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References 1. Taverna DM, Goldstein RA: Why are proteins marginally stable? Proteins 2002, 46(1):105-109. 2. Efficient Mo nte-Carlo Simulation of Proteins [robotics.stanford.edu/~itayl/mcs/] 3. Gerstein M, Krebs W: A database of macromolecular motions. Nucleic Acids Res 1998, 26(18):4280-4290. 4. Murzin AG, Brenner SE, Hubbard T, Chothia C: SCOP: a structural classification of proteins database for the investigation of sequences and structures. J Mol Biol 1995, 247(4):536-540. 5. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE: The Protein Data Bank. Nucleic Acids Res 2000, 28(1):235-242. 6. Pearl F, Todd A, Sillitoe I, Dibley M, Redfern O, Lewis T, Bennett C, Marsden R, Grant A, Lee D et al: The CATH Domain Structure Database and related resources Gene3D and DHS provide comprehensive domain family information for genome analysis. Nucleic Acids Res 2005, 33(Database issue):D247-251. 7. Janin J, Wodak SJ: Structural domains in proteins and their role in the dynamics of protein function. Prog Biophys Mol Biol 1983, 42(1):21-78. 8. Gerstein M, Lesk AM, Chothia C: Structural mechanisms for domain movements in proteins. Biochemistry 1994, 33(22):6739-6749. 280
9. M Gerstein RJ, T Johnson, J Tsai, W Krebs: Studying Macromolecular Motions in a Database Framework: from Structure to Sequence. Rigidity Theory and Applications 1999:401-442. 10. Krebs WG, Alexandrov V, Wilson CA, Echols N, Yu H, Gerstein M: Normal mode analysis of macromolecular motions in a database framework: developing mode concentration as a useful classifying statistic. Proteins 2002, 48(4):682-695. 11. Camon E, Magrane M, Barrell D, Lee V, Dimmer E, Maslen J, Binns D, Harte N, Lopez R, Apweiler R: The Gene Ontology Annotation (GOA) Database: sharing knowledge in Uniprot with Gene Ontology. Nucleic Acids Res 2004, 32(Database issue):D262-266. 12. Wells S, Menor, S., Hespenheide, B.M. and Thorpe, M.F: Physical Biology 2005, 2:S127. 13. Samuel Flores EY, Nicholas Carriero, and Mark Gerstein: submitted 2005. 14. Keating K, Flores, S., Kuhn, L., Gerstein, M.: in preparation 2005. 15. Jacobs DJ, Rader AJ, Kuhn LA, Thorpe MF: Protein flexibility predictions using graph theory. Proteins 2001, 44(2):150-165. 16. B. Brooks RB, B. Olafson, D. States, S. Swaminathan, and M. Karplus: CHARMM: A Program for Macromolecular Energy, Minimization, and Dynamics Calculations. Comp Chem 1983, 4:87-217. 17. Lindahl E, Hess B, van der Spoel D: GROMACS: A package for molecular simulation and trajectory analysis. J Mol Mod 2001, 7:306-317. 18. Rader AJ, Hespenheide BM, Kuhn LA, Thorpe MF: Protein unfolding: rigidity lost. Proc Natl Acad Sci U S A 2002, 99(6):3540-3545. 281
Page 1 and 2:
Abstract Flexibility and domain dyn
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Flexibility and domain dynamics of
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Table of Contents Table of figures
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Can hinges be predicted by a simple
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FlexOracle (FO1, FO1M, FO) 176 Defi
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Conclusions 279 References 281 Tabl
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Table of tables Table 2.1: Amino ac
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Acknowledgements Committee: Mark Ge
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Introduction The problem of predict
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Ab initio methods attempt to model
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potential for simplification, motio
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Chapter 1: The molecular motions da
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MolMovDB is a resource for studying
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voids in proteins, helix-helix pack
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A full-feature version of FIRST5 wi
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gradual transition from the initial
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energy. Thus the first residue list
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activity, DNA-directed DNA polymera
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homology table to an entry in the C
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Highlight active sites from the CSA
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Figure 1.3: Conformational change a
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particular, we found that hinges te
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and the holo. In this case it is po
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Our molecular motions database serv
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We first made a simple GOR(Garnier-
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The morph trajectory was sterically
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protein in the Jmol window to his/h
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of the hinge was otherwise unclear,
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Catalytic Sites Atlas (nonredundant
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! ! ! ! ! h c = number of times res
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! ! ! ! µ h " d c D # H . It is eq
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As mentioned earlier, the fact that
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STRIDE[50] recognizes secondary str
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! ! alignment at this position[24,
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To test this idea, we decided to ca
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GOR method is useful for predicting
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! HI active"site (i) as 0.4 for res
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lowered, and the area under the cur
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would be preferable to the other, o
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Discussion Correlations were found
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Sequence in the immediate neighborh
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Figures p-value 0.5 0.25 0 .01 PHE
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Figure 2.3: Secondary structure Res
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Figure 2.5: Conservation: full set
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Figure 2.7: Solvent accessible surf
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completely random predictor, with a
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samples 1800 1600 1400 1200 1000 80
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m = distance from nearest residues
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Hinge All Hinge Residues residues R
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in hinge residues HI p-value 1 277
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Total resid. in Hinge Atlas 54839 H
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Chi- Computer annotated set Hinge A
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BMC: Bioinformatics, we present the
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hinges. Kundu et al. use the lowest
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Domains can move relative to each o
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The quantity ! E(i) represents the
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Identification of local minima As w
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compute FoldX_energy (stability of
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energy element of each cluster was
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At least two sets of atomic coordin
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! FN (false negatives): Number of r
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We observed qualitatively (figures
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pairs of structures used to generat
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coordinate set does not significant
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The LIR family is composed of eight
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CaM is a major calcium-binding prot
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The results of running FlexOracle a
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Figure 3.2: Results for individual
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a. 139
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Figure 3.3: Folylpolyglutamate Synt
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. c. 143
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a. 145
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Figure 3.5: Lir-1 (closed) Morph ID
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. c. 149
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a. 151
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Figure 3.7: Ribose binding protein
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. c. 155
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Tables GNM 157 Single-cut predictor
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Chapter 4: HingeMaster: normal mode
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The problem of hinge prediction is
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Translation Libration Screw Motion
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! [ K] = [ U] " [ ] 2 [ U] T Where
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generated one ROC curve for each mo
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! ! 1 (l " k) A(k,l) = 2 % ' $ C(i,
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however, since we found that the Co
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A key part of this analysis involve
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describe its net vibrational displa
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! Because it cuts the backbone at t
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! ! x HingeMaster = x" # y . [6] 2
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different values of ! " * and gener
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manually select domains and color p
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E. coli resides in the periplasmic
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The results for this protein are sh
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esidues (62). As is customary we al
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FlexOracle (FO) The FlexOracle algo
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extra breakpoints based on visual i
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hinge, it is usually predicted by a
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Supplementary methods Statistical t
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! ! ! eigenvector. The resulting re
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TNC induces a conformational change
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possibility that unexpected results
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UDP-N-acetylglucosamine enolpyruvyl
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predicted both hinges, again with m
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Most predictors (including FO, Ston
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FO, hNMb, and hNMd were completely
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HingeMaster makes a strong predicti
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Figure 4.2: ROC curves for HingeMas
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Figure 4.3: HingeMaster results for
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d. e. 219
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221
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Figure 4.5: Human lactoferrin (open
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Figure 4.6: Troponin C (TNC) Morph
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Figure 4.7: Calmodulin, calcium fre
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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: morph server, and left confident th
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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