computer modeling in molecular biology.pdf

More documents

Recommendations

Info

4 Molecular Dynamics and Free Energy Calculations 914.3.3 Computed Changes in Protein Stability for theIle 96-+Ala MutationTable 4-4 lists the computed free energy values obtained by the exponential formula(EF) and thermodynamic integration (TI) procedures for the Ile 96 into Alaalchemical transformation. These are, ~lGf”~ for the solvated native protein,LIGL’~ for the solvated unfolded state, and d GI’A for the gas phase reference state.Table 4-4. Computed free energy changes (in kcal/aol) the Ile --* Ala mutation in barnase.Contribution AGf-A AGf’A AAGf,,, AAG,,, AAG,,fprotein water referenceTI -3.09 -8.3~~-7.15 - 5.21 -1.15 4.06EF -3.39 -6.81 -6.56 - 3.42 -0.25 3.17EXP -3.3a; -4.0b -0.21‘Negative values in AAG correspond to contributions in which the wild type (Ile) is stabilizedrelative to the mutant (Ala). AGf, AG, and AG, are the free energies for the alchemicaltransformation Ile + Ala, in the folded state (protein), the unfolded state (water), and the unfoldedstate in the gas phase (reference) respectively. AAGf-u is the unfolding free energy differencefor the alchemical transformation. AAG,,, and AA G,,f are respectively, the solvationfree energy differences for Ile versus Ala in water and in the protein interior. The correspondingAAG values are derived from the corresponding AG values as explained in the text.EF, TI and EXP stand for, the exponential formula Eq. (4-13), the thermodynamic integrationprocedure Eq. (4-15), and experimental results respectively. The superscripts a, b and c indicatethat values are taken from Kellis et al. [89, 901 and Wolfenden et 01. [lo61 respectively.By use of the thermodynamic cycle (Figure 4-17 a), the corresponding difference inunfolding free energy, d ~lG~,~ is obtained from the following expression [lo] :AAG~,, = AG,”~ - LIG~”~= AG&, - AG;,,, (4-16)The calculated LIL~G~+~ values are -3.41 and -5.21 kcal/mol obtained using theEF and TI procedures respectively. These values agree in sign and magnitude withthe values of - 3.3 and - 4.0 kcal/mol, measured in the experiment unfolding studiesof Kellis et al. [89, 901. The negative values indicate that unfolding the mutant isenergetically more favorable than unfolding the wild type, and hence that the wildtype folded state is more stable than the mutant folded state.Using another thermodynamic cycle the solvation free energy differencedAG,,,, is given by (Figure 4-17b):AAG,,, = AG;’* - LIG~+ = AG$, - AG:~,, (4-17)
92 Shoshana 1 Wodak, Daniel van Belle, and Martine PkvostproteinIAGf + uwaterA G ;-'AAG:->~waterreferenceIA G sol"* E'UwaterA G :->A4GL'AreferencwaterreferenceE1rIAGr+ f* EIfproteinA G :-'AA G ;->Areferenciprotein
Page 2 and 3:
Computer Modellingin Molecular Biol
Page 5 and 6:
Professor Julia M. GoodfellowDepart
Page 7 and 8:
ContentsColour Illustrations ......
Page 9 and 10:
XContributorsBenoit RouxGroupe de R
Page 11:
Colour IllustrationsXI11Figure 4-4.
Page 14 and 15:
XVIColour IllustrationsFigure 7-18.
Page 16 and 17:
2 Julia M. Goodfellow and Mark A. W
Page 18 and 19:
Page 20 and 21:
Page 22 and 23:
Computer Modelling in Molecular Bio
Page 24 and 25:
2 Modellinn Protein Structures 11su
Page 26 and 27:
2 Modelling Protein Structures 13St
Page 28 and 29:
2 Modelling Protein Structures 15Th
Page 30 and 31:
2 Modelling Protein Structures 17Th
Page 32 and 33:
2 Modelling Protein Structures 19Fa
Page 34 and 35:
2 Modellinn Protein Structures 21th
Page 36 and 37:
2 Modelling Protein Structures 23ho
Page 38 and 39:
2 Modelling Protein Structures 25Wo
Page 40 and 41:
2 Modelling Protein Structures 271.
Page 42 and 43:
2.3.3 Available Modelling Programs2
Page 44 and 45:
2 Modelling Protein Structures 31sp
Page 46 and 47:
2 Modellina Protein Structures 33Ac
Page 48 and 49:
2 Modelling Protein Structures 35[8
Page 50 and 51:
38 D. J Osmthorue and I? K. C Paul3
Page 52 and 53: 40 D. J; Osnuthorue and I? K. C. Pa
Page 54 and 55: 42 D. J. OsPuthorDe and J? K. C Pau
Page 56 and 57: ~443.3.1D. J. Osnuthorpe and I? K.
Page 58 and 59: 46 D. 1 Osnuthorpe and r! K. C. Pau
Page 60 and 61: 48 D. J. Osguthorpe and I? K. C. Pa
Page 62 and 63: 50 D. J. Osguthorpe and I? K. C. Pa
Page 64 and 65: 52 D. J. Osguthorpe and I! K. C. Pa
Page 66 and 67: 54 D. L Osguthorpe and I! K. C Paul
Page 68 and 69: 56 D. J Osguthorpe and I? K. C. Pau
Page 70 and 71: 58 D. J. Osguthorpe and r! K. C. Pa
Page 72 and 73: Computer Modelling in Molecular Bio
Page 74 and 75: 4 Molecular Dynamics and Free Energ
Page 80 and 81: 4 Molecular Dvnamics and Free Enera
Page 94 and 95: 4 Molecular Dynamics and Free Enerw
Page 104 and 105: 4 Molecular Dynamics and Free Enern
Page 114 and 115: Computer Modelling in Molecular Bio
Page 116 and 117: 5 Modelling Nucleic Acids 105and it
Page 118 and 119: 5 Modelling Nucleic Acids 107of the
Page 120 and 121: 5 Modelling Nucleic Acids 109ElFigu
Page 122 and 123: 5 Modelling Nucleic Acids 11 1Figur
Page 124 and 125: 5 Modellinn Nucleic Acids 1135.7 Ch
Page 126 and 127: 5 Modelling Nucleic Acids 115With i
Page 128 and 129: 5 Modellinn Nucleic Acids 117Figure
Page 130 and 131: 5 Modelling Nucleic Acids 119Figure
Page 132 and 133: 5 Modelling Nucleic Acids 1211.0mod
Page 134 and 135: 5 Modelling Nucleic Acids 1235.12 M
Page 136 and 137: 5 Modelling Nucleic Acids 125rotati
Page 138 and 139: 5 Modellinp Nucleic Acids 1275.14 M
Page 140 and 141: 5 Modelling Nucleic Acids 129simula
Page 142 and 143: 5 Modellinn Nucleic Acids 131[40] G
Page 144 and 145: 134 Benoft Roux6.1 IntroductionThe
Page 146 and 147: 136 Benoit Roux[18-201. The gramici
Page 148 and 149: 138 Benoft Rouxwhere D (x) is the l
Page 150 and 151: 140 Benoft Roux“one-ion” conduc
Page 152 and 153:
142 Benoft RouxTable 6-1. Interacti
Page 154 and 155:
144 Benoit RouxFigure 6-2. Solvated
Page 156 and 157:
146 Benoit Rouxwater box equilibrat
Page 158 and 159:
148 Benoit Roux-I I I I II I I I II
Page 160 and 161:
150 Benoi’t Rouxbulk waters. A st
Page 162 and 163:
152 Benoit RouxOne advantage of thi
Page 164 and 165:
154 Benoft Roux-.3.-15-c 10 -h5 5 -
Page 166 and 167:
156 Benoft Rouxwhere x and v are th
Page 168 and 169:
158 Benoit RouxReactant to ProductO
Page 170 and 171:
160 Benoit Rouxremain in close cont
Page 172 and 173:
162 Benoit Rouxis the deviation of
Page 174 and 175:
164 Benoft RouxTable 6-3. Pre-expon
Page 176 and 177:
166 Benoft RouxThis chapter demonst
Page 178 and 179:
168 Benoft Rouxproximately one Kf c
Page 180 and 181:
Computer Modelling in Molecular Bio
Page 182 and 183:
7 Major Histocompatibility Complex
Page 184 and 185:
7 Major HistocompatibiIity Complex
Page 186 and 187:
7 Maior Histocompatibility Complex
Page 188 and 189:
Page 190 and 191:
Page 192 and 193:
7 Maior Histocomuatibilitv Cornulex
Page 194 and 195:
Page 196 and 197:
Page 198 and 199:
Page 200 and 201:
Page 202 and 203:
Page 204 and 205:
Page 206 and 207:
Page 208 and 209:
Page 210 and 211:
HLA-B*270 IHLA-B*2702HLA-B*2703HLA-
Page 212 and 213:
7 Maior Histocomvatibilitv Comrdex
Page 214 and 215:
Page 216 and 217:
Page 218 and 219:
Page 220 and 221:
Page 222 and 223:
Page 224 and 225:
216 Oliver S. Smart8.1 Introduction
Page 226 and 227:
218 Oliver S. Smartvolving large mo
Page 228 and 229:
220 Oliver S. Smart8.2 TheoryConsid
Page 230 and 231:
222 Oliver S. Smart(8-2)and applyin
Page 232 and 233:
224 Oliver S. SmartThe repulsion te
Page 234 and 235:
226 Oliver S. Smart8.4 Applications
Page 236 and 237:
228 Oliver S. Smartrigid-body penal
Page 238 and 239:
230 Oliver S. Smart216 252 200 324
Page 240 and 241:
232 Oliver S. SmartrbFigure 8-5. A
Page 242 and 243:
234 Oliver S. Smartlink the eoc and
Page 244 and 245:
236 Oliver S. Smarttermediates mark
Page 246 and 247:
238 Oliver S. Smartmoving conformat
Page 248 and 249:
240 Oliver S. Smart[39] Halgren, T.
Page 250 and 251:
242 IndexGGramicidin A 134- NMR dat
show all

computer modeling in molecular biology.pdf

Create successful ePaper yourself

Delete template?

Save as template?