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Part III: Protein Structure Alignment and Assessment
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Part III: Protein Structure Alignment and Assessment
by Min Li, Jianxin Wang, Yi Pan
Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics
Cover
Series
Title Page
Copyright
Preface
Contributors
Part I: From Protein Sequence to Structure
Chapter 1: Emphasizing The Role of Proteins in Construction of the Developmental Genetic Toolkit in Plants
1.1 Introduction
1.2 Evolutionary Developmental (Evo-Devo) Roles in Embryogenesis of Plants (in Developmental Plant Genetic Toolkit Formation)
1.3 Phases in Embryogenesis in Arabidopsis Thaliana
1.4 Analysis
1.5 Conclusions
References
Bibliography
Chapter 2: Protein Sequence Motif Information Discovery
2.1 Introduction
2.2 Granule Computing Approaches
2.3 Experimental Setup
2.4 Protein Sequence Motif Information Discovered by FGK Model
References
Chapter 3: Identifying Calcium Binding Sites in Proteins
3.1 Introduction
3.2 Methods
3.3 Results and Discussion
3.4 Conclusion
References
Chapter 4: Review of Imbalanced Data Learning for Protein Methylation Prediction
4.1 Introduction
4.2 Protein and Methylation
4.3 Related Works on Methylation Prediction
4.4 Conclusion
Acknowledgments
References
Chapter 5: Analysis and Prediction of Protein Posttranslational Modification Sites
5.1 Introduction
5.2 Musite: A Machine Learning Approach
5.3 Musite Implementation
5.4 Summary
Acknowledgments
References
Part II: Protein Analysis and Prediction
Chapter 6: Protein Local Structure Prediction
6.1 Introduction
6.2 Structural Cluster Approach
6.3 Sequence Cluster Approach
6.4 Support Vector Machines for Local Protein Structure Prediction
6.5 Clustering Support Vector Machines for Local Protein Structure Prediction
6.6 Experimental Results
References
Chapter 7: Protein Structural Boundary Prediction
7.1 Introduction
7.2 Background
7.3 New Binary Classifiers for Protein Structural Boundary Prediction
7.4 Conclusion
References
Chapter 8: Prediction of RNA Binding Sites in Proteins
8.1 Introduction
8.2 Background
8.3 Framework of Prediction
8.4 Description Features of Protein RNA Binding Sites
8.5 Existing Methods
8.6 Feature Analysis and Comparison Study
8.7 Conclusion
Acknowledgments
References
Chapter 9: Algorithmic Frameworks for Protein Disulfide Connectivity Determination
9.1 Introduction
9.2 Determining Disulfide Bonds from Sequence Information: Formulations, Features, and Algorithmic Frameworks
9.3 Algorithmic Methods for Determining Disulfide Bonds Using Mass Spectrometry
9.4 Experimental Results
9.5 Conclusions and Future Directions
Acknowledgments
References
Chapter 10: Protein Contact Order Prediction: Update
10.1 Introduction
10.2 Correlated protein properties
10.3 Other contact measurements
10.4 Contact order calculation
10.5 Contact order prediction by homology
10.6 Contact order prediction from sequence
10.7 The public contact order web server
10.8 Conclusions
References
Chapter 11: Progress in Prediction of Oxidation States of Cysteines via Computational Approaches
11.1 Introduction
11.2 Survey of Previous Efforts to Predict Bonding State of Cysteine Residues on Protein Via Computational Approaches
11.3 Summary
References
Chapter 12: Computational Methods in CryoElectron Microscopy 3D Structure Reconstruction
12.1 Introduction
12.2 Iterative image reconstruction methods
12.3 Adaptive simultaneous algebraic reconstruction technique (ASART)
12.4 Multilevel parallel strategy for iterative reconstruction algorithm
12.5 Experimental results and discussion
12.6 Summary
Acknowledgments
References
Part III: Protein Structure Alignment and Assessment
Chapter 13: Fundamentals of Protein Structure Alignment
13.1 Introduction
13.2 Biological Motivation of Protein Structure Alignment
13.3 Mathematical Frameworks
13.4 More Recent Advances with Database Queries
References
Chapter 14: Discovering 3D Protein Structures for Optimal Structure Alignment
14.1 Introduction
14.2 Protein Structure
14.3 Protein Databases
14.4 Vector Space Model
14.5 Suffix Trees
14.6 Indexing 3D Protein Structures
14.7 Protein Similarity Algorithm
14.8 Summary
References
Chapter 15: Algorithmic Methodologies for Discovery of Nonsequential Protein Structure Similarities
15.1 Introduction
15.2 Structural Alignment
15.3 Global Sequence Order–Independent Structural Alignment
15.4 Local Sequence Order–Independent Structural Alignment
15.5 Conclusion
Acknowledgments
References
Chapter 16: Fractal Related Methods for Predicting Protein Structure Classes and Functions
16.1 Introduction
16.2 Methods
16.3 Results and conclusions
Acknowledgment
References
Chapter 17: Protein Tertiary Model Assessment
17.1 Introduction
17.2 Overview of Protein Model Assessment
17.3 Design and Method
17.4 Implementation Using Svm
17.5 Implementation Using IFID3
17.6 Conclusion
References
Bibliography
Part IV: Protein–Protein Analysis of Biological Networks
Chapter 18: Network Algorithms For Protein Interactions
18.1 Introduction
18.2 Optimization approaches to clustering
18.3 Hierarchical algorithms
18.4 Features of PPI networks
18.5 Implementation of hierarchical methods
18.6 Conclusion
References
Chapter 19: Identifying Protein Complexes from Protein–Protein Interaction Networks
19.1 Introduction
19.2 Density-Based and Local Search Methods
19.3 Hierarchical Clustering Methods
19.4 Finding Overlapping Clusters
19.5 Identification of Protein Complexes by Integrating Multiple Biological Sources
19.6 Identifying Protein Complexes From Dynamic PPI Network
19.7 Challenges and Future Research
References
Chapter 20: Protein Functional Module Analysis With Protein–Protein Interaction (PPI) Networks
20.1 Introduction
20.2 Properties of PPI Networks
20.3 Previous Module Detection Approaches
20.4 Weighted Graph Model of Protein Interaction Networks
20.5 Theories and Methods
20.6 Experimental Results
20.7 Conclusion
References
Chapter 21: Efficient Alignments of Metabolic Networks with Bounded Treewidth
21.1 Introduction
21.2 An overview of metabolic network alignment and mining approaches
21.3 Generalized Network Alignment Problem
21.4 A generalized dynamic programming algorithm
21.5 Predicting pathway holes and resolving enzyme ambiguity
References
Chapter 22: Protein–protein Interaction Network Alignment: Algorithms and Tools
22.1 Introduction
22.2 Preliminaries
22.3 METHODS (Point 5)
22.4 Coarse-Grain Comparison
22.5 Concluding Remarks
References
Part V: Application of Protein Bioinformatics
Chapter 23: Protein-Related Drug Activity Comparison Using Support Vector Machines
23.1 Introduction
23.2 Related Studies for Pyrimidines Drug Activity Comparison
23.3 Feature Granules and Hierarchical Kernel Design
23.4 Experimental Results for Different Machine Learning Models
23.5 Summary
References
Chapter 24: Finding repetitions in biological networks: challenges, trends, and applications
24.1 Introduction
24.2 The Biological Networks Domain
24.3 Problem Formulation
24.4 Methods
24.5 Concluding Remarks
References
Chapter 25: MeTaDoR: Online Resource and Prediction Server for Membrane Targeting Peripheral Proteins
25.1 Introduction
25.2 Resource Content
25.3 Summary and Conclusion
Acknowledgment
References
Chapter 26: Biological networks–based analysis of gene expression signatures*
26.1 Introduction
26.2 Gene expression signatures
26.3 Biological Network–based identification of gene expression signatures
26.4 Biological Network–based integration of gene expression signatures
26.5 Discussion and Conclusion
References
Index
Series
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Chapter 13: Fundamentals of Protein Structure Alignment
Part III
Protein Structure Alignment and Assessment
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