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Alexander Karpikov, Andrea Sboner, Thomas Royce, Xin Chen, Sabas Abuabara, Jonathan Sachs Gerstein group Outside Yale: Jay Ponder, David van der Spoel, Michel Dumontier, Burak Erman, Roman Laskowski Funding: The National Institutes of Health Organizations: Yale Graduate Student Rugby Team, Yale Tango Club, Graduate Student Assembly Family: Enrique II, Martha, Ana Maria, Sofia, Enrique III, Enrique (RIP) and Elisa Flores; Itze Pavon; Edelia and Daniel Castaneda; Elodia and Clyde Hough; Samuel, Mark, John, Laura, Martha Jane, Dixon II, Gail, Dixon(RIP) and Katherine(RIP) Coulbourn; Susan and Ted Mocarski 16
Introduction The problem of predicting the time evolution of systems with many nonlinearly coupled degrees of freedom has long been a topic of much interest and intense work. Celestial mechanics computations can often by performed by assuming only 1/r potentials, but the sheer number of bodies can make the problem difficult. Fluid mechanics involves solving the Navier-Stokes equations which is simple for small systems, but not for large systems such as the global climate. Quantum dynamics can in principle be simplified by decomposition into orthogonal eigenstates, but for some systems a large number of the latter may be needed. Interactions between particles complicate the Hamiltonian, such that for the general case only a few degrees of freedom can be modeled at a time. There are many challenging dynamical problems in physics, and biological physics is no exception. Problems exist at a macroscopic scale in dealing with viscous flow, heat and mass transfer, and thermodynamics within organisms and in their interactions with the environment. On smaller length scales one encounters diffusion, Brownian motion, active motility, charge transport, and many other phenomena. At the level of single and aggregated molecules lie systems large enough that the number of degrees of freedom can be problematic yet small enough to be dominated by thermal fluctuations. At an even smaller level photochemistry, electron and proton transfer, phonons, isomerization, bond breaking, polarization, and other quantum mechanical phenomena occur which are 17
Page 1 and 2: Abstract Flexibility and domain dyn
Page 3 and 4: Flexibility and domain dynamics of
Page 5 and 6: Table of Contents Table of figures
Page 7 and 8: Can hinges be predicted by a simple
Page 9 and 10: FlexOracle (FO1, FO1M, FO) 176 Defi
Page 11 and 12: Conclusions 279 References 281 Tabl
Page 13 and 14: Table of tables Table 2.1: Amino ac
Page 15: Acknowledgements Committee: Mark Ge
Page 19 and 20: Ab initio methods attempt to model
Page 21 and 22: potential for simplification, motio
Page 23 and 24: Chapter 1: The molecular motions da
Page 25 and 26: MolMovDB is a resource for studying
Page 27 and 28: voids in proteins, helix-helix pack
Page 29 and 30: A full-feature version of FIRST5 wi
Page 31 and 32: gradual transition from the initial
Page 33 and 34: energy. Thus the first residue list
Page 35 and 36: activity, DNA-directed DNA polymera
Page 37 and 38: homology table to an entry in the C
Page 39 and 40: Highlight active sites from the CSA
Page 43 and 44: Figure 1.3: Conformational change a
Page 45 and 46: particular, we found that hinges te
Page 47 and 48: and the holo. In this case it is po
Page 49 and 50: Our molecular motions database serv
Page 51 and 52: We first made a simple GOR(Garnier-
Page 53 and 54: The morph trajectory was sterically
Page 55 and 56: protein in the Jmol window to his/h
Page 57 and 58: of the hinge was otherwise unclear,
Page 59 and 60: Catalytic Sites Atlas (nonredundant
Page 61 and 62: ! ! ! ! ! h c = number of times res
Page 63 and 64: ! ! ! ! µ h " d c D # H . It is eq
Page 65 and 66: As mentioned earlier, the fact that
Page 67 and 68:
STRIDE[50] recognizes secondary str
Page 69 and 70:
! ! alignment at this position[24,
Page 71 and 72:
To test this idea, we decided to ca
Page 73 and 74:
GOR method is useful for predicting
Page 75 and 76:
! HI active"site (i) as 0.4 for res
Page 77 and 78:
lowered, and the area under the cur
Page 79 and 80:
would be preferable to the other, o
Page 81 and 82:
Discussion Correlations were found
Page 83 and 84:
Sequence in the immediate neighborh
Page 85 and 86:
Figures p-value 0.5 0.25 0 .01 PHE
Page 87 and 88:
Figure 2.3: Secondary structure Res
Page 89 and 90:
Figure 2.5: Conservation: full set
Page 91 and 92:
Figure 2.7: Solvent accessible surf
Page 93 and 94:
completely random predictor, with a
Page 95 and 96:
samples 1800 1600 1400 1200 1000 80
Page 97 and 98:
m = distance from nearest residues
Page 99 and 100:
Hinge All Hinge Residues residues R
Page 101 and 102:
in hinge residues HI p-value 1 277
Page 103 and 104:
Total resid. in Hinge Atlas 54839 H
Page 105 and 106:
Chi- Computer annotated set Hinge A
Page 107 and 108:
BMC: Bioinformatics, we present the
Page 109 and 110:
hinges. Kundu et al. use the lowest
Page 111 and 112:
Domains can move relative to each o
Page 113 and 114:
The quantity ! E(i) represents the
Page 115 and 116:
Identification of local minima As w
Page 117 and 118:
compute FoldX_energy (stability of
Page 119 and 120:
energy element of each cluster was
Page 121 and 122:
At least two sets of atomic coordin
Page 123 and 124:
! FN (false negatives): Number of r
Page 125 and 126:
We observed qualitatively (figures
Page 127 and 128:
pairs of structures used to generat
Page 129 and 130:
coordinate set does not significant
Page 131 and 132:
The LIR family is composed of eight
Page 133 and 134:
CaM is a major calcium-binding prot
Page 135 and 136:
The results of running FlexOracle a
Page 137 and 138:
Figure 3.2: Results for individual
Page 139 and 140:
a. 139
Page 141 and 142:
Figure 3.3: Folylpolyglutamate Synt
Page 143 and 144:
. c. 143
Page 145 and 146:
a. 145
Page 147 and 148:
Figure 3.5: Lir-1 (closed) Morph ID
Page 149 and 150:
. c. 149
Page 151 and 152:
a. 151
Page 153 and 154:
Figure 3.7: Ribose binding protein
Page 155 and 156:
. c. 155
Page 157 and 158:
Tables GNM 157 Single-cut predictor
Page 159 and 160:
Chapter 4: HingeMaster: normal mode
Page 161 and 162:
The problem of hinge prediction is
Page 163 and 164:
Translation Libration Screw Motion
Page 165 and 166:
! [ K] = [ U] " [ ] 2 [ U] T Where
Page 167 and 168:
generated one ROC curve for each mo
Page 169 and 170:
! ! 1 (l " k) A(k,l) = 2 % ' $ C(i,
Page 171 and 172:
however, since we found that the Co
Page 173 and 174:
A key part of this analysis involve
Page 175 and 176:
describe its net vibrational displa
Page 177 and 178:
! Because it cuts the backbone at t
Page 179 and 180:
! ! x HingeMaster = x" # y . [6] 2
Page 181 and 182:
different values of ! " * and gener
Page 183 and 184:
manually select domains and color p
Page 185 and 186:
E. coli resides in the periplasmic
Page 187 and 188:
The results for this protein are sh
Page 189 and 190:
esidues (62). As is customary we al
Page 191 and 192:
FlexOracle (FO) The FlexOracle algo
Page 193 and 194:
extra breakpoints based on visual i
Page 195 and 196:
hinge, it is usually predicted by a
Page 197 and 198:
Supplementary methods Statistical t
Page 199 and 200:
! ! ! eigenvector. The resulting re
Page 201 and 202:
TNC induces a conformational change
Page 203 and 204:
possibility that unexpected results
Page 205 and 206:
UDP-N-acetylglucosamine enolpyruvyl
Page 207 and 208:
predicted both hinges, again with m
Page 209 and 210:
Most predictors (including FO, Ston
Page 211 and 212:
FO, hNMb, and hNMd were completely
Page 213 and 214:
HingeMaster makes a strong predicti
Page 215 and 216:
Figure 4.2: ROC curves for HingeMas
Page 217 and 218:
Figure 4.3: HingeMaster results for
Page 219 and 220:
d. e. 219
Page 221 and 222:
221
Page 223 and 224:
Figure 4.5: Human lactoferrin (open
Page 225 and 226:
Figure 4.6: Troponin C (TNC) Morph
Page 227 and 228:
Figure 4.7: Calmodulin, calcium fre
Page 229 and 230:
229
Page 231 and 232:
Tables Predictor Basis Max. hinge p
Page 233 and 234:
test true c positive positive sensi
Page 235 and 236:
235 Closed Metal bound # of hinge p
Page 237 and 238:
Chapter 5: The Conformation Explore
Page 239 and 240:
ending, which involves a rigid regi
Page 241 and 242:
lengths and angles)[89]. The equili
Page 243 and 244:
for holo as well as apo forms, but
Page 245 and 246:
queried using the “?” button. O
Page 247 and 248:
coordinate. This phenomenon is know
Page 249 and 250:
! At the end of each equilibration
Page 251 and 252:
aborted. This marked the correspond
Page 253 and 254:
sRMSD has a major limitation that m
Page 255 and 256:
with respect to the starting struct
Page 257 and 258:
generated structure with respect to
Page 259 and 260:
Ribose Binding Protein (RBP) As des
Page 261 and 262:
strikingly similar both to the holo
Page 263 and 264:
energy minimization. The minimizati
Page 265 and 266:
Figures Figure 5.1: Assignment of r
Page 267 and 268:
Figure 5.3: Results for glutamine b
Page 269 and 270:
Figure 5.4: Results for biotin carb
Page 271 and 272:
Figure 5.6: Results for Adenylate K
Page 273 and 274:
close to the holo. The structure wi
Page 275 and 276:
Table 5.1: MolMovDB ID, PDB ID, fre
Page 277 and 278:
with sRMSD; however its main utilit
Page 279 and 280:
morph server, and left confident th
Page 281 and 282:
9. M Gerstein RJ, T Johnson, J Tsai
Page 283 and 284:
28. Wriggers W, Schulten K: Protein
Page 285 and 286:
45. Dumontier M, Yao R, Feldman HJ,
Page 287 and 288:
62. Thorpe MF, D.J. Jacobs, M.V. Ch
Page 289 and 290:
81. Winn MD, Isupov MN, Murshudov G
Page 291 and 292:
98. Morris GM, Goodsell DS, Hallida
Page 293 and 294:
115. Miyazawa T: Normal Vibrations
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