Table of Contents- Part 1: Generating and inferring structures - 1 Ab initio protein structure prediction- 1.1 Introduction- 1.2 Energy functions- 1.2.1 Physics-based energy functions- 1.2.2 Knowledge-based energy function combined with fragments- 1.3 Conformational search methods- 1.3.1 Monte Carlo simulations- 1.3.2 Molecular dynamics- 1.3.3 Genetic algorithm- 1.3.4 Mathematical optimization- 1.4 Model selection- 1.4.1 Physics-based energy function- 1.4.2 Knowledge-based energy function- 1.4.3 Sequence-structure compatibility function- 1.4.4 Clustering of decoy structure- 1.5 Remarks and discussion- 2 Fold Recognition - 2.1 Introduction- 2.1.1 The importance of blind trials: the CASP competition- 2.1.2 Ab initio structure prediction versus homology modelling- 2.1.3 The limits of fold space- 2.1.4 A note on terminology: threading and fold recognition - 2.2 Threading- 2.2.1 Knowledge-based potentials- 2.2.2 Finding an alignment- 2.2.3 Heuristics for alignment- 2.3 Remote homology detection without threading- 2.3.1 Using predicted structural features- 2.3.2 Sequence profiles and hidden Markov models- 2.3.3 Fold Classification and Support Vector Machines- 2.3.4 Consensus approaches- 2.3.5 Traversing the homology network- 2.4 Alignment accuracy, model quality and statistical significance- 2.4.1 Algorithms for alignment generation and assessment- 2.4.2 Estimation of statistical significance- 2.5 Tools for fold recognition on the web- 2.6 The future- 3 Comparative protein structure modelling- 3.1 Introduction- 3.1.1 Structure determines function- 3.1.2 Sequences, structures, structural genomics- 3.1.3 Approaches to protein structure prediction- 3.2 Steps in comparative protein structure modelling- 3.2.1 Searching for structures related to the target sequence- 3.2.2 Selecting templates- 3.2.3 Sequence to structure alignment- 3.2.4 Model building- 3.2.5 Model evaluation- 3.3 Performance of comparative modelling- 3.3.1 Accuracy of methods- 3.3.2 Errors in comparative models- 3.4 Applications of comparative modelling- 3.4.1 Modelling of individual proteins- 3.4.2 Comparative modelling and the Protein Structure Initiative- 3.5 Summary- 4 Membrane protein structure prediction- 4.1 Introduction- 4.2 Structural classes- 4.2.1 Alpha-helical bundles- 4.2.2 Beta-barrels- 4.3 Membrane proteins are difficult to crystallise- 4.4 Databases- 4.5 Multiple sequence alignments- 4.6 Transmembrane protein topology prediction- 4.6.1 Alpha-helical proteins- 4.6.2 Beta-barrel proteins- 4.6.3 Whole genome analysis- 4.6.4 Data sets, homology, accuracy and cross-validation- 4.7 3D structure prediction- 4.8 Future developments- 5 Bioinformatics approaches to the structure and function of intrinsically disordered proteins- 5.1 The concept of protein disorder- 5.2 Sequence features of IDPs- 5.2.1 The unusual amino acid composition of IDPs- 5.2.2 Sequence patterns of IDPs- 5.2.3 Low sequence complexity and disorder- 5.3 Prediction of disorder- 5.3.1 Prediction of low-complexity regions- 5.3.2 Charge-hydropathy plot- 5.3.3 Propensity-based predictors- 5.3.4 Predictors based on the lack of secondary structure- 5.3.5 Machine learning algorithms- 5.3.6 Prediction based on contact potentials- 5.3.7 A reduced alphabet suffices to predict disorder- 5.3.8 Comparison of disorder prediction methods...
EAN/ISBN : 9781402090585
Publisher(s): Springer Netherlands
Format: ePub/PDF

Author(s): Rigden, Daniel John