Reviews in Computational Chemistry Volume 18

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Introduction 45 Major Contributions to Protein–Ligand Interactions The selective binding of a low molecular weight ligand to a specific protein is determined by the structural and energetic recognition of those two molecules. For ligands of pharmaceutical interest, the protein–ligand interactions are usually noncovalent in nature. The binding affinity can be determined from the experimentally measured binding constant Ki G ¼ RT ln Ki ¼ H T S ½1Š The experimentally determined binding constants Ki are typically in the range of 10 2 to 10 12 mol/L, corresponding to a Gibbs free energy of binding G between 10 and 70 kJ/mol in aqueous solution. 6,24 There exists a growing body of experimental data on 3D structures of protein–ligand complexes and binding affinities. 25 These data indicate that several features can be found in almost all complexes of tightly bound ligands. These features include 1. A high steric complementarity between the protein and the ligand. This observation is consistent with the long established lock-and-key paradigm. 2. A high complementarity of the surface properties. Lipophilic parts of the ligands are most frequently found to be in contact with lipophilic parts of the protein. Polar groups are usually paired with suitable polar protein groups to form hydrogen bonds or ionic interactions. 3. The ligand usually adopts an energetically favorable conformation. Generally speaking, direct interactions between the protein and the ligand are essential for binding. The most important types of direct interactions are depicted in Figure 1. Structural data on unfavorable protein–ligand interactions are sparse. The scarcity of such complexes is due, in part, to the fact that structures of weakly binding ligands are more difficult to obtain and they are usually considered less interesting by many drug discovery chemists and structural biologists. However, weak binding data are vital for the development of scoring functions. What data are available indicate that unpaired buried polar groups at the protein–ligand interface are strongly adverse to binding. For example, few buried CO and NH groups in folded proteins fail to form hydrogen bonds. 26 Therefore, in the ligand design process, one has to ensure that polar functional groups, either of the protein or the ligand, will find suitable counterparts if they become buried upon ligand binding. Another situation that can lead to diminished binding affinity is imperfect steric fitting, which leads to holes at the protein–ligand interface. The enthalpic and the entropic component of the binding affinity can be determined experimentally, for example, by isothermal titration calorimetry
46 The Use of Scoring Functions in Drug Discovery Applications Figure 1 Typical interactions found in protein–ligand complexes. Usually, the lipophilic part of the ligand is in contact with the lipophilic parts of the protein (side chains of Ile, Val, Leu, Phe, Trp, perpendicular contact to amide bonds). In addition, hydrogen bonds are often involved. Some interactions can be charge assisted. Cation–p interactions and metal complexation can also play a significant role in individual cases. protein ligand O H N O H O H O H N − O O − O CH 3 Zn 2+ H N + H + N H H H (ITC). Unfortunately, these data are still sparse and are difficult to interpret. 27 Existing thermodynamic data indicate that there is always a substantial compensation between enthalpic and entropic contributions. 28–30 The data also show that the binding may be enthalpy-driven (e.g., streptatividin–biotin, G ¼ 76:5 kJ/mol; H ¼ 134 kJ/mol) or entropy-driven (e.g., streptavidin–2-(4 0 -hydroxy-azobenzene)benzoic acid (HABA), G ¼ 22:0 kJ/mol; H ¼þ7:1 kJ/mol). 31 Data from protein mutants yield estimates of 5 2:5 kJ/mol for the contribution from individual hydrogen bonds to the binding affinity. 32–34 Similar values have been obtained for the contribution of an intramolecular hydrogen bond to protein stability. 35–37 The consistency of experimental values derived from different proteins suggests some degree of additivity in the hydrogen-bonding interactions. The contribution of hydrogen bonds to the binding affinity strongly depends on solvation and desolvation effects. Here lies the biggest challenge C H 3 N + S H hydrogen bonds ionic interactions hydrophobic interactions cation-π interaction metal complexation
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Reviews in Computational Chemistry
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Kenny B. Lipkowitz Department of Ch
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vi Preface three-dimensional struct
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viii Preface some descriptors and i
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Epilogue and Dedication My associat
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Contents 1. Clustering Methods and
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Contents xv Electron Transfer in Po
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Contributors John M. Barnard, Barna
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Contributors to Previous Volumes *
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Volume 3 (1992) Tamar Schlick, Opti
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Volume 7 (1996) Geoffrey M. Downs a
Page 23 and 24: Volume 11 (1997) Mark A. Murcko, Re
Page 25 and 26: T. Daniel Crawford* and Henry F. Sc
Page 27 and 28: Topics Covered in Volumes 1-18 * Ab
Page 29 and 30: Reviews in Computational Chemistry
Page 31 and 32: 2 Clustering Methods and Their Uses
Page 39 and 40: 10 Clustering Methods and Their Use
Page 71 and 72: 42 The Use of Scoring Functions in
Page 73: 44 The Use of Scoring Functions in
Page 79 and 80: Table 1 Reference List for the Most
Page 117 and 118: CHAPTER 3 Potentials and Algorithms
Page 119 and 120: Vn (1 + cos(nω + γ)) 2 K θ (θ
Page 121 and 122: are modified by their environment w
Page 123 and 124: Table 1 Polarizability Parameters f
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The polarizable point dipole models
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two to three orders of magnitude sl
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M i z i + q i d i k i and shell cha
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Shell Models 103 on estimates of sh
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minimization can be replaced by mor
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The energy required to create a cha
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for all i ði:e:; 8 iÞ: @U @qi l
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where we have used q Cl ¼ qNa. The
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Electronegativity Equalization Mode
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Electronegativity Equalization Mode
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of N molecules is taken as a Hartre
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is treated using variable charges.
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water have been developed, includin
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Applications 123 classical and rigi
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developing polarizable models. A va
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Comparison of the Polarization Mode
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negligible errors in such propertie
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ecome significant at field strength
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noteworthy in this regard because t
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References 135 9. P. Cieplak and P.
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References 137 48. E. L. Pollock an
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References 139 Computational Chemis
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References 141 139. J. Hinze and H.
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References 143 178. J. J. P. Stewar
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References 145 216. M. W. Mahoney a
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CHAPTER 4 New Developments in the T
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Introduction 149 applications). For
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FCWD(∆E ) FCWD(0) ∆E = 0 ∆E
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Introduction 153 Equations [6]-[12]
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Paradigm of Free Energy Surfaces 15
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Paradigm of Free Energy Surfaces 15
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In Eqs. [18] and [19], F0i is the e
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where ‘‘þ’’ and ‘‘ ’
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is, however, small for the usual co
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solute-solvent coupling through the
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where Z is the electrode overpotent
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energy surfaces of ET. 33,50-56 It
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This result indicates a fundamental
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βF i (X ) 40 20 0 −20 1 2 βλ 1
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Table 1 Main Features of the Two-Pa
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and Hss ¼ U rep ss 1 2 X j;k ðmj
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λ i /eV 4 3 2 1 0 0 10 20 30 40 Bo
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the width/Stokes shift relation (Eq
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Table 3 Mapping of the Q Model on S
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This situation is of course not sat
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F ± (Y ad )/λ I 0.8 0.4 0 −0.4
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it by choosing the GMH basis set 7
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Anharmonic higher order terms gain
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of the radiation is the perturbatio
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individual vibrational excitations
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m 12 /D 6 5.5 5 4.5 4 3.5 17 18 19
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In Eq. [144], the coordinates Y km
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ε/M −1 cm −1 4000 2000 0 analy
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βσ 2 /10 3 cm −1 14 12 10 8 Opt
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0.2 0.1 0 12 16 20 24 28 32 The app
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References 207 7. M. D. Newton, Adv
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References 209 59. R. Kubo and Y. T
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Reviews in Computational Chemistry Volume 18

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