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El. knyga: From Protein Structure to Function with Bioinformatics

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  • Formatas: PDF+DRM
  • Išleidimo metai: 06-Apr-2017
  • Leidėjas: Springer
  • Kalba: eng
  • ISBN-13: 9789402410693
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From Protein Structure to Function with BioinformaticsDidesnis paveikslėlis
  • Formatas: PDF+DRM
  • Išleidimo metai: 06-Apr-2017
  • Leidėjas: Springer
  • Kalba: eng
  • ISBN-13: 9789402410693
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This book is about protein structural bioinformatics and how it can help understand and predict protein function. It covers structure-based methods that can assign and explain protein function based on overall folds, characteristics of protein surfaces, occurrence of small 3D motifs, protein-protein interactions and on dynamic properties. Such methods help extract maximum value from new experimental structures, but can often be applied to protein models. The book also, therefore, provides comprehensive coverage of methods for predicting or inferring protein structure, covering all structural classes from globular proteins and their membrane-resident counterparts to amyloid structures and intrinsically disordered proteins.





The book is split into two broad sections, the first covering methods to generate or infer protein structure, the second dealing with structure-based function annotation. Each chapter is written by world experts in the field. The first section covers methods ranging from traditional homology modelling and fold recognition to fragment-based ab initio methods, and includes a chapter, new for the second edition, on structure prediction using evolutionary covariance. Membrane proteins and intrinsically disordered proteins are each assigned chapters, while two new chapters deal with amyloid structures and means to predict modes of protein-protein interaction. The second section includes chapters covering functional diversity within protein folds and means to assign function based on surface properties and recurring motifs. Further chapters cover the key roles of protein dynamics in protein function and use of automated servers for function inference. The book concludes with two chapters covering case studies of structure prediction, based respectively on crystal structures and protein models, providing numerous examples of real-world usage of the methods mentioned previously.









This book is targeted at postgraduate students and academic researchers. It is most obviously of interest to protein bioinformaticians and structural biologists, but should also serve as a guide to biologists more broadly by highlighting the insights that structural bioinformatics can provide into proteins of their interest.
Part I Generating and Inferring Structures
1 Ab Initio Protein Structure Prediction
3(34)
Jooyoung Lee
Peter L. Freddolino
Yang Zhang
1.1 Introduction
4(1)
1.2 Energy Functions
5(13)
1.2.1 Physics-Based Energy Functions
7(4)
1.2.2 Knowledge-Based Energy Function Combined with Fragments
11(7)
1.3 Conformational Search Methods
18(3)
1.3.1 Monte Carlo Simulations
18(1)
1.3.2 Molecular Dynamics
19(1)
1.3.3 Genetic Algorithm
20(1)
1.3.4 Mathematical Optimization
21(1)
1.4 Model Selection
21(4)
1.4.1 Physics-Based Energy Function
22(1)
1.4.2 Knowledge-Based Energy Function
23(1)
1.4.3 Sequence-Structure Compatibility Function
24(1)
1.4.4 Clustering of Decoy Structures
25(1)
1.5 Remarks and Discussions
25(12)
References
27(10)
2 Protein Structures, Interactions and Function from Evolutionary Couplings
37(22)
Thomas A. Hopf
Debora S. Marks
2.1 Introduction
38(4)
2.2 Evolutionary Couplings from Sequence Alignments
42(4)
2.2.1 The Global Model
42(4)
2.3 Three-Dimensional Protein Structures from Evolutionary Couplings
46(6)
2.3.1 Transmembrane Proteins
48(1)
2.3.2 Protein Interactions and Complexes
49(2)
2.3.3 Conformational Plasticity and Disordered Proteins
51(1)
2.4 Predicting the Effect of Mutations
52(2)
2.5 Summary and Future Challenges
54(5)
References
55(4)
3 Fold Recognition
59(32)
Lawrence A. Kelley
3.1 Introduction
59(5)
3.1.1 The Importance of Blind Trials: The CASP Competition
60(1)
3.1.2 Ab Initio Structure Prediction Versus Homology Modelling
60(2)
3.1.3 The Limits of Fold Space
62(2)
3.2 Pushing Sequence Similarity to the Limits: The Power of Evolutionary Information
64(8)
3.2.1 The Rise of Hidden Markov Models
67(1)
3.2.2 Using Predicted Structural Features
68(2)
3.2.3 Harnessing 3D Structure to Enhance Recognition
70(1)
3.2.4 Knowledge-Based Potentials
70(2)
3.2.5 Summary
72(1)
3.3 CASP: The Great Filter
72(4)
3.3.1 The Leaders
73(1)
3.3.2 Individual Algorithms
73(2)
3.3.3 Consensus Methods
75(1)
3.4 Post-processing
76(9)
3.4.1 Choosing and Combining Candidate Models
76(3)
3.4.2 Post-processing in Practice
79(3)
3.4.3 Use of Contacts
82(3)
3.5 Tools for Fold Recognition on the Web
85(1)
3.6 The Future
86(5)
References
88(3)
4 Comparative Protein Structure Modelling
91(44)
Andras Fiser
4.1 Introduction
91(5)
4.1.1 Structure Determines Function
91(1)
4.1.2 Sequences, Structures, Structural Genomics
92(2)
4.1.3 Approaches to Protein Structure Prediction
94(2)
4.2 Steps in Comparative Protein Structure Modelling
96(20)
4.2.1 Searching for Structures Related to the Target Sequence
98(2)
4.2.2 Selecting Templates
100(2)
4.2.3 Sequence to Structure Alignment
102(1)
4.2.4 Model Building
103(11)
4.2.5 Model Evaluation
114(2)
4.3 Performance of Comparative Modelling
116(3)
4.3.1 Accuracy of Methods
116(1)
4.3.2 Errors in Comparative Models
117(2)
4.4 Applications of Comparative Modelling
119(1)
4.4.1 Modelling of Individual Proteins
119(1)
4.4.2 Comparative Modelling and the Protein Structure Initiative
119(1)
4.5 Summary
120(15)
References
121(14)
5 Advances in Computational Methods for Transmembrane Protein Structure Prediction
135(32)
Tim Nugent
David Jones
Sikander Hayat
5.1 Introduction
136(1)
5.2 Membrane Protein Structural Classes
136(3)
5.2.1 α-Helical Bundles
137(1)
5.2.2 Transmembrane β-Barrels
137(2)
5.3 Databases
139(1)
5.4 Multiple Sequence Alignments
140(1)
5.5 Transmembrane Protein Topology Prediction
141(9)
5.5.1 Early α-Helical Topology Prediction Approaches
142(1)
5.5.2 Machine Learning Approaches for α-Helical Topology Prediction
142(2)
5.5.3 Signal Peptides and Re-entrant Helices
144(1)
5.5.4 Consensus Approaches for α-Helical Topology Prediction
145(1)
5.5.5 Transmembrane β-Barrel Topology Prediction
146(1)
5.5.6 Empirical Approaches for β-Barrel Topology Prediction
147(1)
5.5.7 Machine Learning Approaches for β-Barrel Topology Prediction
148(1)
5.5.8 Consensus Approaches for β-Barrel Topology Prediction
149(1)
5.6 3D Structure Prediction
150(8)
5.6.1 Homology Modelling of α-Helical Transmembrane Proteins
150(1)
5.6.2 Homology Modelling of Transmembrane β-Barrel Proteins
151(1)
5.6.3 De Novo Modelling of α-Helical Transmembrane Proteins
152(2)
5.6.4 De Novo Modelling of Transmembrane β-Barrels
154(1)
5.6.5 Covariation-Based Approaches
154(1)
5.6.6 Evolutionary Covariation-Based Methods for De Novo Modelling of α-Helical Membrane Proteins
155(2)
5.6.7 Evolutionary Covariation-Based Methods for Transmembrane β-Barrel Structure Prediction
157(1)
5.7 Future Directions
158(9)
References
158(9)
6 Bioinformatics Approaches to the Structure and Function of Intrinsically Disordered Proteins
167(38)
Zsuzsanna Dosztanyi
Peter Tompa
6.1 The Concept of Protein Disorder
168(1)
6.2 Sequence Features of IDPs
169(2)
6.2.1 The Unusual Amino Acid Composition of IDPs
169(1)
6.2.2 Low Sequence Complexity and Disorder
169(1)
6.2.3 Flavours of Disorder
170(1)
6.3 Prediction of Disorder
171(8)
6.3.1 Charge-Hydropathy Plot
171(1)
6.3.2 Propensity-Based Predictors
171(3)
6.3.3 Prediction Based on Simplified Biophysical Models
174(1)
6.3.4 Machine Learning Algorithms
175(2)
6.3.5 Related Approaches for the Prediction of Protein Disorder
177(1)
6.3.6 Comparison of Disorder Prediction Methods
178(1)
6.4 Databases of IDPs
179(1)
6.5 Structural Features of IDPs
180(1)
6.6 Functional Classification of IDPs
181(7)
6.6.1 Gene Ontology-Based Functional Classification of IDPs
182(1)
6.6.2 Classification of IDPs Based on Their Mechanism of Action
183(2)
6.6.3 Functional Features of IDPs
185(3)
6.7 Prediction of the Function of IDPs
188(6)
6.7.1 Predicting Short Recognition Motifs in IDRs
190(1)
6.7.2 Prediction of Disordered Binding Regions/MoRFs
191(1)
6.7.3 Combination of Information on Sequence and Disorder: Phosphorylation Sites and CaM Binding Motifs
192(1)
6.7.4 Correlation of Disorder Pattern and Function
193(1)
6.8 Evolution of IDPs
194(1)
6.9 Conclusions
195(10)
References
195(10)
7 Prediction of Protein Aggregation and Amyloid Formation
205(60)
Ricardo Grana-Montes
Jordi Pujols-Pujol
Carlota Gomez-Picanyol
Salvador Ventura
7.1 Introduction
206(1)
7.2 The Physico-chemical and Structural Basis of Protein Aggregation
206(10)
7.2.1 Intrinsic Determinants of Protein Aggregation
213(1)
7.2.2 Extrinsic Determinants of Protein Aggregation
214(1)
7.2.3 Specific Sequence Stretches Drive Aggregation
214(1)
7.2.4 Structural Determinants of Amyloid-like Aggregation
215(1)
7.3 Prediction of Protein Aggregation from the Primary Sequence
216(26)
7.3.1 Phenomenological Approaches
221(4)
7.3.2 Structure-Based Approaches
225(5)
7.3.3 Consensus Methods
230(2)
7.3.4 Applications of Sequence-Based Predictors
232(10)
7.4 Prediction of Aggregation Propensity from the Tertiary Structure
242(11)
7.5 Concluding Remarks
253(12)
References
254(11)
8 Prediction of Biomolecular Complexes
265(30)
Anna Vangone
Romina Oliva
Luigi Cavallo
Alexandre M.J.J. Bonvin
8.1 Introduction
266(2)
8.2 Docking
268(7)
8.2.1 Step 1: Searching
269(1)
8.2.2 Step 2: Scoring
270(4)
8.2.3 Data-Driven Docking
274(1)
8.3 The Challenges of Docking: Flexibility and Binding Affinity
275(3)
8.3.1 Changes upon Binding: The Flexible Docking Challenge
275(1)
8.3.2 The `Perfect' Scoring Function and the Binding Affinity Problem
276(2)
8.4 Protein-Peptide Docking
278(1)
8.5 Post-docking: Interface Prediction from Docking Results and Use of Docking-Derived Contacts for Clustering and Ranking
279(4)
8.5.1 Web Tools for the Post-docking Processing
281(2)
8.6 Concluding Remarks
283(12)
References
284(11)
Part II From Structures to Functions
9 Function Diversity Within Folds and Superfamilies
295
Benoit H. Dessailly
Natalie L. Dawson
Sayoni Das
Christine A. Orengo
9.1 Defining Function
296(1)
9.2 From Fold to Function
297(6)
9.2.1 Definition of a Fold
297(3)
9.2.2 Prediction of Function Using Fold Relationships
300(3)
9.3 Function Diversity Between Homologous Proteins
303(17)
9.3.1 Definitions
303(4)
9.3.2 Evolution of Protein Superfamilies
307(1)
9.3.3 Function Divergence During Protein Evolution
308(12)
9.4 Conclusion
320
Bibliography
7
10 Function Prediction Using Patches, Pockets and Other Surface Properties 3
327(1)
Daniel J. Rigden
10.1 Definitions of Protein Surfaces
328(1)
10.2 Surface Patches
329(11)
10.2.1 Hydrophobic Patches
329(7)
10.2.2 Electrostatics
336(2)
10.2.3 Sequence Conservation
338(1)
10.2.4 Surface Atom Triplet Propensities
339(1)
10.2.5 Multiple Properties
340(1)
10.3 Pockets
340(7)
10.3.1 Geometric Descriptions of Pockets
342(1)
10.3.2 Channels and Tunnels
343(1)
10.3.3 Distinguishing Functional Pockets
344(1)
10.3.4 Predicting Ligands for Pockets
345(2)
10.4 Prediction of Catalytic Residues
347(2)
10.5 Protein-Protein Interfaces
349(1)
10.6 Other Specialised Binding Site Predictors
350(2)
10.7 Medicinal Applications
352(1)
10.8 Conclusions
353(8)
References
354(7)
11 3D Motifs
361(32)
Jerome P. Nilmeier
Elaine C. Meng
Benjamin J. Polacco
Patricia C. Babbitt
11.1 Background: Functional Annotation
362(4)
11.1.1 What Is Function?
363(1)
11.1.2 Genomics and Functional Annotation
363(2)
11.1.3 The Need for Structure-Based Methods
365(1)
11.2 3D Motif Matching Techniques
366(7)
11.2.1 What Is a 3D Motif?
366(3)
11.2.2 Historical Development of Motif Matching Methods
369(4)
11.3 Algorithmic Approaches to Motif Matching
373(5)
11.3.1 Methods Using 3D Motifs
374(1)
11.3.2 Efficiency Considerations for 3D Motifs
375(1)
11.3.3 Methods with Nonstandard Motif Information
376(1)
11.3.4 Interpretation of Results
377(1)
11.4 Methods for Deriving Motifs
378(5)
11.4.1 Literature Search and Manual Curation
379(1)
11.4.2 Annotated Sites in PDB Structures
379(1)
11.4.3 Mining for Emergent Properties
380(3)
11.5 Molecular Docking for Functional Annotation
383(2)
11.6 Discussion and Conclusions
385(8)
References
386(7)
12 Protein Dynamics: From Structure to Function
393(34)
Marcus B. Kubitzki
Bert L. de Groot
Daniel Seeliger
12.1 Molecular Dynamics Simulations
393(13)
12.1.1 Principles and Approximations
394(2)
12.1.2 Applications
396(6)
12.1.3 Limitations---Enhanced Sampling Algorithms
402(4)
12.2 Principal Component Analysis
406(3)
12.3 Collective Coordinate Sampling Algorithms
409(4)
12.3.1 Essential Dynamics
409(1)
12.3.2 TEE-REX
410(3)
12.4 Methods for Functional Mode Prediction
413(6)
12.4.1 Normal Mode Analysis
413(1)
12.4.2 Elastic Network Models
414(1)
12.4.3 CONCOORD
415(4)
12.5 Summary and Outlook
419(8)
References
420(7)
13 Integrated Servers for Structure-Informed Function Prediction
427(22)
Roman A. Laskowski
13.1 Introduction
427(4)
13.1.1 The Problem of Predicting Function from Structure
428(2)
13.1.2 Structure-Function Prediction Methods
430(1)
13.2 ProKnow
431(5)
13.2.1 Fold Matching
432(2)
13.2.2 3D Motifs
434(1)
13.2.3 Sequence Homology
434(1)
13.2.4 Sequence Motifs
434(1)
13.2.5 Protein Interactions
434(1)
13.2.6 Combining the Predictions
435(1)
13.2.7 Prediction Success
435(1)
13.3 ProFunc
436(8)
13.3.1 ProFunc's Structure-Based Methods
437(5)
13.3.2 Assessment of the Structural Methods
442(2)
13.4 Conclusion
444(5)
References
445(4)
14 Case Studies: Function Predictions of Structural Genomics Results
449(18)
James D. Watson
Roman A. Laskowski
Janet M. Thornton
14.1 Introduction
449(2)
14.2 Function Prediction Case Studies
451(5)
14.2.1 Teichman et al. (2001)
451(1)
14.2.2 Kim et al. (2003)
451(2)
14.2.3 Watson et al. (2007)
453(3)
14.2.4 Lee et al. (2011)
456(1)
14.3 Some Specific Examples
456(4)
14.3.1 Adams et al. (2007)
456(1)
14.3.2 AF0491 Protein
457(2)
14.3.3 The GxGYxYP Family
459(1)
14.4 Community Annotation
460(1)
14.5 Conclusions
461(6)
References
462(5)
15 Prediction of Protein Function from Theoretical Models
467(32)
Daniel J. Rigden
Iwona A. Cymerman
Janusz M. Bujnicki
15.1 Background
467(2)
15.2 Suitability of Protein 3D Models for Structure-Based Predictions
469(9)
15.2.1 Surface Properties
470(2)
15.2.2 Functional Sites
472(1)
15.2.3 Specific Binding Predictions
473(1)
15.2.4 Small Molecule Binding
474(2)
15.2.5 Protein-Protein Interactions
476(1)
15.2.6 Protein Model Databases
477(1)
15.3 Function Prediction Examples
478(15)
15.3.1 Fold Prediction with Fragment-Based Ab Initio Models
478(3)
15.3.2 Fold Prediction with Contact-Based Models
481(2)
15.3.3 Plasticity of Catalytic Site Residues
483(1)
15.3.4 Prediction of Ligand Specificity
484(1)
15.3.5 Prediction of Cofactor Specificity Using an Entry from a Database of Models
485(3)
15.3.6 Mutation Mapping
488(1)
15.3.7 Protein Complexes
489(1)
15.3.8 Structure Modelling of Alternatively Spliced Isoforms
490(1)
15.3.9 From Broad Function to Molecular Details
491(2)
15.4 Conclusions
493(6)
References
493(6)
Index 499
Daniel Rigden is a Reader in post-genomic bioinformatics in the Institute of Integrative Biology.  His interests span the broad relationships between protein sequences, structures and functions and how these evolve with time. As such, he applies a wide range of bioinformatics tools to diverse proteins of interest. This leads to interesting collaborations acorss the Institute and more broadly. A current prime interest is solution of crystal structures by Molecular Replacement using unconventional protein models, implemented in the program AMPLE.
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