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9 Protein Dynamics: From Structure to Function 2399.4.3 CONCOORDCONCOORD (de Groot et al. 1997) uses a geometry-based approach to predictprotein flexibility. The three-dimensional structure of a protein is determined byvarious interactions such as covalent bonds, hydrogen bonds and non-polar interactions.Most of these interactions remain intact during functionally relevantconformational changes. This notion lies at the heart of the CONCOORD simulationmethod: based on an input structure, alternative structures are generated thatshare the large majority of interactions found in the original configuration. To thisend, in the first step of a CONCOORD simulation (Fig. 9.11) interactions in asingle input structure are analyzed and turned into geometrical constraints,mainly distance constraints with upper and lower bounds for atomic distances butalso angle constraints and information about planar and chiral groups. This geometricaldescription of the structure can be compared to a construction plan of theprotein. In the second step, starting from random atomic coordinates, the structureis iteratively rebuilt based on the predefined construction plan, commonlyseveral hundreds of times. As each run starts from random coordinates, themethod does not suffer from sampling problems like MD simulations and theresulting ensemble covers the whole conformational space that is available withinthe predefined constraints. However, the method does not provide informationabout the path between two conformational substates or about timescales andenergies (Fig. 9.12).9.4.3.1 ApplicationsCONCOORD and the newly developed extension tCONCOORD (t stands fortransition) (Seeliger et al. 2007) have been applied to diverse proteins. Adenylatekinase displays a distinct domain-closing motion upon binding to its substrate(ATP/AMP) or an inhibitor (see Fig. 9.13 left) with a C α-RMSD of 7.6 Åbetween the ligand-bound and the ligand-free conformation. Two tCONCOORDsimulations were carried out using a closed conformation (PDB 1AKE) as input.In one simulation the ligand (Ap 5A) was removed. Fig. 9.13 (right) shows theresult of a principal components analysis (PCA) applied to the experimentalstructures. The first eigenvector (x-axis) corresponds to the domain-openingmotion indicated by the arrow in Fig. 9.13 (left). Every dot in the plot representsa single structure. Red dots represent the ensemble that has been generated usingthe closed conformation of adenylate kinase without ligand as input. Green dotsrepresent the ensemble that has been generated using the ligand-bound structureas input. Whereas the simulation with inhibitor basically samples closed conformationsaround the ligand-bound state, the ligand-free simulation samples both,open and closed conformations, thereby reaching the experimentally determinedopen conformations with RMSD’s of 2.4, 2.6, and 3.1 Å for 1DVR, 1AK2, and4AKE, respectively. In structure-based drug design, often the reverse problem,
240 M.B. Kubitzki et al.Fig. 9.11 Schematic representation of the CONCOORD method for generating structure ensemblesfrom a single input structure. In a first step (program dist) a single input structure is analyzedand turned into a geometric description of the protein. In a second step (program disco) the structureis rebuilt based on the predefined constraints, starting from random coordinates
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Daniel John RigdenEditorFrom Protei
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ContentsSection I Generating and In
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Contentsxi4.2.1 Alpha-Helical Bundl
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Contentsxiii7.4.2 Predicting Bindin
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Contentsxv12.3 Accuracy and Added V
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4 J. Lee et al.about 5.3 million pr
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6 J. Lee et al.Table 1.1 A list of
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8 J. Lee et al.2000; Sorin and Pand
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10 J. Lee et al.Although most knowl
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12 J. Lee et al.Fig. 1.3 Two exampl
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14 J. Lee et al.rugged containing m
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16 J. Lee et al.1.3.4 Mathematical
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18 J. Lee et al.potentials, each re
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20 J. Lee et al.viewpoint of struct
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22 J. Lee et al.Hsieh MJ, Luo R (20
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24 J. Lee et al.Sippl MJ (1990) Cal
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Chapter 2Fold RecognitionLawrence A
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2 Fold Recognition 29It has long be
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2 Fold Recognition 31Fig. 2.2 Graph
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2 Fold Recognition 33distribute the
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2 Fold Recognition 35Table 2.1 (a)
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2 Fold Recognition 37dependent on t
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2 Fold Recognition 39based on the r
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2 Fold Recognition 41The idea of co
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2 Fold Recognition 43query sequence
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2 Fold Recognition 452.3.4 Consensu
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2 Fold Recognition 472.4 Alignment
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2 Fold Recognition 49statistical me
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2 Fold Recognition 51methods (e.g.
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2 Fold Recognition 53Berman HM, Wes
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2 Fold Recognition 55Tress ML, Jone
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58 A. Fiserwith more than 50% seque
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60 A. FiserIn contrast to ab initio
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62 A. FiserTable 3.1 (continued)SWI
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64 A. Fiserbetween fold recognition
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66 A. FiserFig. 3.1 Comparing accur
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68 A. Fiserinto conserved core regi
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70 A. Fiseralignments are built and
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72 A. Fiserfold, such as the hyperv
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74 A. Fiserfunctions delivers impro
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76 A. Fiserfrom efforts of genome s
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78 A. FiserA rigorous statistical e
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80 A. Fiser(Clore et al. 1993). Cha
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82 A. FiserImproved and new methods
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84 A. FiserClaessens M, Van Cutsem
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86 A. FiserHavel TF, Snow ME (1991)
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88 A. FiserPetrey D, Xiang Z, Tang
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90 A. Fiservan Vlijmen HW, Karplus
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92 T. Nugent and D.T. Jones4.2 Stru
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94 T. Nugent and D.T. JonesFig. 4.2
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96 T. Nugent and D.T. JonesTable 4.
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98 T. Nugent and D.T. Jones2000), d
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100 T. Nugent and D.T. JonesTable 4
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102 T. Nugent and D.T. JonesFig. 4.
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104 T. Nugent and D.T. JonesFig. 4.
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106 T. Nugent and D.T. Jonessubdivi
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108 T. Nugent and D.T. Jonescomplex
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110 T. Nugent and D.T. JonesMartell
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Chapter 5Bioinformatics Approaches
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5 Structure and Function of Intrins
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Chapter 6Function Diversity Within
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6 Function Diversity Within Folds a
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Chapter 7Predicting Protein Functio
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7 Predicting Protein Function from
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Table 7.1 Online resources and tool
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Chapter 83D MotifsElaine C. Meng, B
Page 196 and 197: 8 3D Motifs 189clustering similar s
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Page 202 and 203: 8 3D Motifs 195Table 8.1 (continued
Page 204 and 205: 8 3D Motifs 1978.3.1 User-Defined M
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Page 210 and 211: 8 3D Motifs 203In addition to SITE
Page 212 and 213: 8 3D Motifs 205folds; they shared a
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Page 216 and 217: 8 3D Motifs 209Table 8.3 Web server
Page 218 and 219: 8 3D Motifs 211its greater overall
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Page 222 and 223: 8 3D Motifs 215Ivanisenko VA, Pintu
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Page 258 and 259: 252 R.A. LaskowskiConsequently, man
Page 260 and 261: 254 R.A. Laskowskiucla.edu, and Pro
Page 262 and 263: 256 R.A. LaskowskiFig. 10.2 Gene On
Page 264 and 265: 258 R.A. Laskowski10.2.5 Protein In
Page 266 and 267: 260 R.A. LaskowskiFig. 10.4 Schemat
Page 268 and 269: 262 R.A. Laskowskiaccessibility and
Page 270 and 271: 264 R.A. LaskowskiFig. 10.6 A ligan
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Page 274 and 275: 268 R.A. LaskowskiFig. 10.8 Example
Page 276 and 277: 270 R.A. LaskowskiReferencesAltschu
Page 278 and 279: 272 R.A. LaskowskiXenarios I, Salwi
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290 J.D. Watson and J.M. ThorntonKi
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Chapter 12Prediction of Protein Fun
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12 Prediction of Protein Function f
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320 IndexConsensus templates, 205Co
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322 IndexLigand-binding templates,
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324 IndexStructure-function linkage
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