Reviews in Computational Chemistry Volume 18

More documents

Recommendations

Info

estimations of binding affinity. These scoring functions guide the conformational and orientational search of a ligand within the binding site and ultimately provide a relative ranking of putative ligands with respect to a target. The purpose of this chapter is to describe some of these functions, discuss their strengths and weaknesses, explain how they are used in practical applications, and present selected results to highlight the current status of the field. The Process of Virtual Screening Introduction 43 In this section, we discuss a general strategy of virtual screening based on the 3D structure of a target. Typically, the following steps are typically taken. 1. Analysis of the 3D protein structure. 2. Selection of one or more key interactions that need to be satisfied by all candidate molecules. 3. Computational search (by docking and/or pharmacophore queries) in chemical databases for compounds that fit into the binding site and satisfy key interactions. 4. Analysis of the retrieved hits and removal of undesirable compounds. 5. Synthesis or purchase of the selected compounds. 6. Biological testing. The first step is usually a careful analysis of available 3D protein structures. If possible, highly homologous structures will also be analyzed, either to generate additional ideas about possible ligand structural motifs or to gain some insight on how to achieve selectivity relative to other proteins of the same class. A superposition of different protein–ligand complexes can provide some indication about key interactions that are repeatedly found in tight binding protein–ligand complexes. Such an overlay will also highlight flexible parts of the protein. Programs like GRID 19 or LUDI 20,21 are frequently used to visualize potential interaction sites (hot spots) in the binding site of the protein. If there are conserved water molecules in the binding site mediating hydrogen bonds between the protein and the ligand, and if these water molecules cannot be replaced, then including them in the docking process can dramatically improve the hit rate. 13–15 An important result from the aforementioned 3D structure analysis is usually the identification of one or more key interactions that all ligands should satisfy. An example of such a binding hypothesis is that aspartic protease inhibitors should form at least one hydrogen bond to the catalytic Asp side chains. Although it could be left to the computational algorithm using a good scoring function to pick molecules, experience indicates that the percentage of active compounds in a designed library can be significantly increased if a good binding hypothesis is used as filter. In addition, part of a known ligand may be used as a starting scaffold, and virtual screening techniques can then be used to select side chains.
44 The Use of Scoring Functions in Drug Discovery Applications Once a reasonable binding hypothesis has been generated, the next step is the actual virtual screening. Whether one uses databases of commercially available compounds or ‘‘virtual’’ libraries of hypothetical chemical structures, it makes sense to dock not just any compound, but only those that pass a number of simple property filters. Such filters remove 1. Compounds with reactive functional groups. Reactive groups such as SO2Cl and CHO cause problems in some biological assays due to nonspecific covalent binding to the protein. 2. Compounds with a molecular weight below 150 or above 500. Very small molecules like benzene are known to bind to proteins in a rather nonspecific manner and at several sites. Very large molecules (like polypeptides) are difficult to optimize subsequently because bioavailability is usually low for compounds with a molecular weight above 500. 3. Compounds that are not ‘‘drug-like’’ according to criteria that have been derived from sets of known drugs. 22,23 Each remaining compound is then docked into the binding site and scored. The docking process is the most demanding step computationally and is usually carried out on multiprocessor computers. Depending on the docking algorithm and the scoring function, this step may easily take several days of CPU time. The result is a list of several hundred to a few thousand docked small molecule structures each with a computed score, which is further analyzed to weed out undesirable structures. Selection criteria could be 1. Lipophilicity, if not addressed before. Highly lipophilic molecules are difficult to test because of their low solubility in water. 2. Structural class. If 50% of the docked structures belong to a single chemical class, it is probably unnecessary to test all of them. 3. Improbability of docked binding mode. Fast docking tools cannot produce reasonable solutions for all compounds. Often even some high-scoring compounds are found to be docked to the outer surface of the protein. Computational filters help to detect such situations. Finally, the selected compounds are purchased or synthesized and then tested. If the goal is to identify weakly binding small molecules, it is important to ensure that the biological assay is sensitive and robust enough to pick up these molecules. Measurements using 100–1000 mM concentration of the ligand frequently cause problems due to the limited solubility of the ligands in water. To compensate for this, the assay is often carried out in the presence of 1–5% dimethyl sulfoxide (DMSO) (see, e.g., Ref. 14). Note that the process of virtual screening still involves manual interventions at various stages. In principle, the whole process can be carried out in a fully automated manner, but in practice visual inspection and manual selection are still very useful.
Page 1 and 2:
Reviews in Computational Chemistry
Page 3 and 4:
Kenny B. Lipkowitz Department of Ch
Page 5 and 6:
vi Preface three-dimensional struct
Page 7 and 8:
viii Preface some descriptors and i
Page 9 and 10:
Epilogue and Dedication My associat
Page 11 and 12:
Contents 1. Clustering Methods and
Page 13 and 14:
Contents xv Electron Transfer in Po
Page 15 and 16:
Contributors John M. Barnard, Barna
Page 17 and 18:
Contributors to Previous Volumes *
Page 19 and 20:
Volume 3 (1992) Tamar Schlick, Opti
Page 21 and 22: 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: 42 The Use of Scoring Functions in
Page 75 and 76: 46 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
Page 125 and 126:
The polarizable point dipole models
Page 127 and 128:
two to three orders of magnitude sl
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
Page 133 and 134:
minimization can be replaced by mor
Page 135 and 136:
The energy required to create a cha
Page 137 and 138:
for all i ði:e:; 8 iÞ: @U @qi l
Page 139 and 140:
where we have used q Cl ¼ qNa. The
Page 141 and 142:
Electronegativity Equalization Mode
Page 143 and 144:
Electronegativity Equalization Mode
Page 145 and 146:
of N molecules is taken as a Hartre
Page 147 and 148:
is treated using variable charges.
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
Page 161 and 162:
noteworthy in this regard because t
Page 163 and 164:
References 135 9. P. Cieplak and P.
Page 165 and 166:
References 137 48. E. L. Pollock an
Page 167 and 168:
References 139 Computational Chemis
Page 169 and 170:
References 141 139. J. Hinze and H.
Page 171 and 172:
References 143 178. J. J. P. Stewar
Page 173 and 174:
References 145 216. M. W. Mahoney a
Page 175 and 176:
CHAPTER 4 New Developments in the T
Page 177 and 178:
Introduction 149 applications). For
Page 179 and 180:
FCWD(∆E ) FCWD(0) ∆E = 0 ∆E
Page 181 and 182:
Introduction 153 Equations [6]-[12]
Page 183 and 184:
Paradigm of Free Energy Surfaces 15
Page 185 and 186:
Paradigm of Free Energy Surfaces 15
Page 187 and 188:
In Eqs. [18] and [19], F0i is the e
Page 189 and 190:
where ‘‘þ’’ and ‘‘ ’
Page 191 and 192:
is, however, small for the usual co
Page 193 and 194:
solute-solvent coupling through the
Page 195 and 196:
where Z is the electrode overpotent
Page 197 and 198:
energy surfaces of ET. 33,50-56 It
Page 199 and 200:
This result indicates a fundamental
Page 201 and 202:
βF i (X ) 40 20 0 −20 1 2 βλ 1
Page 203 and 204:
Table 1 Main Features of the Two-Pa
Page 205 and 206:
and Hss ¼ U rep ss 1 2 X j;k ðmj
Page 207 and 208:
λ i /eV 4 3 2 1 0 0 10 20 30 40 Bo
Page 209 and 210:
the width/Stokes shift relation (Eq
Page 211 and 212:
Table 3 Mapping of the Q Model on S
Page 213 and 214:
This situation is of course not sat
Page 215 and 216:
F ± (Y ad )/λ I 0.8 0.4 0 −0.4
Page 217 and 218:
it by choosing the GMH basis set 7
Page 219 and 220:
Anharmonic higher order terms gain
Page 221 and 222:
of the radiation is the perturbatio
Page 223 and 224:
individual vibrational excitations
Page 225 and 226:
m 12 /D 6 5.5 5 4.5 4 3.5 17 18 19
Page 227 and 228:
In Eq. [144], the coordinates Y km
Page 229 and 230:
ε/M −1 cm −1 4000 2000 0 analy
Page 231 and 232:
βσ 2 /10 3 cm −1 14 12 10 8 Opt
Page 233 and 234:
0.2 0.1 0 12 16 20 24 28 32 The app
Page 235 and 236:
References 207 7. M. D. Newton, Adv
Page 237 and 238:
References 209 59. R. Kubo and Y. T
show all

Reviews in Computational Chemistry Volume 18

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?