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Jurriaan Huskens, PhD (1968) is full professor of "Molecular Nanofabrication" at the University of Twente, Netherlands. Present research interests encompass: supramolecular chemistry at interfaces, supramolecular materials, multivalency, nanofabrication, and solar fuels.
Leonard J. Prins, PhD is a professor in Organic Chemistry at the University of Padova, Italy. His current research interests include network reactivity in complex chemical systems and the origin of cooperativity in multivalent catalysts.
Rainer Haag, PhD joined the Freie Universität Berlin as full Professor of Organic and Macromolecular Chemistry in 2004. Currently he serves on the Editorial Board of the Angewandte Chemistry and is the spokesperson of the collaborative research center 765 on "multivalency."
Bart Jan Ravoo, PhD (1970) is full professor at the Westfälische Wilhelms-Universität Münster, Germany, where he is in charge of the "Synthesis of Nanoscale Systems" group. Since 2016 he is co-director of the Center for Soft Nanoscience (SoN). His main research interest are soft materials made by self-assembly, functional nanoparticles, and self-assembled monolayers.
List of Contributors xi
Foreword xv
Preface xvii
Part I General Introduction to Multivalent Interactions 1
1 Additivity of Energy Contributions in Multivalent Complexes 3 Hans?-Jorg Schneider
1.1 Introduction 3
1.2 Additivity of Single Interactions - Examples 3
1.3 Limitations of Additivity 7
1.3.1 Free Energy Values ¿G Instead of Enthalpic and Entropic Values ¿H, T¿S 7
1.3.2 Mismatch as Limitation of Additivity 9
1.3.3 Medium Effects as Limiting Factor 12
1.3.4 Strain and Induced Fit 12
1.4 Cooperativity 13
1.5 Allostery 14
1.6 Conclusions 17
References 18
2 Models and Methods in Multivalent Systems 23 Jurriaan Huskens
2.1 Introduction 23
2.1.1 General Introduction 23
2.1.2 Multivalent versus Cooperative Interactions 24
2.2 Numerical Data Analysis 25
2.2.1 Model Simulations Using a Spreadsheet Approach 26
2.2.2 Setting Up and Assessing Titrations 30
2.2.3 Using Spreadsheet Simulations to Fit Experimental Data to a Model 36
2.3 Models for Multivalent Systems 41
2.3.1 The Simplest Multivalent System: A 1:1 Complex with Two Interaction Sites 41
2.3.2 Multivalent Binding at Surfaces 46
2.4 Special Multivalent Systems 53
2.4.1 Increasing the Valency of Interfacial Assemblies: Dendrimers, Oligomers, and Polymers 53
2.4.2 Heterotropic Interactions 58
2.4.3 Kinetics and Dynamics 63
2.5 Conclusions 68
Acknowledgments 68
References 68
3 Design Principles for Super Selectivity using Multivalent Interactions 75 Tine Curk, Jure Dobnikar, and Daan Frenkel
3.1 Introduction 75
3.1.1 Background: Ultra?-sensitive Response 75
3.2 Super Selectivity: An Emergent Property of Multivalency 78
3.3 Multivalent Polymer Adsorption 84
3.4 Which Systems are Super Selective? 86
3.4.1 Rigid Geometry Interactions 86
3.4.2 Disordered Multivalency 87
3.5 Design Principles for Super?-Selective Targeting 90
3.6 Summary: It is interesting, but is it useful? 93
Appendix 3.A: What Is Effective Molarity? 95
Acknowledgements 98
References 98
4 Multivalency in Biosystems 103 Jens Dernedde
4.1 Introduction 103
4.2 Cell-Cell Adhesion 104
4.2.1 Homotypic Interactions, Cadherins Keep Cells Together 105
4.2.2 Selectins, Heterotypic Cell Adhesion to Fight Infections 106
4.2.3 Bacterial Adhesion by FimH 108
4.3 Phase Transition, Multivalent Intracellular Assemblies 109
4.4 Multivalency in the Fluid Phase, Pathogen Opsonization 111
4.5 Conclusion 113
Acknowledgment 113
References 114
Part II Multivalent Systems in Chemistry 121
5 Multivalency in Cyclodextrin/Polymer Systems 123 Akihito Hashidzume and Akira Harada
5.1 Introduction 123
5.2 General Perspectives of Multivalency in Cyclodextrin/Polymer Systems 125
5.3 Typical Examples of Multivalency in Cyclodextrin/Polymer Systems 126
5.3.1 Formation of Polymer Aggregates from Cyclodextrin?-Polymers and Guest?-Polymers 126
5.3.2 Selectivity of Interaction Enhanced by Multivalency 127
5.3.3 Self?-Healable Hydrogels Based on Multivalency 134
5.4 Summary and Outlook 136
Acknowledgments 136
References 138
6 Cucurbit[n-uril?-Mediated Multiple Interactions 143 Zehuan Huang and Xi Zhang
6.1 Introduction to Cucurbit[n-uril Chemistry 143
6.2 Heteroternary Complexes 143
6.3 Homoternary Complexes 146
6.4 Conclusions 150
References 150
7 Multivalency as a Design Criterion in Catalyst Development 153 Paolo Scrimin, Maria A. Cardona, Carlos M. Leon Prieto, and Leonard J. Prins
7.1 Introduction 153
7.2 Formation of Enzyme?-Like Catalytic Pockets 154
7.3 Cooperativity Between Functional Groups 157
7.4 Mechanistic Effects 161
7.5 The Dendritic Effect in Multivalent Nanozymes 164
7.5.1 Peptide?-Based Dendrimers for the Cleavage of Phosphodiesters 166
7.5.2 Catalytic 3D SAMs on Au NPs 168
7.6 Multivalent Catalysts and Multivalent Substrates 170
7.7 Conclusions 172
Acknowledgements 174
References 174
8 Multivalent Molecular Recognition on the Surface of Bilayer Vesicles 177 Jens Voskuhl, Ulrike Kauscher, and Bart Jan Ravoo
8.1 Introduction 177
8.2 Molecular Recognition of Vesicles 179
8.2.1 Metal Coordination 180
8.2.2 Light Responsive Interactions 184
8.2.3 Hydrogen Bonding and Electrostatic Interactions 185
8.3 Biomimetic Vesicles 188
8.3.1 Vesicles as Multivalent Platforms 188
8.3.2 Membrane Fusion 193
8.4 Vesicle?-based Supramolecular Materials 196
8.4.1 Hydrogels 196
8.4.2 Immobilization of Vesicles 198
8.4.3 Nanoparticles and Nanocontainers 198
8.5 Conclusion 201
Acknowledgment 201
References 201
Part III Multivalent Systems in Biology 205
9 Blocking Pathogens by Multivalent Inhibitors 207 Sumati Bhatia, Benjamin Ziem, and Rainer Haag
9.1 Introduction 207
9.2 Design of Multivalent Ligand Architectures 209
9.3 Multivalent Carbohydrate Ligands 212
9.4 Scaffold Architecture 215
9.4.1 Linear and Dendritic Scaffolds 215
9.4.2 Multivalent Gold Nanoparticles 218
9.4.3 2D Platforms 220
9.5 Nano?-and Microgels for Pathogen Inhibition 222
9.6 Conclusion 223
Acknowledgments 224
References 224
10 Multivalent Protein Recognition Using Synthetic Receptors 229 Akash Gupta, Moumita Ray, and Vincent M. Rotello
10.1 Introduction 229
10.2 Structural Properties of Protein Surfaces 229
10.2.1 Protein-Protein Interfacial Areas 229
10.2.2 Chemical Nature of the Protein-Protein Interface 230
10.2.3 "Hot Spots" 230
10.2.4 O?-Ring Structure 232
10.3 Synthetic Receptors for Protein Surface Recognition 232
10.3.1 Porphyrin Scaffolds for Protein Surface Recognition 232
10.3.2 Protein Surface Recognition Using Molecular Tweezers 238
10.3.3 Calixarene Scaffolds for Protein Surface Recognition 240
10.3.4 Recognition of Protein Surfaces Using Nanoparticles 243
10.3.4.1 Nanoparticles as Protein Mimics 244
10.3.4.2 Regulating the Structure and Function of Proteins Using Nanoparticles 246
10.3.4.3 Nanoparticle?-based Protein Sensors 250
10.4 Future Perspective and Challenges 254
Acknowledgment 257
References 257
11 Multivalent Calixarenes for the Targeting of Biomacromolecules 263 Francesco Sansone and Alessandro Casnati
11.1 Introduction 263
11.2 Binding to Proteins and Enzymes 266
11.3 Recognition of Carbohydrate Binding Proteins (Lectins) 273
11.4 Binding Polyphosphates, Oligonucleotides and Nucleic Acids 279
11.5 Conclusions 284
Acknowledgements 285
References 285
12 Cucurbit[n]uril Assemblies for Biomolecular Applications 291 Emanuela Cavatorta, Luc Brunsveld, Jurriaan Huskens, and Pascal Jonkheijm
12.1 Introduction 291
12.2 Molecular Recognition Properties of CB[n- 293
12.2.1 Interactions with the Carbonyl Portals of CB[n- 293
12.2.2 Release of High Energy Water Molecules from the CB[n- Cavity 295
12.2.3 Enthalpy?-driven Hydrophobic Effect for CB[n- 295
12.2.4 Enthalpy?-driven Hydrophobic Effect for CB[8- Heteroternary Complexes 297
12.3 Control Over the Binding Affinity with CB[n- 299
12.4 CB[n] Recognition of Amino Acids, Peptides, and Proteins 301
12.5 CB[n] for Bioanalytical and Biomedical Applications 305
12.5.1 CB[n]-mediated Assembly of Bioactive Polymers and Hydrogels 305
12.5.2 CB[n]-mediated Assembly of Bioactive Nanoparticles 307
12.5.3 CB[n]?-mediated Assembly on Bioactive Surfaces 313
12.6 Conclusions and Outlook 317
Acknowledgment 319
References 319
13 Multivalent Lectin-Glycan Interactions in the Immune System 325 Joao T. Monteiro and Bernd Lepenies
13.1 Introduction 325
13.2 Targeting Innate Immunity to Shape Adaptive Immunity 327
13.3 C?-type Lectin Receptors 328
13.3.1 Multivalent Glycoconjugates Targeting DC?-SIGN 331
13.3.2 Multivalent Glycoconjugates Targeting Other CLRs 331
13.4 Galectins 332
13.5 Siglecs 334
13.6 Conclusions 335
Acknowledgment 335
References 335
14 Blocking Disease Linked Lectins with Multivalent Carbohydrates 345 Marjon Stel and Roland J. Pieters
14.1 Introduction 345
14.2 Haemagglutinin 347
14.3 LecA 349
14.4 LecB 354
14.5 Galectins 358
14.6 Concanavalin A 362
14.7 Cholera Toxin 366
14.8 Propeller Lectins 367
14.9 Conclusion 371
Acknowledgements 371
References 371
Index 381
Hans-Jörg Schneider
FR Organische Chemie, Universität des Saarlandes, 66123, Saarbrücken, Germany
Additivity of individual binding contributions is the very basis of multivalency. In classical coordination chemistry such simultaneous actions are described as the chelate effect. They offer almost unlimited ways to enhance the affinity [1,2,3,4,5,6], and therefore within certain limitations also the selectivity [7] of synthetic and natural complexes. Although additivity is often implied in experimental and theoretical approaches it is subject to many limitations which will be also discussed in the present chapter.
If only one kind of interaction is present in a complex one can expect a simple linear correlation between the number n of the individual interaction free energies ??Gi and the total ?Gt (Equation 1.1), as illustrated in Figure 1.1 for salt bridges [8]. Even though the organic ion pair complexes are based on cations and anions of very different size and polarizability one observes essentially additive salt bridges; the slope of the correlation indicates an average of ??G?=?(5?±?1) kJ/mol per salt bridge. The value of (5?±?1) kJ/mol is observed in usual buffer solution, but varies as expected from the Debye-Hückel equation with the ionic strength of the solution [9]. Scheme 1.1 shows a corresponding value of K?~?10?M-1 per salt bridge for typical complexes where the affinity depends as expected on the degree of protonation [7].
Figure 1.1 Additive ion pair contributions in a variety of complexes with a number nC of salt bridges. From slope: average (5?±?1) kJ/mol per salt bridge. A,B and C,C' - complexes of a tetraphenolate cyclophane (4-) with Me4N+ and an azoniacyclophane (4+) with mono- and dianionic naphthalene derivatives; D - anionic (sulfonate or carboxylate) with cationic (ammonio) triphenylmethane derivatives; E - organic dianions with organic dications; F - cationic azamacrocycle (6+ charges) with aliphatic dicarboxylates; G - cationic azacrowns with adenosine mono-, di- and triphosphates.
Source: Ref. [8]. Reproduced with permission of John Wiley and Sons.
Scheme 1.1 Complexation log K values of anions 1-5 with a macrocyclic amine as function of the degree of protonation of the amine; and ion pairing with some representative complexes; log K values in water; n is the estimated number of salt bridges.
The additivity depicted in Figure 1.1 and Scheme 1.1 for salt bridges is in line with the Bjerrum equation, which describes ion pair association as a function of the ion charges zA and zB; Figure 1.2 shows for over 200 ion pairs a linear dependence of log K vs. zAzB [3]. For inorganic salts one finds similar ??G values of 5-6?kJ/mol per salt bridge and a similar dependence on charges [10]. At zero ionic strength the stability decreases in the order Ca2+?>?Mg2+ >?>?Li+?>?Na+?>?K+ and can be described by Equation 1.2 [11]. Additivity is observed although ion pairing in water is determined entirely by entropic contributions[11], unless other contributions dominate [12].
Figure 1.2 Ion pair association constants at zero ionic strength as a function of charge product, calculated for 203 ion pairs.
If there is more than one kind of interaction, Equation 1.3 applies. Often however, only one of the contributions is the same, like salt bridges in complexes of nucleotides with a positively charged host (Scheme 1.2) [13]. Additivity is then observed by the constant stability difference of 2?×???G?~?10?kJ/mol between complexes with charged nucleotides and neutral nucleosides. The 10?kJ/mol reflects the presence of two salt bridges between the phosphate dianion and the host ammonium center, which agrees with structural analyses by NMR spectroscopy.
Scheme 1.2 Complexation free energies ?G of nucleotides and nucleosides with the cyclophane CP66.
The complexes shown in Scheme 1.2 exhibit constant single ??GA values only for the salt bridges, whereas the second contribution ??GB varies as a function of the different nucleobases. Figure 1.3 illustrates a case where both ??GA and ??GB remain constant, the latter reflecting cation-p interactions. In principle one could use Equation 1.3 to derive both ??GA and ??GB, but more reliable values are obtained if for one interaction a ??G value is used which is known from independent analyses, such as ??GA?=?5?kJ/mol for each salt bridge (see above). Then one observes a rather linear correlation with the number of phenyl units which shows a contribution of ??GB?~?1.5?kJ/mol for the single +N-p interaction [14].
Figure 1.3 Ion pairs exhibiting both salt bridges and cation-p interactions; if ??GA?=?5?kJ/mol for each salt bridge are subtracted from ?Gt of each complex. Outliers (open circles) are due to conformational mismatch.
Source: Ref. [14]. Reproduced with permission of American Chemical Society.
The effect of nitro substituents on dispersive interactions is another example of additive energy contributions (Figure 1.4) [15,16]. Additivity with respect to substituent effects is observed in Hammett-type linear free energy relationship correlations; Figure 1.5 shows an example for hydrogen bonds with C─H bonds as donor and with hexamethylphosphoramide as acceptor [17].
Figure 1.4 Additive ??GX increments in complexes of porphyrins bearing cationic or anionic substituents R in meso position (TPyP or TPS) in water, after deduction of 5?kJ/mol for ion pair contribution where applicable. ??GX increments in TPyP complexes for nitro substituents as an example (deviation for ortho-dinitro due to steric hindrance); correlation between measured complexation energies ?Gexp and ?Gcalc calculated on the basis of experimentally determined averaged single contributions ?GS. Filled circles, complexes with TPyP; open circles, complexes with TPS.
Source: Ref. [15]. Reproduced with permission of John Wiley and Sons.
Figure 1.5 Hammett-type correlation of equilibria of hydrogen bonds with hexamethylphosphoramide as acceptor and para-substituted tetrafluorobenzenes or phenylacetonitriles as donor; log K versus Hammett substituent constants.
Source: Ref. [17]. Reproduced with permission of John Wiley and Sons.
The examples shown above as well as most others in the literature rely on free energy values ?G, although consideration of the corresponding ?H and T?S parameters could shed more light on the underlying binding mechanisms. As pointed out earlier by Jencks, the empirical use of ?G "avoids the difficult or insoluble problem of interpreting observed ?H and T?S values for aqueous solution" [18]. Furthermore, according to Jencks, there is often an additional "connection Gibbs energy, ?GS" (Equation 1.4) which he ascribed largely to changes in translational and rotational entropy. These connection ?GS can be either negative or positive and will be discussed as major liming factors for additivity below in the context of cooperativity and allostery.
The success of using free energy values instead of enthalpic and entropic values is in an essential part due to entropy-enthalpy compensation which has empirically been found to hold with many complexations, although it is theoretically not well-founded [19,20,21]. Another factor is that in typical supramolecular complexes the loss of translatory freedom is already paid by a single association step. The loss of rotational freedom upon complex formation has been experimentally [9] found to be smaller than theoretically expected (see below).
Entropy contributions pose particular problems, not only for the precise experimental determination, which in the past often relied on the temperature dependence of equilibrium constants (the Van 'tHoff method) instead...
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