From Protein Structure to Function with Bioinformatics.pdf

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Contentsxi4.2.1 Alpha-Helical Bundles ......................................................... 924.2.2 Beta-Barrels .......................................................................... 924.3 Membrane Proteins Are Difficult to Crystallise ............................... 944.4 Databases .......................................................................................... 944.5 Multiple Sequence Alignments ......................................................... 964.6 Transmembrane Protein Topology Prediction .................................. 984.6.1 Alpha-Helical Proteins .......................................................... 984.6.2 Beta-Barrel Proteins .............................................................. 1024.6.3 Whole Genome Analysis ...................................................... 1024.6.4 Data Sets, Homology, Accuracyand Cross-Validation ............................................................. 1034.7 3D Structure Prediction ..................................................................... 1054.8 Future Developments ........................................................................ 1075 Bioinformatics Approaches to the Structureand Function of Intrinsically Disordered Proteins ............................... 113Peter Tompa5.1 The Concept of Protein Disorder ...................................................... 1135.2 Sequence Features of IDPs ............................................................... 1155.2.1 The Unusual Amino AcidComposition of IDPs ............................................................ 1155.2.2 Sequence Patterns of IDPs .................................................... 1155.2.3 Low Sequence Complexity and Disorder ............................. 1165.3 Prediction of Disorder ....................................................................... 1165.3.1 Prediction of Low-Complexity Regions ............................... 1165.3.2 Charge-Hydropathy Plot ....................................................... 1175.3.3 Propensity-Based Predictors ................................................. 1175.3.4 Predictors Based on the Lackof Secondary Structure .......................................................... 1185.3.5 Machine Learning Algorithms .............................................. 1195.3.6 Prediction Based on Contact Potentials ................................ 1205.3.7 A Reduced Alphabet Sufficesto Predict Disorder ................................................................ 1215.3.8 Comparison of Disorder Prediction Methods ....................... 1225.4 Functional Classification of IDPs ..................................................... 1225.4.1 Gene Ontology-Based FunctionalClassification of IDPs ........................................................... 1225.4.2 Classification of IDPs Basedon Their Mechanism of Action ............................................. 1235.4.3 Function-Related Structural Elements in IDPs ..................... 1265.5 Prediction of the Function of IDPs ................................................... 1285.5.1 Correlation of Disorder Pattern and Function ....................... 1285.5.2 Predicting Short Recognition Motifs in IDRs ....................... 1285.5.3 Prediction of MoRFs ............................................................. 129
xiiContents5.5.4 Combination of Information on Sequenceand Disorder: Phosphorylation Sitesand CaM Binding Motifs ...................................................... 1315.5.5 Flavours of Disorder ............................................................. 1315.6 Limitations of IDP Function Prediction ............................................ 1325.6.1 Rapid Evolution of IDPs ....................................................... 1325.6.2 Sequence Independence of Functionand Fuzziness ........................................................................ 1335.6.3 Good News: Conservation and Disorder .............................. 1345.7 Conclusions ....................................................................................... 135Section II From Structures to Functions6 Function Diversity Within Folds and Superfamilies ............................. 143Benoit H. Dessailly and Christine A. Orengo6.1 Defining Function ............................................................................. 1436.2 From Fold to Function ...................................................................... 1456.2.1 Definition of a Fold ............................................................... 1456.2.2 Prediction of Function Using FoldRelationships ......................................................................... 1486.3 Function Diversity Between Homologous Proteins .......................... 1516.3.1 Definitions ............................................................................. 1516.3.2 Evolution of Protein Superfamilies ....................................... 1526.3.3 Function Divergence During Protein Evolution ................... 1546.4 Conclusion ........................................................................................ 1627 Predicting Protein Function from Surface Properties .......................... 167Nicholas J. Burgoyne and Richard M. Jackson7.1 Surface Descriptions ......................................................................... 1677.1.1 The van der Waals Surface .................................................... 1677.1.2 Molecular Surface (Solvent Excluded Surface) .................... 1687.1.3 The Solvent Accessible Surface ............................................ 1687.2 Surface Properties ............................................................................. 1697.2.1 Hydrophobicity ..................................................................... 1697.2.2 Electrostatics Properties ........................................................ 1707.2.3 Surface Conservation ............................................................ 1717.3 Function Predictions Using Surface Properties ................................ 1717.3.1 Hydrophobic Surface ............................................................ 1727.3.2 Electrostatic Surface ............................................................. 1727.3.3 Surface Conservation ............................................................ 1737.3.4 Combining Surface Propertiesfor Function Prediction ......................................................... 1747.4 Protein-Ligand Interactions .............................................................. 1747.4.1 Properties of Protein-Ligand Interactions ............................. 174
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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
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8 3D Motifs 189clustering similar s
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8 3D Motifs 191To improve the signa
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8 3D Motifs 193structures together.
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8 3D Motifs 195Table 8.1 (continued
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8 3D Motifs 1978.3.1 User-Defined M
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8 3D Motifs 199Fig. 8.3 The FFF mot
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8 3D Motifs 2018.3.2 Motif Discover
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8 3D Motifs 203In addition to SITE
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8 3D Motifs 205folds; they shared a
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8 3D Motifs 207from a training set
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8 3D Motifs 209Table 8.3 Web server
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8 3D Motifs 211its greater overall
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8 3D Motifs 213Ideally, a 3D motif
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8 3D Motifs 215Ivanisenko VA, Pintu
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Chapter 9Protein Dynamics: From Str
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9 Protein Dynamics: From Structure
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252 R.A. LaskowskiConsequently, man
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254 R.A. Laskowskiucla.edu, and Pro
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256 R.A. LaskowskiFig. 10.2 Gene On
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258 R.A. Laskowski10.2.5 Protein In
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260 R.A. LaskowskiFig. 10.4 Schemat
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262 R.A. Laskowskiaccessibility and
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264 R.A. LaskowskiFig. 10.6 A ligan
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266 R.A. LaskowskiGly97Gly99Tyr98Ty
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268 R.A. LaskowskiFig. 10.8 Example
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270 R.A. LaskowskiReferencesAltschu
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272 R.A. LaskowskiXenarios I, Salwi
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274 J.D. Watson and J.M. ThorntonTh
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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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