Table of ContentsnPart 1: Generating and inferring structures nn1 Ab initio protein structure predictionn1.1 Introductionn1.2 Energy functionsn1.2.1 Physics-based energy functionsn1.2.2 Knowledge-based energy function combined with fragmentsn1.3 Conformational search methodsn1.3.1 Monte Carlo simulationsn1.3.2 Molecular dynamicsn1.3.3 Genetic algorithmn1.3.4 Mathematical optimizationn1.4 Model selectionn1.4.1 Physics-based energy functionn1.4.2 Knowledge-based energy functionn1.4.3 Sequence-structure compatibility functionn1.4.4 Clustering of decoy structuren1.5 Remarks and discussionnn2 Fold Recognition n2.1 Introductionn2.1.1 The importance of blind trials: the CASP competitionn2.1.2 Ab initio structure prediction versus homology modellingn2.1.3 The limits of fold spacen2.1.4 A note on terminology: 'threading' and 'fold recognition'n2.2 Threadingn2.2.1 Knowledge-based potentialsn2.2.2 Finding an alignmentn2.2.3 Heuristics for alignmentn2.3 Remote homology detection without threadingn2.3.1 Using predicted structural featuresn2.3.2 Sequence profiles and hidden Markov modelsn2.3.3 Fold Classification and Support Vector Machinesn2.3.4 Consensus approachesn2.3.5 Traversing the homology networkn2.4 Alignment accuracy, model quality and statistical significancen2.4.1 Algorithms for alignment generation and assessmentn2.4.2 Estimation of statistical significancen2.5 Tools for fold recognition on the webn2.6 The futurenn3 Comparative protein structure modellingn3.1 Introductionn3.1.1 Structure determines functionn3.1.2 Sequences, structures, structural genomicsn3.1.3 Approaches to protein structure predictionn3.2 Steps in comparative protein structure modellingn3.2.1 Searching for structures related to the target sequencen3.2.2 Selecting templatesn3.2.3 Sequence to structure alignmentn3.2.4 Modelbuildingn3.2.5 Model evaluationn3.3 Performance of comparative modellingn3.3.1 Accuracy of methodsn3.3.2 Errors in comparative modelsn3.4 Applications of comparative modellingn3.4.1 Modelling of individual proteinsn3.4.2 Comparative modelling and the Protein Structure Initiativen3.5 Summarynn4 Membrane protein structure predictionn4.1 Introductionn4.2 Structural classesn4.2.1 Alpha-helical bundlesn4.2.2 Beta-barrelsn4.3 Membrane proteins are difficult to crystallisen4.4 Databasesn4.5 Multiple sequence alignmentsn4.6 Transmembrane protein topology predictionn4.6.1 Alpha-helical proteinsn4.6.2 Beta-barrel proteinsn4.6.3 Whole genome analysisn4.6.4 Data sets, homology, accuracy and cross-validationn4.7 3D structure predictionn4.8 Future developmentsnn5 Bioinformatics approaches to the structure and function of intrinsically disordered proteinsn5.1 The concept of protein disordern5.2 Sequence features of IDPsn5.2.1 The unusual amino acid composition of IDPsn5.2.2 Sequence patterns of IDPsn5.2.3 Low sequence complexity and disordern5.3 Prediction of disordern5.3.1 Prediction of low-complexity regionsn5.3.2 Charge-hydropathy plotn5.3.3 Propensity-based predictorsn5.3.4 Predictors based on the lack of secondary structuren5.3.5 Machine learning algorithmsn5.3.6 Prediction based on contact potentialsn5.3.7 A reduced alphabet suffices to predict disordern5.3.8 Comparison of disorder prediction methodsn5.4 Functional classification of IDPsn5.4.1 Gene Ontology-based functional classification of IDPsn5.4.2 Classification of IDPs based on their mechanism of actionn5.4.3 Function-related structural elements in IDPsn5.5 Prediction of the function of IDPsn5.5.1 Correlation of disorder pattern and functionn5.5.2 Predicting short recognition motifs in IDRsn5.5.3 Prediction of MoRFsn5.5.4 Combination of information on sequence and disorder:
Proteins lie at the heart of almost all biological processes and have an incredibly wide range of activities. Central to the function of all proteins is their ability to adopt, stably or sometimes transiently, structures that allow for interaction with other molecules. An understanding of the structure of a protein can therefore lead us to a much improved picture of its molecular function. This realisation has been a prime motivation of recent Structural Genomics projects, involving large-scale experimental determination of protein structures, often those of proteins about which little is known of function. These initiatives have, in turn, stimulated the massive development of novel methods for prediction of protein function from structure. Since model structures may also take advantage of new function prediction algorithms, the first part of the book deals with the various ways in which protein structures may be predicted or inferred, including specific treatment of membrane and intrinsically disordered proteins. A detailed consideration of current structure-based function prediction methodologies forms the second part of this book, which concludes with two chapters, focusing specifically on case studies, designed to illustrate the real-world application of these methods. With bang up-to-date texts from world experts, and abundant links to publicly available resources, this book will be invaluable to anyone who studies proteins and the endlessly fascinating relationship between their structure and function.
Comprehensively covers all recent developments in structure-based function prediction of proteins
Contains abundant links to publicly available resources
Genuinely world class roster of authors
Includes full coverage of techniques to generate and infer model protein structures
Separate chapters of case studies illustrating current practice in structure-based function prediction