Reviews in Computational Chemistry Volume 18

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References 85 172. C. Lemmen, T. Lengauer, and G. Klebe, J. Med. Chem., 41, 4502 (1998). FlexS: A Method for Fast Flexible Ligand Superposition. 173. C. Lemmen, A. Zien, R. Zimmer, and T. Lengauer, Abstracts of the Pacific Symposium on Biocomputing, Big Island, Hawaii, January 4–9, 1999, pp. 482–493. Application of Parameter Optimization to Molecular Comparison. 174. G. R. Desiraju and T. Steiner, The Weak Hydrogen Bond in Chemistry and Biology, Oxford University Press, Oxford, UK, 1999. 175. G. Parkinson, A. Gunasekera, J. Vojtechovsky, X. Zhang, T. Kunkel, H. Berman, and R. H. Ebright, Nature Struct. Biol., 3, 837 (1996). Aromatic Hydrogen Bond in Sequence-Specific Protein DNA Recognition. 176. J. P. Gallivan and D. A. Dougherty, Proc. Natl. Acad. Sci. U.S.A., 96, 9459 (1999). Cation–p Interactions in Structural Biology. 177. J. P. Gallivan and D. A. Dougherty, J. Am. Chem. Soc., 122, 870 (2000). A Computational Study of Cation–p Interactions vs. Salt Bridges in Aqueous Media: Implications for Protein Engineering. 178. G. B. McGaughey, M. Gagné, and A. K. Rappé, J. Biol. Chem., 273, 15458 (1998). p-Stacking Interactions. 179. C. Chipot, R. Jaffe, B. Maigret, D. A. Pearlman, and P. A. Kollman, J. Am. Chem. Soc., 118, 11217 (1996). The Benzene Dimer: A Good Model for p–p Interactions in Proteins? A Comparison Between the Benzene and the Toluene Dimers in the Gas Phase and in Aqueous Solution. 180. T. G. Davies, R. E. Hubbard, and J. R. H. Tame, Protein Sci., 8, 1432 (1999). Relating Structure to Thermodynamics: The Crystal Structures and Binding Affinity of Eight OppA- Peptide Complexes. 181. G. Klebe, F. Dullweber, and H.-J. Boehm, in Drug-Receptor Thermodynamics: Introduction and Applications, R. B. Raffa, Ed., Wiley, Chichester, UK, 2001, pp. 83–103. Thermodynamic Models of Drug-Receptor Interactions: A General Introduction. 182. S. Ha, R. Andreani, A. Robbins, and I. Muegge, J. Comput.-Aided Mol. Design, 14, 435 (2000). Evaluation of Docking/Scoring Approaches: A Comparative Study Based on MMP3 Inhibitors. 183. I. Massova and P. A. Kollman, Perspect. Drug Discovery Design, 18, 113 (2000). Combined Molecular Mechanical and Continuum Solvent Approach (MM-PBSA/GBSA) to Predict Ligand Binding. 184. B. Kuhn and P. A. Kollman, J. Med. Chem., 43, 3786 (2000). Binding of a Diverse Set of Ligands to Avidin and Streptavidin: An Accurate Quantitative Prediction of Their Relative Affinities by a Combination of Molecular Mechanics and Continuum Solvent Models. 185. N. Froloff, A. Windemuth, and B. Honig, Protein Sci., 6, 1293 (1997). On the Calculation of Binding Free Energies Using Continuum Methods: Application to MHC Class I Protein– Peptide Interactions. 186. J. Shen, J. Med. Chem., 40, 2953 (1997). A Theoretical Investigation of Tight-Binding Thermolysin Inhibitors. 187. C. J. Woods, M. A. King, and J. W. Essex, J. Comput.-Aided Mol. Design, 15, 129 (2001). The Configurational Dependence of Binding Free Energies: A Poisson–Boltzmann Study of Neuraminidase Inhibitors. 188. G. Archontis, T. Simonson, and M. Karplus, J. Mol. Biol., 306, 307 (2001). Binding Free Energies and Free Energy Components from Molecular Dynamics and Poisson–Boltzmann Calculations. Application to Amino Acid Recognition by Aspartyl–tRNA Synthetase. 189. A. R. Leach, J. Mol. Biol., 235, 345 (1994). Ligand Docking to Proteins With Discrete Side- Chain Flexibility. 190. R. M. A. Knegtel, I. D. Kuntz, and C. M. Oshiro, J. Mol. Biol., 266, 424 (1997). Molecular Docking to Ensembles of Protein Structures.
86 The Use of Scoring Functions in Drug Discovery Applications 191. C. McMartin and R. S. Bohacek, J. Comput.-Aided Mol. Design, 11, 333 (1997). QXP: Powerful, Rapid Computer Algorithms for Structure-Based Drug Design. 192. J. Apostolakis, A. Plückthun, and A. Caflisch, J. Comput. Chem., 19, 21 (1998). Docking Small Ligands in Flexible Binding Sites. 193. V. Schnecke, C. A. Swanson, E. D. Getzoff, J. A. Tainer, and L. A. Kuhn, Proteins: Struct., Funct., Genet., 33, 74 (1998). Screening a Peptidyl Database for Potential Ligands to Proteins With Side-Chain Flexibility. 194. I. Kolossváry, and W. C. Guida, J. Comput. Chem., 20, 1671 (1999). Low-Mode Conformational Search Elucidated: Application to C39H80 and Flexible Docking of 9-Deazaguanine Inhibitors to PNP. 195. H. B. Broughton, J. Mol. Graphics Modell., 18, 247 (2000). A Method for Including Protein Flexibility in Protein–Ligand Docking: Improving Tools for Database Mining and Virtual Screening. 196. H. Claussen, C. Buning, M. Rarey, and T. Lengauer, J. Mol. Biol., 308, 377 (2001). FlexE: Efficient Molecular Docking Considering Protein Structure Variations. 197. M. D. Miller, R. P. Sheridan, S. K. Kearsley, and D. J. Underwood, in Methods in Enzymology, L. C. Kuo and J. A. Shafer, Eds., Academic Press, San Diego, 1994, Vol. 241, pp. 354–370. Advances in Automated Docking Applied to Human Immunodeficiency Virus Type 1 Protease. 198. P. S. Charifson, J. J. Corkery, M. A. Murcko, and W. P. Walters, J. Med. Chem., 42, 5100 (1999). Consensus Scoring: A Method for Obtaining Improved Hit Rates from Docking Databases of Three-Dimensional Structures into Proteins. 199. C. A. Baxter, C. W. Murray, D. E. Clark, D. R. Westhead, and M. D. Eldridge, Proteins: Struct., Funct., Genet., 33, 367 (1998). Flexible Docking Using Tabu Search and an Empirical Estimate of Binding Affinity. 200. WDI: World Drug Index, 1996, Derwent Information. http://www.derwent.com. 201. Protherics PLC, Runcorn, Cheshire, UK. The data set was formerly available at http://www. protherics.com/crunch/. 202. S. B. Shuker, P. J. Hajduk, R. P. Meadows, and S. W. Fesik, Science, 274, 1531 (1996). Discovering High-Affinity Ligands for Proteins: SAR by NMR. 203. N. Tomioka and A. Itai, J. Comput.-Aided Mol. Design, 8, 347 (1994). GREEN: A Program Package for Docking Studies in Rational Drug Design. 204. T. Toyoda, R. K. B. Brobey, G.-I. Sano, T. Horii, N. Tomioka, and A. Itai, Biochem. Biophys. Res. Commun., 235, 515 (1997). Lead Discovery of Inhibitors of the Dihydrofolate Reductase Domain of Plasmodium falciparum Dihydrofolate Reductase–Thymidylate Synthase. 205. P. Burkhard, P. Taylor, and M. D. Walkinshaw, J. Mol. Biol., 277, 449 (1998). An Example of a Protein Ligand Found by Database Mining: Description of the Docking Method and Its Verification by a 2.3 A˚ X-Ray Structure of a Thrombin–Ligand Complex. 206. R. Abagyan, M. Trotov, and D. Kuznetsov, J. Comput. Chem., 15, 488 (1994). ICM—A New Method for Protein Modeling and Design: Applications to Docking and Structure Prediction from the Distorted Native Conformation. 207. M. Schapira, B. M. Raaka, H. H. Samuels, and R. Abagyan, Proc. Natl. Acad. Sci. U.S.A., 97, 1008 (2000). Rational Discovery of Novel Nuclear Hormone Receptor Antagonists. 208. A. V. Filikov, V. Monan, T. A. Vickers, R. H. Griffey, P. D. Cook, R. A. Abagyan, and T. L. James, J. Comput.-Aided Mol. Design, 14, 593 (2000). Identification of Ligands for RNA Targets via Structure-Based Virtual Screening: HIV-1 TAR. 209. Available Chemicals Directory, MDL Information Systems, Inc., San Leandro, CA, USA. http://www.mdl.com. 210. D. W. Christianson and C. A. Fierke, Acc. Chem. Res., 29, 331 (1996). Carbonic Anhydrase: Evolution of the Zinc-Binding Site by Nature and by Design. 211. Maybridge Database, Maybridge Chemical Co. Ltd., UK, 1999. http://www.maybridge.com and http://www.daylight.com.
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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 63 and 64: 34 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 135 and 136: The energy required to create a cha
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Page 139 and 140: where we have used q Cl ¼ qNa. The
Page 141 and 142: 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
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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
Page 161 and 162: noteworthy in this regard because t
Page 163 and 164: 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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