From Protein Structure to Function with Bioinformatics

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 Modelbuilding 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 5.4 Functional classification of IDPs 5.4.1 Gene Ontology-based functional classification of IDPs 5.4.2 Classification of IDPs based on their mechanism of action 5.4.3 Function-related structural elements in IDPs 5.5 Prediction of the function of IDPs 5.5.1 Correlation of disorder pattern and function 5.5.2 Predicting short recognition motifs in IDRs 5.5.3 Prediction of MoRFs 5.5.4 Combination of information on sequence and disorder: