Reviews in Computational Chemistry Volume 18

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References 83 130. P. D. J. Grootenhuis and P. J. M. van Galen, Acta Crystallogr., Sect. D, 51, 560 (1995). Correlation of Binding Affinities With Non-Bonded Interaction Energies of Thrombin- Inhibitor Complexes. 131. S. J. Weiner, P. A. Kollman, D. A. Case, U. C. Singh, C. Ghio, G. Alagona, S. Profeta Jr., and P. Weiner, J. Am. Chem. Soc., 106, 765 (1984). A New Force Field for Molecular Mechanical Simulation of Nucleic Acids and Proteins. 132. S. J. Weiner, P. A. Kollman, D. T. Nguyen, and D. A. Case, J. Comput. Chem., 7, 230 (1986). An All Atom Force Field for Simulations of Proteins and Nucleic Acids. 133. B. R. Brooks, R. E. Bruccoleri, B. D. Olafson, D. J. States, S. Swaminathan, and M. Karplus, J. Comput. Chem., 4, 187 (1983). CHARMM: A Program for Macromolecular Energy, Minimization, and Dynamics Calculations. 134. A. Nicholls and B. Honig, Science, 268, 1144 (1995). Classical Electrostatics in Biology and Chemistry. 135. N. Majeux, M. Scarsi, J. Apostolakis, C. Ehrhardt, and A. Caflisch, Proteins: Struct., Funct., Genet., 37, 88 (1999). Exhaustive Docking of Molecular Fragments with Electrostatic Solvation. 136. W. C. Still, A. Tempczyk, R. C. Hawley, and T. Hendrickson, J. Am. Chem. Soc., 112, 6127 (1990). Semianalytical Treatment of Solvation for Molecular Mechanics and Dynamics. 137. M. L. Lamb, K. W. Burdick, S. Toba, M. M. Young, A. G. Skillman, X. Zou, J. R. Arnold, and I. D. Kuntz, Proteins: Struct., Funct., Genet., 42, 296 (2001). Design, Docking and Evaluation of Multiple Libraries Against Multiple Targets. 138. T. Zhang and D. E. Koshland Jr., Protein Sci., 5, 348 (1996). Computational Method for Relative Binding Free Energies of Enzyme–Substrate Complexes. 139. P. H. Huenenberger, V. Helms, N. Narayana, S. S. Taylor, and J. A. McCammon, Biochemistry, 38, 2358 (1999). Determinants of Ligand Binding to cAMP-Dependent Protein Kinase. 140. H. Meirovitch, in Reviews in Computational Chemistry, K. B. Lipkowitz and D. B. Boyd, Eds., Wiley-VCH, New York, 1998, Vol. 11, pp. 1–74. Calculation of the Free Energy and the Entropy of Macromolecular Systems by Computer Simulation. 141. J. Greer, J. W. Erickson, J. J. Baldwin, and M. D. Varney, J. Med. Chem., 37, 1035 (1994). Application of the Three-Dimensional Structures of Protein Target Molecules in Structure- Based Design. 142. J. Bostrom, P.-O. Norrby, and T. Liljefors, J. Comput.-Aided Mol. Design, 12, 383 (1998). Conformational Energy Penalties of Protein-Bound Ligands. 143. M. Vieth, J. D. Hirst, and C. L. Brooks III, J. Comput.-Aided Mol. Design, 12, 563 (1998). Do Active Site Conformations of Small Ligands Correspond to Low Free-Energy Solution Structures ? 144. G. Klebe and T. Mietzner, J. Comput.-Aided Mol. Design, 8, 583 (1994). A Fast and Efficient Method to Generate Biologically Relevant Conformations. 145. I. D. Kuntz, K. Chen, K. A. Sharp, and P. A. Kollman, Proc. Natl. Acad. Sci. U.S.A., 96, 9997 (1999). The Maximal Affinity of Ligands. 146. Ajay and M. A. Murcko, J. Med. Chem., 38, 4953 (1995). Computational Methods to Predict Binding Free Energy in Ligand–Receptor Complexes. 147. J. D. Hirst, Curr. Opin. Drug Discovery Dev., 1, 28 (1998). Predicting Ligand-Binding Energies. 148. R. M. A. Knegtel and P. D. J. Grootenhuis, Perspect. Drug Discovery Design, 9–11, 99 (1998). Binding Affinities and Non-Bonded Interaction Energies. 149. T. I. Oprea and G. R. Marshall, Perspect. Drug Discovery Design, 9–11, 3 (1998). Receptor- Based Prediction of Binding Activities. 150. J. R. H. Tame, J. Comput.-Aided Mol. Design, 13, 99 (1999). Scoring Functions: A View From the Bench. 151. H.-J. Boehm and M. Stahl, Med. Chem. Res., 9, 445 (1999). Rapid Empirical Scoring Functions in Virtual Screening Applications.
84 The Use of Scoring Functions in Drug Discovery Applications 152. P. J. Goodford, J. Mol. Graphics, 3, 107 (1985). Favored Sites for Drug Design. 153. M. Matsumara, W. J. Becktel, and B. W. Matthews, Nature (London), 334, 406 (1988). Hydrophobic Stabilization in T4 Lysozyme Determined Directly by Multiple Substitutions of Ile 3. 154. V. Nauchatel, M. C. Villaverde, and F. Sussman, Protein Sci., 4, 1356 (1995). Solvent Accessibility as a Predictive Tool for the Free Energy of Inhibitor Binding to the HIV-1 Protease. 155. A. M. Davis and S. J. Teague, Angew. Chem. Int. Ed., 38, 736 (1999). Hydrogen Bonding, Hydrophobic Interactions, and Failure of the Rigid Receptor Hypothesis. 156. L. Wesson and D. Eisenberg, Protein Sci., 1, 227 (1992). Atomic Solvation Parameters Applied to Molecular Dynamics of Proteins in Solution. 157. S. Vajda, Z. Weng, R. Rosenfeld, and C. DeLisi, Biochemistry, 33, 13977 (1994). Effect of Conformational Flexibility and Solvation on Receptor-Ligand Binding Free Energies. 158. S. Miyazawa and R. L. Jernigan, Macromolecules, 18, 534 (1985). Estimation of Effective Interresidue Contact Energies From Protein Crystal Structures: Quasi-Chemical Approximation. 159. P. Rose, Computer Assisted Molecular Design Course, Jan 16–18, 1997, University of California, San Francisco, CA. 160. E. Gallicchio, M. M. Kubo, and R. M. Levy, J. Am. Chem. Soc., 120, 4526 (1998). Entropy– Enthalpy Compensation in Solvation and Ligand Binding Revisited. 161. F. C. Bernstein, T. F. Koetzle, G. J. B. Williams, E. F. Meyer Jr., M. D. Brice, J. R. Rodgers, O. Kennard, T. Shimanouchi, and M. Tasumi, J. Mol. Biol., 112, 535 (1977). The Protein Data Bank: A Computer-Based Archival File for Macromolecular Structures. http:// www.rcsb.org/pdb. 162. H. Gohlke and G. Klebe, Curr. Opin. Struct. Biol., 11, 231 (2001). Statistical Potentials and Scoring Functions Applied to Protein–Ligand Binding. 163. M. J. Sippl, J. Comput.-Aided Mol. Design, 7, 473 (1993). Boltzmann’s Principle, Knowledge- Based Mean Fields and Protein Folding. An Approach to the Computational Determination of Protein Structures. 164. G. Verkhivker, K. Appelt, S. T. Freer, and J. E. Villafranca, Protein Eng., 8, 677 (1995). Empirical Free Energy Calculations of Ligand–Protein Crystallographic Complexes. I. Knowledge-Based Ligand Protein Interaction Potentials Applied to the Prediction of Human Immunodeficiency Virus 1 Protease Binding Affinity. 165. A. Wallqvist, R. L. Jernigan, and D. G. Covell, Protein Sci., 4, 1181 (1995). A Preference- Based Free-Energy Parameterization of Enzyme-Inhibitor Binding. Applications to HIV-1- Protease Inhibitor Design. 166. A. Wallqvist and D. G. Covell, Proteins: Struct., Funct., Genet., 25, 403 (1996). Docking Enzyme-Inhibitor Complexes Using a Preference-Based Free-Energy Surface. 167. Y. Sun, T. J. A. Ewing, A. G. Skillman, and I. D. Kuntz, J. Comput.-Aided Mol. Design, 12, 579 (1998). CombiDOCK: Structure-Based Combinatorial Docking and Library Design. 168. E. J. Martin, R. E. Critchlow, D. C. Spellmeyer, S. Rosenberg, K. L. Spear, and J. M. Blaney, Pharmacochem. Libr., 29, 133 (1998). Diverse Approaches to Combinatorial Library Design. 169. DOCK User Manual, Regents of the University of California, San Francisco, CA. http:// www.cmpharm.ucsf.edu/kuntz/kuntz.html. 170. R. M. A. Knegtel, D. M. Bayada, R. A. Engh, W. von der Saal, V. J. van Geerestein, and P. D. J. Grootenhuis, J. Comput.-Aided Mol. Design, 13, 167 (1999). Comparison of Two Implementations of the Incremental Construction Algorithm in Flexible Docking of Thrombin Inhibitors. 171. M. Stahl and H.-J. Boehm, J. Mol. Graphics Modell., 16, 121 (1998). Development of Filter Functions for Protein–Ligand Docking.
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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
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Volume 11 (1997) Mark A. Murcko, Re
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T. Daniel Crawford* and Henry F. Sc
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Topics Covered in Volumes 1-18 * Ab
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Reviews in Computational Chemistry
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2 Clustering Methods and Their Uses
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10 Clustering Methods and Their Use
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Page 61 and 62: 32 Clustering Methods and Their Use
Page 71 and 72: 42 The Use of Scoring Functions in
Page 79 and 80: Table 1 Reference List for the Most
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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
Page 125 and 126: The polarizable point dipole models
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Page 129 and 130: M i z i + q i d i k i and shell cha
Page 131 and 132: Shell Models 103 on estimates of sh
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Page 139 and 140: where we have used q Cl ¼ qNa. The
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Page 143 and 144: Electronegativity Equalization Mode
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Page 149 and 150: water have been developed, includin
Page 151 and 152: Applications 123 classical and rigi
Page 153 and 154: developing polarizable models. A va
Page 155 and 156: Comparison of the Polarization Mode
Page 157 and 158: negligible errors in such propertie
Page 159 and 160: ecome significant at field strength
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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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