent approaches for the analysis and interpretation

of binding isotherms, and should serve as a concise guide for handling experimental data

I M B A Oldstone, "Viurses, Plagues, and History." Oxford University Press, Oxford, 1998

2 W D Wilson and K Li, Curr Med Chem 7, 73 (2000)

3 K Michael and Y Tor, Chem Eur J 4, 2091 (1998)

4 F Walter, Q Vicens, and E Westhof, Curr Opin Chem Biol 3, 694 (1999)

5 K Li, M Fernandez-Saiz, C T Rigl, A Kumar, K G Ragunathan, A W McConnaughie, D W

Boykin, H J Schneider, and W D Wilson, Bioorg Med Chem 5, 1157 (1997)

6 T Hermann and W Westhof, Curt Opinion Biotechnol 8, 278 (1998)

7 C Chow and F M Bogdan, Chem Rev 97, 1489 (1997)

8 M Afshar, C D Prescott, and G Varani, Curr Opin BiotechnoL 10, 59 (1999)

9 M J Rogers, Y V Bukhman, R E McCutchan, and D E Draper, RNA 3, 815 (1997)

10 G L Conn, R R Gutell, and D E Draper, Biochemistry 37, 11980 (1998)

Copyright © 2001 by Academic Press All rights of reproduction in any form reserved

22 BIOPHYSICAL APPROACHES [21

begin to understand the interaction in detail Binding studies provide entry into the thermodynamics of the DNA interactions, which in turn provides great insight into the molecular forces that drive the binding process This chapter summarizes both model-dependent and -independent approaches for the analysis and interpretation

of binding isotherms, and should serve as a concise guide for handling experimental data

I M B A Oldstone, "Viurses, Plagues, and History." Oxford University Press, Oxford, 1998

2 W D Wilson and K Li, Curr Med Chem 7, 73 (2000)

3 K Michael and Y Tor, Chem Eur J 4, 2091 (1998)

4 F Walter, Q Vicens, and E Westhof, Curr Opin Chem Biol 3, 694 (1999)

5 K Li, M Fernandez-Saiz, C T Rigl, A Kumar, K G Ragunathan, A W McConnaughie, D W

Boykin, H J Schneider, and W D Wilson, Bioorg Med Chem 5, 1157 (1997)

6 T Hermann and W Westhof, Curt Opinion Biotechnol 8, 278 (1998)

7 C Chow and F M Bogdan, Chem Rev 97, 1489 (1997)

8 M Afshar, C D Prescott, and G Varani, Curr Opin BiotechnoL 10, 59 (1999)

9 M J Rogers, Y V Bukhman, R E McCutchan, and D E Draper, RNA 3, 815 (1997)

10 G L Conn, R R Gutell, and D E Draper, Biochemistry 37, 11980 (1998)

Copyright © 2001 by Academic Press All rights of reproduction in any form reserved

[2] R N A INTERACTIONS 23

TABLE I SMALL MOLECULES AND THEIR BIOLOGICAL RNA TARGETS

uAA A

A O C A G c c A U c A u U A# AGcGU A

A U 5'-GCC GGU-3'

ACc G C G c c G C c c CC-5'

RNA-ligand interactions can be investigated by the same techniques as D N A - ligand interactions Nuclear magnetic resonance (NMR) 27-33 and X-ray crystallo- graphy 34 can be used to obtain detailed structural information about RNA-ligand complexes, although obtaining the quantity of RNA necessary for such studies is

J l D Fourmy, M I Recht, S C Blanchard, and J D Puglisi, Science 274, 1367 (1996)

12 D Fourmy, S Yoshizawa, and J D Puglisi, J Mol Biol 277, 333 (1998)

13 M I Recht, D Fourmy, S C Blanchard, K D Dahlquist, and J D Puglisi, J Mol BioL 262, 421 (1996)

14 E g Bichenkova, S E Sadat-Ebrahimi, A N Wilton, N O'Toole, D S Marks, and K T Douglas,

Nucleosides Nucleotides 17, 1651 (1998)

15 S E S Ebrahimi, A N Wilton, and K T Douglas, Chem Commun 4, 385 (1997)

16 C E Holmes, R J Duff, G A van der Marel, J van Boom, and S M Hecht, Bioorg Med Chem

5, 1235 0997)

17 U yon Ahsen, J Davies, and R Schroeder, Nature (London) 353, 368 (1991 )

I 8 j Rogers, A H Chang, U yon Ahsen, and R Schroeder, ,l Mol Biol 259, 916 (1996)

Melting experiments by spectroscopic methods such as UV and circular dichro- ism (CD) can provide information regarding the thermodynamics of drug binding.5,38 43 CD has the added ability to provide information regarding the

19 T Hermann and E Westhof, J Mol Biol 276, 903 (1998)

2o M L Zapp, S Stern, and M R Green, Cell 74, 969 (1993)

21 K Li, G Xiao, T Rigl, A Kumar, D W Boykin, and W D Wilson, in "Structure, Motion, Inter-

action and Expression of Biological Macromolecules: Proceedings of the 10th Conversation in the Discipline Biomoleculor Stereodynamics," University of Albany, New York (R H Sarma and M H Sarma, eds.), pp 137-145 Adenine Press, Schenectady, New York, 1998

22 L Ratmeyer, M L Zapp, M R Green, R Vinayak, A Kumar, D W Boykin, and W D Wilson,

Biochemistry 35, 13689 (1996)

23 S Wang, P W Huber, M Cui, A W Czaruik, and H.-Y Mei, Biochemistry 37, 5549 (1998)

24 H.-Y Mei, D P Mack, A A Galan, N S Halim, A Heldsinger, J A Loo, D W Moreland, K A

Sannes-Lowery, L Sharmeen, and A W Czarnik, Bioorg Med Chem 5, 1173 (1997)

25 E Hamy, V Brondani, A Florsheimer, W Stark, M J J Blommers, and T Klimkait, Biochemisttiy

37, 5085 (1998)

26 A R Ferre-D'Amare, K Zhou, and J A Doudna, Nature (London) 395, 567 (1998)

27 M Katahira, S Kobayashi, A Matsugami, K Ouhashi, S Uesugi, R Yamamoto, K Taira,

S Nishikawa, and P Kumar, Nucleic Acids Symp Sel: 42, 269 (1999)

28 W H Gmeiner, Curt Med Chem 5, 115 (1998)

29 S Yoshizawa, D Founny, and J D Puglisi, EMBO J 17, 6437 (1998)

30 Q Chen, R H Shafer, and 1 D Kuntz, Biochemistry 36, 11402 (1997)

31 L Jiang, A K Suri, R Fiala, and D J Patel, Chem Biol 4, 35 (1997)

32 E Hamy, V Brondani, A Florsheimer, W Stark, M J Blommers, and T Klimkait, Biochemistry

36 N Gelus, E Hamy, and C Bailly, Bioorg Med Chem 7, 1075 (1999)

37 G Rosendahl and S Douthwaite, Nucleic Acids Res 22, 357 (1994)

38 L Dassonneville, E Hamy, P Colson, C Houssier, and C Bailly, Nucleic Acids Res 25, 4487

(1997)

39 j E Draper and T C Gluick, Methods Enzymol 259, 281 (1995)

4o D S Pilch, M A Kirolos, X Liu, G E Plum, and K J Breslauer, Biochemistry 34, 9962

(1995)

41 U Sehlstedt, P Aich, J Bergman, H Vallberg, B Norden, and A Graslund, J Mol Biol 278, 31

(1998)

[21 R N A INTERACTIONS 25 mode of drug binding to R N A (groove binding versus intercalation) Fluores- cence experiments are useful for obtaining binding information provided the small molecule has fluorescent properties that are altered on binding to RNA 23 Calori- metric techniques such as differential scanning calorimetry (DSC) and isothermal calorimetry (ITC) provide direct quantitative information regarding the thermo- dynamics of drug binding and are perhaps the most reliable methods for obtaining such information 4°-42 Calorimetric methods, however, usually require substan- tially more material than spectrophotometric methods, and they have difficulty measuring large binding constants

Gel band shift has been the most widely used method for studying nucleic acid-ligand interactions 5,35 Providing that the R N A - s m a l l molecule complex can

be separated from free R N A (i.e., there is a detectable band shift from drug bind- ing) and that the binding kinetics are relatively slow, the technique can provide accurate binding constants and stoichiometry with only minimal quantities of RNA and drug The technique is particularly useful for screening focused combinato- rial libraries that meet the above-described criteria Many RNA-small molecule complexes are not easily separated on gels, however, or have kinetics that are too fast for resolution on gels It is clear that additional methods are required for such complexes

Several newer techniques for studying nucleic acid-small molecule interac- tions have been described Ibis Therapeutics (Carlsbad, CA) a division of Isis Pharmaceuticals, has developed a sophisticated mass spectrometry-based assay for screening mixtures of small molecules for binding to RNA 44-46 The technique permits multiple ligands and multiple RNAs to be screened simultaneously In ad- dition, the technique can provide binding affinities and binding sites on RNA This

is an excellent method for analysis of multiple compounds when the appropriate equipment is available

Luedtke and Tor 47 have developed a novel solid-phase assay for investigating

R N A - s m a l l molecule interactions The assay was used to identify small molecules that bind to the Rev-responsive element (RRE) fragment of the HIV virus and was based on the competition between potential R N A binders and a fluorescent Rev peptide A biotinylated RRE fragment was immobilized on insoluble agarose beads that were covalently modified with streptavidin A ternary complex was formed

42 Z Xu, D S Pilch, A R Srinivasan, W K Olson, N E Geacintov, and K J Breslauer, Bioorg Med Chem 5, 1137 (1997)

43 G Luck, C Zimmer, and B C Baguley, Biochim Biophys Acta 782, 41 (1984)

44 S A Hofstadler, K A Sannes-Lowery, S T Crooke, D J Ecker, H Sasmor, S Manalili, and R H Griffey, Anal Chem 71, 3436 (1999)

45 R H Griffey, S A Hofstadler, K A Sannes-Lowery, D J Ecker, and S T Crooke, Proc Natl Acad Sci U.S.A 96, 10129 (1999)

46 K Hamasaki and R R Rando, Anal Biochem 261, 183 (1998)

47 N W Luedtke and Y Tor, Angew Chem 39, 1788 (2000)

26 BIOPHYSICAL APPROACHES [2]

on addition of fluorescein-labeled Rev peptide Ligand binding was indirectly detected by monitoring the quantity of fluorescent peptide either released into solution or remaining on the solid support as ligand bound to the RNA The assay offers a simple, rapid, and quantitative method that facilitates the discovery and characterization of RNA binders directed at a specific complex

Surface plasmon resonance (SPR) biosensors are another relatively new tech- nique for characterizing nucleic acid-small molecule interactions Although SPR biosensors have been commercially available for more than a decade, their appli- cation to nucleic acid-small molecule systems is just developing Only a handful

of SPR articles describing macromolecule-small molecule interactions have been published to date, and only a few of these focus on nucleic acid-small molecule systems.48 51 As with the methods described above, SPR offers a rapid and pow- erful tool for screening small molecule libraries 49'52 An advantage of SPR is that the method simultaneously can provide kinetic and equilibrium characterization

of the interactions of active compounds with macromolecules 53 Such informa- tion gives important insight for understanding whether inhibitory activity is gov- erned by the kinetic or equilibrium (or both) properties of the interaction For example, Hendrix e t al found that whereas two similar aminoglycoside antibi- otics have significantly different inhibitory potency, their binding constants are rather s i m i l a r 9 The dissociation rates for the two molecules, however, are sub- stantially different, accounting for the more than 100-fold difference in inhibitory activity

When one of the interacting species can be captured on a biosensor surface, the advantages of SPR over conventional interaction analyses can be numerous SPR monitors molecular interactions in real time 53 and is a significant improvement over optical methods for systems involving strong binding and/or low absorbance or fluorescence In addition, material requirements are minimal when compared with spectrophotometric techniques, generally requiring only picomole to nanomole quantities, while simultaneously providing accurate and reproducible data Al- though excellent articles on nucleic acid-small molecules have begun to appear,

to our knowledge, a comprehensive guide for investigating nucleic acid-small molecule systems through SPR has not been published to date For this reason and because of the commercial availability of high-sensitivity SPR instruments that are amenable for studying these systems, we have focused this review on how to study R N A - s m a l l molecule interactions with SPR biosensors

48 C.-H Wong, M Hendrix, W S Priestley, and W A Greenberg, Chem Biol 5, 397 (1998)

49 C.-H Wong, M Hendrix, D D Manning, C Rosenbohm, and W A Greenberg, J Am Chem Soc

120, 8319 (1998)

50 M Hendrix, E S Priestley, G E Joyce, and C.-H Wong, J Am Chem Soc 119, 3641 (1997)

51 L Wang, C Bailly, A Kumar, D Ding, M Bajic, D W Boykin, and W D Wilson, Proc Natl

52 P.-O Markgren, M Hamalained, and U H Danielson, Anal Biochem 265, 340 (1998)

53 Biacore, "BIACore BIAapplications Handbook." Biacore, Uppsala, Sweden, June 1994

[2] RNA INTERACTIONS 27

D e s c r i p t i o n o f S u r f a c e P l a s m o n R e s o n a n c e B i o s e n s o r s

SPR biosensors employ surface plasmon resonance to qualitatively and/or quantitatively describe molecular interactions Although several companies now offer SPR biosensors, 54 currently, instruments from BIAcore (Uppsala, Sweden) are the most widely used The BIAcore 2000 biosensor is used in our laboratory, and the experimental guidelines and technical details presented in this review are specific to this biosensor and the BIAcore 3000 biosensor However, the basic principles of using SPR to study molecular interactions are universal In biosensor experiments, the interaction between molecules in solution (the analyte in BIAcore literature) and molecules immobilized on a sensor chip surface (the ligand in BI- Acore literature) is monitored in real time ~3 Labeling is not required and there are many immobilization chemistries available depending on experimental designY Biosensor experiments involve immobilizing one of the interacting species on

a sensor chip surface either covalently or through affinity capture (such as biotin- streptavidin or antibody-antigen) to produce a biospecific surface 55 Binding of analyte to this biospecific surface is monitored through SPR in a thin gold film that is the base of all sensor chips The change in SPR response is directly related

to changes in the refractive index at the biospecific surface The refractive index

at the surface is directly related to the concentration of molecules at the surface Binding of analyte to the immobilized ligand, for example, causes a change in the refractive index at the surface, which in turn leads to a change in the reflected light angle at which SPR is observed (the SPR angle) Binding data are presented in the form of a sensorgram, which is a plot of the SPR angle, converted to resonance units (RU), versus time 53 (Fig 1) For the most commonly used sensor chips, a change of 1000 RU is equivalent to binding of about 1 ng of protein and 0.8 ng

of nucleic acid surface concentration per millimeter squared 53,56,57 The greater response per nanogram of nucleic acid is a result of their generally higher re- fractive index increment with respect to proteins Small molecules can have quite different refractive index increments (RIIs) than proteins and nucleic acids and the importance of this difference is discussed below

E x p e r i m e n t a l D e s i g n a n d P r o t o c o l s

E x p e r i m e n t a l D e s i g n ~ C o n s i d e r a t i o n s

There are several important factors to consider when designing an SPR exper- iment At the forefront of experimental design are the questions of which flow cell surface to use, which species to immobilize, what chemistry to use to immobilize it,

54 R L Rich and D G Myszka, Curl: Opin Biotechnol 11, 54 (2000)

55 Biacore, "BIACore BIAtechnology Handbook." Biacore, Uppsala, Sweden, June 1994

56 M Buckle, R M Williams, M Negroni, and H Buc, Proc Natl Acad Sci U.S.A 93, 889 (1996)

57 R J Fisher, M Fivash, J Casasfinet, J W Erickson, A Kondoh, S V Bladen, C Fisher, D K Watson, and T Papas, Protein Sci 3, 257 (1994)

28 BIOPHYSICAL APPROACHES [2]

3

n-

steady- association state dissociation

and how much of it to immobilize In an ideal experiment, both interacting species would be immobilized in reverse experiments to ensure that any data collected is not affected by the immobilization and other experimental factors If only one can

be immobilized then it is advantageous to immobilize the lower molecular weight species because the response signal on analyte binding is dependent on the molec- ular weight (the greater the molecular weight, the greater the signal) 53 In R N A (or

D N A ) - s m a l l molecule systems, it is often impractical or impossible to immobilize the small molecule, especially when the purpose o f the experiment is to screen a small molecule library Subtle changes in a small molecule can drastically affect binding, making perturbation-free immobilization and data interpretation difficult

In contrast, immobilizing R N A through a 5' or 3' tether is unlikely to affect spe- cific binding at a site in the interior o f the molecule Fortunately, we have found

[21 RNA INTERACTIONS 29

excellent signal-to-noise ratios (S/N) with small molecule binding, and where it has been possible to determine the equilibrium constant through both solution and SPR methods for small molecule-nucleic acid complexes, the constants are in ex- cellent agreement 58 Measuring binding of the low molecular weight compound does, however, require considerable attention to baseline stability Methods that

we have found to give the necessary S/N ratios are presented throughout

For immobilizing nucleic acids, the biotin-streptavidin affinity complex has been the immobilization method used in the vast majority of BIAcore SPR experi- ments,48-51.59-63 although other immobilization methods are appearing 64 In the biotin-streptavidin immobilization method, biotinylated RNA (see Preparation

of RNA, below) is immobilized on a sensor chip that has been derivatized with streptavidin (see Preparation of Sensor Chip, below) The rather large affinity constant for the biotin-streptavidin complex creates a quite stable binding surface over the time course of most experiment However, in our experience, streptavidin- nucleic acid sensor chips gradually lose binding activity through repeat use We have been able to immobilize additional nucleic acid onto the sensor chip to achieve the original binding level but the activity of the surface continues to decline Alternative immobilization strategies, including covalent attachment on acti- vated carboxymethylated dextran through both a primary amine and a thiol group tethered to the 5' end of the DNA, offer the potential for longer chip stability How- ever, in our hands, neither strategy yielded enough immobilized nucleic acid for small molecule studies It appears that the negative charge on the carboxymethyl dextran inhibits sufficient immobilization of the negative nucleic acid even at extremes of salt and pH in reasonable time periods We were, however, able to quantitatively immobilize a 5'-thiolated nucleic acid directly onto a gold surface, using the method outlined in Heine and Tarlov, 65 but the sensor chip lost sig- nificant activity rapidly, probably due to noncovalent adsorption of the nucleic acid on the surface The interaction between small molecule compounds and the gold sensor chip surface is a point that needs investigation as research on this

58 S Mazur, F A Tanious, D Ding, A Kumar, D W Boykin, I J Simpson, S Neidle, and W D Wilson, J Mol Biol 300, 321 (2000)

59 p j Bates, H S Dosanjh, S Kumar, T C Jenkins, C A Laughton, and S Neidle, Nucleic Acids

6o G Bischoff, R Bischoff, E Birch-Hirschfeld, U Gromann, S Lindau, W.-V Meister, S Bambirra,

C Bohley, and S Hoffman, J Biomol Struct Dynam 16, 187 (1998)

61 B Persson, K Stenhag, E Nilsson, A Larsson, M Uhlen, and E-A Nygren, Anal Biochem 246,

34 (1997)

62 C Rutigliano, N Bianchi, M Tomassetti, L Pippo, C Mischiati, G Feriotto, and R Gambari, Int

63 T M Nair, D G Myszka, and D R Davis, Nucleic Acids Res 28, 1935 (2000)

64 C I Webster, M A Cooper, L C Packman, D H Williams, and J C Gray, Nucleic Acids Res 28,

1618 (2000~[

65 T M Heme and M J Tarlov, 119, 8916 (1997)

30 BIOPHYSICAL APPROACHES [2] type of immobilization proceeds We are currently working on a new method for covalently immobilizing nucleic acid onto a modified sensor chip that will allow for regeneration of the original surface after the chip has lost activity or when

it is desirable to immobilize a different nucleic acid sequence In the meantime, however, immobilization through the biotin-streptavidin affinity complex is the recommended strategy to efficiently and reproducibly immobilize nucleic acids for SPR experiments

The next question that must be addressed in sensor chip surface preparation is how much nucleic acid to immobilize For kinetic experiments it is usually best

to immobilize the smallest amount of nucleic acid (or any ligand) possible, while maintaining the desired signal-to-noise ratio, in order to minimize mass transport effects Mass transport of analyte to the surface will alter kinetic data when the rate of mass transport is slower than or on the same time scale as the interaction kinetics 53 Because a high concentration of surface binding sites depletes the ana- lyte at the surface, the more nucleic acid immobilized the greater the contribution

from mass transport Crouch et al suggest an immobilization level less than 100 RU

for kinetic analysis of RNA-protein interactions 66 When the analyte is a small molecule, however, it becomes necessary to increase the ligand surface density because the instrument response from small molecule binding will be much less than the response from protein binding We typically immobilize about 250 RU

of hairpin nucleic acid ('v50 bases in length) for small molecule assays In our experience, the increase in surface density does not affect kinetic data because small molecules diffuse more rapidly than macromolecules and are not as limited

by mass transport as macromolecules Our small molecule nucleic acid experi- ments to date have not been limited by mass transport It is important, however,

to account for mass transport when it is present, to correctly analyze kinetic data See Refs 67-69 for a detailed description of processing kinetic data influenced by mass transport and additional suggestions for minimizing its effects Because mass transport does not affect steady state data, it is advantageous for signal-to-noise optimization to immobilize a higher level of nucleic acid for equilibrium analyses, especially when working with small molecules

Experimental Protocols

Preparation o f Sensor Chip For immobilizing biotin-RNA (or any biotin- nucleic acid) (see Preparation of RNA, below) on a sensor chip, the sensor chip

66 R J Crouch, M Wakasa, and M Haruki, in "RNA-Protein Interaction Protocols" (S Haynes, ed.),

pp 143-160 Totowa, New Jersey, 1999

67 B Goldstein, D Coombs, X He, A R Pineda, and C Wofsy, J Mol Recognit 12, 293 (1999)

68 D G Myszka, Curt: Opin Bioteehnol 8, 50 (1997)

69 T A Morton and D G Myszka, Methods Enzymol 295, 268 (1998)

[2] RNA INTERACTIONS 31

must be modified to a streptavidin surface BIAcore offers high-quality premade streptavidin sensor chips (SA sensor chip) that are ready for immediate use However, it is possible and in some cases worthwhile to prepare streptavidin sensor chips (Refs 50 and 53, and unpublished results, 2000) using standard (CM5) dextran surfaces or pioneer chips (chips that are under development but are available from BIAcore) with features such as a low-density carboxyl sur- face The low-density carboxyl surfaces uses dextran but has less negative charge and so may be advantageous when investigating the interactions between RNA and molecules with high positive charge We use the procedure outlined below for immobilizing streptavidin on CM5 sensor chips The procedure is straight- forward, and in our experience, generates streptavidin chips of similar quality

to the premade chips available from BIAcore but with lower cost and additional flexibility

REQUIRED MATERIALS AND SOLUTIONS The following is adapted from Ref 55

CM5 or Pioneer sensor chip

HEPES-buffered saline (HBS) buffer: 10 mM HEPES (pH 7.0), 150 mM NaC1, 3 mM EDTA, 0.005% (v/v) polysorbate 20 (running buffer) N-Hydroxysuccinimide (NHS): 100 mM in water

N-Ethyl-N'-(dimethylaminopropyl)carbodiimide (EDC): 400 mM in water Acetate buffer (pH ~ 5): 10 mM (immobilization buffer)

Streptavidin, 200-400 #g/ml [Pierce (Rockford, IL); Molecular Probes (Engine, OR); or Sigma (St Louis, MO)] in immobilization buffer Ethanolamine hydrochloride: 1 M in water (pH 8.5) (deactivation solution) Sodium dodecyl sulfate (SDS): 0.05% (w/v)

4 For efficient immobilization of streptavidin (or any protein), it is necessary

to use an immobilization buffer with a pH below the pl of the protein If the macromolecule has a negative charge during immobilization, the amount of

32 BIOPHYSICAL APPROACHES [2]

material that can be linked to the surface will be limited Different sources and batches of streptavidin can vary in pl (pl 5-6), resulting in the necessity to opti- mize the immobilization procedure for each new batch (see Preconcentration of Streptavidin, below) BIAcore reports the use of 200 #g/ml in 10 mM acetate, pH 4.5, for streptavidin from Sigma, and 400 #g/ml in 10 mM acetate, pH 5.0, for streptavidin from Molecular Probes We currently use streptavidin at 200 #g/ml

in 10 mM acetate, pH 4.5, for streptavidin from Pierce

5 BIAcore Software requires specification of volumes rather than times when performing injections Therefore, with a flow rate of 5 #l/min, a 2-min injection must be specified as 10 #1

PRECONCENTRAT1ON OF STREPTAVID1N Efficient immobilization of proteins through amine coupling on a carboxymethylated dextran surface involves electro- static attraction between the negative charges on the surface matrix (carboxymethyl dextran) and positive charges on protein when it is below its pl (termed precon- centration) Ideally, the solution should have low ionic strength (50 mM or less) to maximize preconcentration 55 It is important to note that if the pH of the buffer is too low (pH < 3-3.5), the dextran matrix becomes protonated and preconcentration

is less efficient 55 In addition, amine coupling requires uncharged amino groups and is therefore favored by higher pH Clearly, the optimum pH is a compromise between efficient preconcentration and efficient coupling

To optimize the immobilization of streptavidin through amine coupling, injec- tions of streptavidin in buffers differing in pH should be done as outlined below Three buffers at pH 4.5, 5.0, and 5.5 are generally sufficient We currently use

10 mM acetate buffer although any buffer lacking primary amine groups should suffice (note that a Tris buffer is not suitable for amine coupling)

PRECONCENTRATION PROTOCOL The following is adapted from Ref 55

1 Dock a CM5 or Pioneer sensor chip into the instrument

2 Prime the instrument three times with HBS running buffer

3 Start a sensorgram at 5 #l/min

4 Perform a 2-min "QUICK INJECT" of each streptavidin-pH combination from'high to low pH Begin with a streptavidin concentration of 200 #g/ml

5 Perform a l-min "QUICK INJECT" with "Extra Clean" consisting of 0.05% (w/v) SDS to remove any residual streptavidin from the fluidics

6 Perform a 1-min "QUICK INJECT" with "Extra Clean" consisting of HBS buffer to remove any residual SDS from the fluidics

7 Using the crosshair, note the preconcentration level in RU

BIAcore suggests that a streptavidin immobilization level of ~5000 RU is suitable for most applications 55 We currently immobilize 2500-3500 RU of strep- tavidin We have found this immobilization level suitable for our applications

[2] RNA INTERACTIONS 33

Typically, 60-80% of the protein preconcentration level is immobilized Therefore, aim for an electrostatic preconcentration level 150% of the desired immobilization yield 55 For example, to immobilize 3000 RU of streptavidin, use conditions that favor an electrostatic preconcentration level of ~4500 RU Using the data from the preconcentration experiment above, determine which pH is best for immobilizing the desired amount of streptavidin, keeping in mind that a lower pH may also reduce coupling efficiency If none of the solutions produces the desired response,

it may be necessary to increase the concentration of streptavidin [see Important Notes (4) above]

IMMOBILIZATION OF STREPTAVIDIN After choosing the appropriate pH to im- mobilize the desired amount of streptavidin, follow the procedure outlined below for immobilizing streptavidin

1 Run a sensorgram at 5 #l/min

2 With NHS in one vial and EDC in other, use the DILUTE command to make

a 1 : 1 mixture of N H S - E D C (note that the NHS-EDC solution must be prepared fresh immediately prior to use)

3 Inject N H S - E D C for 7 min (35 #1) to activate the carboxymethyl surface

to reactive esters Higher or lower activation may be desired for preparation of nonstandard surfaces

4 Inject streptavidin in the appropriate buffer for 7 min (35 #1) to immobilize streptavidin

5 Inject ethanolamine hydrochloride for 7 min (35 /~1) to deactivate any re- maining reactive esters

If immobilization fails after achieving appropriate levels of preconcentration then

it may be necessary to increase the time of activation (the volume of NHS-EDS injected) or the streptavidin concentration if the pH is relatively low Alternatively, EDC is quite labile and if the EDC solution is not freshly made or has not been kept properly frozen it could contain extensive inactive material We keep EDC as

a dry powder in a desiceant bottle at 4 ° and make fresh solutions as needed rather than aliquot all the EDC as suggested in the BIAcore amine coupling kit We have found that streptavidin immobilization is straightforward as long as fresh solutions are used

Preparation o f RNA: Derivatization to Incorporate Biotin The biotin-strepta- vidin capture method requires that the RNA be modified with biotin The ideal level

of biotinylation for BIAcore nucleic acid applications is one biotin per nucleic acid Small RNAs can be synthetically prepared with biotin on either the 5' or 3' terminus and are commercially available Larger RNAs must be prepared by

in vitro transcription An excellent source for the synthesis of RNA by in vitro

transcription is Wyatt et al 7° The use of guanosine 5'-monophosphorothioate (GMPS) to prime a transcription reaction is a convenient way to site-specifically

34 BIOPHYSICAL APPROACHES [21

incorporate a single 5' terminal thiol group into RNA 50'70-72 The thiol can then

be modified to biotin with biotin-maleimide or biotin-iodoacetamide conjugates (Molecular Probes) A concise procedure for synthesizing GMPS from guanosine can be found in Burgin and Pace 72 Alternatively, larger RNAs can be transcribed

so as to incorporate 4-thiouridine internally, 73 and then modified with biotin as described above Drawbacks to this method are that (1) additional experiments must be performed to ensure the internal biotin moiety does not disrupt native structure or function, and (2) it is difficult to achieve a low level of biotinylation unless only a couple of uridines are present in the RNA RNA can also be site- specifically modified to contain a thiol group on the 5' terminus through a kinase reaction 7t Briefly, RNA is prepared by in vitro transcription, using GMP or GTP

as the priming nucleotide instead of GMPS, or obtained commercially unmodified The Y-phosphate(s) are then removed with phosphatase followed by transfer of the thiophosphate of ATPvS to the RNA by T4 polynucleotide kinase

Other methods to incorporate biotin into RNA include oxidation of the 3' termi- nal cis-diol to dialdehyde by periodate followed by reaction with biotin-hydrazine conjugates (Molecular Probes) Alternatively, the Y-phosphate of R N A (or DNA) can be converted to a 5'-phosphorimidazolide with imidazole and a water-soluble carbodiimide, and subsequently reacted with biotin-amine derivatives (Molecular Probes) Detailed protocols as well as limitations and necessary considerations for all of the above-described methods can be found in Qin and Pyle 71 (that article focuses on modification of RNA with fluorophores, but biotin derivatives can be used instead) It should be noted that regardless of the method used to modify RNA, excess biotin must be efficiently removed after modification to avoid competition with biotin-RNA for streptavidin binding sites and to enable reproducible levels

of immobilization Gel filtration on a desalting column and dialysis are simple methods to separate excess biotin from biotin-RNA 55

Immobilization o f RNA Once derivatized with biotin, the RNA is ready to

be immobilized on a streptavidin-coated sensor chip Generally, immobilization methods that rely on rapid kinetics and high-affinity binding of the ligand to the surface, such as the streptavidin-biotin interaction, do not require preconcentration experiments to assist in immobilization 55 To date, we have worked with relatively short oligonucleotide hairpins (< 50 bases) For this size nucleic acid, we routinely use HBS as the immobilization and running buffer Larger RNAs may require either higher salt to minimize electrostatic repulsion or a pH lower than the pl of streptavidin to favor electrostatic interactions with the negatively charged RNA,

We routinely use a concentration of ~ 2 5 nM oligonucleotide (1 : 1 biotin : RNA

7o j R Wyatt, M Chastain, and J D Puglisi, Biotechniques 11, 764 (1991)

71 p Z Qin and A M Pyle, Methods 18, 60 (1999)

72 A B Burgin and N R Pace, EMBO ,I 9, 4111 (1990)

73 j E Milligan and O C Uhlenbeck, Methods Enzymol, 180, 51 (1989)

[21 RNA INTERACTIONS 35

ratio) in HBS buffer when immobilizing nucleic acids less than 50 bases in length

It may be necessary to increase the concentration when using larger nucleic acids A concentration that is too high, however, will make control over the amount of ligand immobilized difficult In addition, too high a concentration may cause the outer dextran layer to bind RNA rapidly, potentially blocking inner layers and limiting the amount of RNA that can be immobilized As discussed in the introduction, we typically immobilize about 250 RU of nucleic acid for small molecule studies REQUIRED MATERIALS AND SOLUTIONS

Streptavidin-coated sensor chip (SA chip or prepared as outlined above) HBS buffer: 10 mM HEPES (pH 7.0), 150 mM NaC1, 3 mM EDTA, 0.005% (v/v) polysorbate 20 (running buffer)

Activation buffer: 1 M NaC1, 50 mM NaOH

Biotin-modified RNA: ~25 nM in appropriate running buffer

Biotin (optional)

IMPORTANT NOTES

1 RNA is extremely sensitive to nucleases and base hydrolysis Many re- searchers strictly use diethyl pyrocarbonate (DEPC)-treated water when making solutions for RNA research We have found water from commercial systems such

as NANO (Barnstead, Inc., Dubuque, Iowa) or Milli-Q (Pharmacia, Peapack, NJ)

of sufficient quality for RNA work

2 Because of the sensitivity of RNA, it is critical to maintain a "nuclease- free" working environment It is always necessary to run the BIAcore Desorb method just prior to working with RNA, especially if there are multiple users on the instrument

In addition, any external instrument components such as the needle and the tubing placed in the running buffer should be routinely treated with RNase Zap (Ambion, Austin, TX) or another chemical to inactivate/remove any ribonucleases

3 Researchers at the National Institutes of Health (NIH, Bethesda, MD) rou- tinely cleanse the fluidics of the BIAcore instrument by injecting 20 #1 of RNase Zap solution through each flow cell at 20 #l/min followed by ten 20-#1 injections

of DEPC-water (Ref 66 and Inna Gorshkova, personal communication, 2000) It may be advisable to run this procedure when there are multiple instrument users,

if a ribonuclease has ever been used in an experiment, or if problems with RNA degradation during an experiment are encountered

IMMOBILIZATION PROTOCOL The following is adapted from Ref 55

1 Dock the streptavidin-coated sensor chip into the instrument

2 Prime the instrument three times with HBS buffer (note that priming does not need to be done if the streptavidin chip was just made as outlined above,

36 BIOPHYSICAL APPROACHES [2] has not been removed from the instrument and HBS has been the running buffer)

3 Start a sensorgram with a 20-/zl/min flow rate

4 Inject activation buffer for 1 min (20 ~1) three times to remove any unbound streptavidin from the sensor chip

5 Allow buffer to flow for at least 5 min before immobilizing the RNA Note that NaOH will facilitate hydrolysis of the RNA, so it is critical that buffer flows long enough to remove any trace of NaOH

6 Select the desired flow cell on which to immobilize the RNA and start a new sensorgram for only that flow cell Take care not to immobilize R N A on the flow cell chosen as the control flow cell Generally flow cell 1 ("fcl") is used as

a control and RNA is not immobilized on it It is often desirable to immobilize

a different R N A (perhaps wild-type and mutants) on different flow cells In this situation it is necessary to immobilize ligands one at a time Alternatively, it may

be desirable, depending on specific needs, to immobilize different amounts of the same ligand on each flow cell

7 Using M A N U A L INJECT with a flow rate of 2 #l/rain, load the loop with

~ 1 0 0 #1 of R N A and inject over the desired flow cell(s) Using the crosshair, track the number of resonance units immobilized and stop the injection after the desired level is reached As a rule of thumb, do not inject more than 80-90% of the loaded volume, to minimize sample dispersion effects at the end of the injected sample

If necessary, reload the loop and inject more RNA It may be necessary to alter immobilization conditions such as ionic strength, pH, and ligand concentration to obtain the desired immobilization level

8 At the end of the injection and after the baseline has stabilized, use the crosshair to determine the resonance units of RNA immobilized and record this amount The amount of RNA immobilized is required to determine the theoretical number of small molecule binding sites for the flow cell

9 Repeat steps 7 and 8 for each flow celI-RNA combination according to experimental design

i0 Some researchers block the remaining biotin binding sites and the biotin binding sites on the streptavidin in the control flow cell with free biotin This will depend on any nonspecific interactions between components in the flow solution with streptavidin on the sensor chip surface

STORAGE OF RNA SENSOR CHIP Researchers at the NIH store oligonucleotide sensor chips in Tris-buffered saline (TBS) buffer to maintain binding activity (Inna Gorshkova, personal communication, 2000) To do so, remove the sensor chip from the white cartridge, being careful not to touch the flow cells Immerse and store the chip in RNase-free TBS buffer To subsequently use the sensor chip, remove it from the TBS Shake the sensor chip a couple of times to remove the excess buffer, and then leave the chip to air dry in a sterile environment until it is completely