Kihara Protein Function Prediction for Omics Era

1. Auflage 2011
ISBN: 978-94-007-0881-5
Verlag: Springer Netherlands
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 310 Seiten

Gene function annotation has been a central question in molecular biology. The importance of computational function prediction is increasing because more and more large scale biological data, including genome sequences, protein structures, protein-protein interaction data, microarray expression data, and mass spectrometry data, are awaiting biological interpretation. Traditionally when a genome is sequenced, function annotation of genes is done by homology search methods, such as BLAST or FASTA. However, since these methods are developed before the genomics era, conventional use of them is not necessarily most suitable for analyzing a large scale data. Therefore we observe emerging development of computational gene function prediction methods, which are targeted to analyze large scale data, and also those which use such omics data as additional source of function prediction. In this book, we overview this emerging exciting field. The authors have been selected from 1) those who develop novel purely computational methods 2) those who develop function prediction methods which use omics data 3) those who maintain and update data base of function annotation of particular model organisms (E. coli), which are frequently referred

Autoren/Hrsg.

Kihara, Daisuke

Inhaltsverzeichnis

1;Preface;5
2;Contents;8
3;Contributors;10
4;Computational Protein Function Prediction: Framework and Challenges;13
4.1; Introduction;13
4.2; Controlled Functional Vocabularies;15
4.2.1; Gene Ontology;16
4.2.2; MIPS Functional Catalogue;18
4.2.3; Enzyme Commission Numbers;18
4.2.4; Transport Classification (TC) System;19
4.2.5; KEGG Orthology (KO);19
4.2.6; Other Biological Ontologies;20
4.3; Definition of Functional Similarity;21
4.4; Limitations of Homology Based Function Transfer and Erroneous Database Annotations;24
4.5; Critical Assessment of Function Prediction Methods;25
4.6; Summary;26
4.7;References;27
5;Enhanced Sequence-Based Function Prediction Methods and Application to Functional Similarity Networks;30
5.1; Introduction;30
5.2; Conventional Sequence-Based Function Prediction Methods;30
5.3; Protein Function Prediction (PFP) Method;31
5.3.1; PFP Algorithm;32
5.3.2; PFP Performance and Benchmarking;34
5.4; Extended Similarity Group (ESG) Method;35
5.4.1; The ESG Algorithm;35
5.4.2; Performance of ESG;38
5.4.3; Difference between PFP and ESG;39
5.4.4; PFP and ESG Web Server;40
5.5; Structure of the Gene Functional Space;40
5.6; Summary;43
5.7;References;43
6;Gene Cluster Prediction and Its Application to Genome Annotation;46
6.1; Introduction;46
6.2; Description of Existing Techniques;48
6.2.1; Graph-Based Approach;49
6.2.2; Evolutionary Model-Based Approach;51
6.2.3; EGGS: Gene Pattern Prediction Based on Genome Context;54
6.2.4; Gene Cluster Prediction Based on a Mutable Pattern Model;56
6.2.5; Query-Based Approach;57
6.3; Experiments;59
6.3.1; Formulation and Algorithm;59
6.3.2; Demonstrative Experiments;61
6.3.2.1; Gene Cluster Prediction;61
6.3.2.2; Annotation Assignment based on Gene Cluster;61
6.3.2.3; Results;61
6.4; Summary;63
6.5;References;64
7;Functional Inference in Microbial Genomics BasedINTnl; on Large-Scale Comparative Analysis;66
7.1; Introduction;66
7.2; General Schemes in Comparative Genomics;67
7.3; Functional Inference Based on Comparative Genomics;70
7.4; Ortholog Classification Problem;72
7.5; Ortholog Databases for Microbial Comparative Genomics;76
7.6; DomClust: Hierarchical Clustering Algorithm for Ortholog Group Construction at the Domain Level;78
7.7; MBGD: Microbial Genome Database for Comparative Analysis;82
7.8; A Sample MBGD Session: Phylogenetic Pattern Analysis;86
7.9; Core Genome Analysis: What Is the Essential Part of a Set of Related Genomes?;88
7.10; CoreAligner: Multiple Genome Alignment Procedure for Identifying the Core Genome Structure;91
7.11; RECOG: Research Environment for Comparative Genomics;95
7.12; Conclusion and Future Prospects;97
7.13;References;98
8;Predicting Protein Functional Sites with Phylogenetic Motifs: Past, Present and Beyond;104
8.1; Introduction;104
8.2; The Past;105
8.2.1; The MINER Algorithm;105
8.2.2; Prediction of Functional Sites Within the NSS Protein Family;108
8.3; The Present;109
8.3.1; Residue Specific Predictions;109
8.3.2; Integrating Conservation and Evolutionary Viewpoints;111
8.3.3; The Importance of Topology;112
8.4; The Future;113
8.5; Accessibility and miniMINER;113
8.6;References;115
9;Exploiting Protein Structures to Predict Protein Functions;117
9.1; Introduction;117
9.2; Divergence of Protein Structures and Functions During Evolution;119
9.3; To What Extent Can Function Be Predicted from the Structure of the Domain;121
9.3.1; Global Structure Comparison;121
9.3.2; Assigning Functions Based on Local Structural Similarity;122
9.3.3; Methods That Search for Patterns of Conservation Without Having Functional Groups or Motifs Defined;123
9.3.4; Methods That Search for Structural Differences Between Defined Functional Groups to Identify Functional Determinants;124
9.3.4.1; The FLORA Algorithm;124
9.3.4.2; Benchmark Dataset;124
9.3.4.3; Overview of Method;125
9.3.4.4; Step 1: Identify Structurally Conserved Residues;126
9.3.4.5; Scoring Query Structures Against FLORA Template Sets for Individual Domains;127
9.3.4.6; Assessing the Performance of FLORA;127
9.3.4.7; Visualisation of Functionally Specific Positions Detected by FLORA;129
9.4; Incorporating Sequence Based Protocols with FLORA to Identify Functionally Specific Residues;130
9.5;References;132
10;Sequence Order Independent Comparison of Protein Global Backbone Structures and Local Binding Surfaces for Evolutionary and Functional Inference;134
10.1; Introduction;134
10.2; Structural Alignment;136
10.3; Global Sequence Order Independent Structural Alignment;137
10.3.1; A Fragment Assembly Based Approach to Sequence Order Independent Structural Alignment;137
10.3.2; Detecting Permuted Proteins;139
10.3.2.1; Nucleoplasmin-Core and Auxin Binding Protein;140
10.3.2.2; Beyond Circular Permutation;141
10.4; Local Sequence Order Independent Structural Alignment;142
10.4.1; Bi-partite Graph Matching Approach to Structural Alignment;143
10.4.2; Signature Pockets and Basis Set of Binding Surface for a Functional Family of Proteins;144
10.4.3; Signature Pockets of NAD Binding Proteins;146
10.5; Conclusion;148
10.6;References;149
11;Protein Binding Ligand Prediction Using Moments-Based Methods;153
11.1; Introduction;153
11.2; Pocket Surface Shape Descriptors;155
11.2.1; Spherical Harmonics;155
11.2.2; 3D Zernike Descriptors;156
11.2.3; 2D Pocket Model with Pseudo-Zernike Moments;157
11.2.4; 2D Pseudo-Zernike Moments;159
11.3; Theoretical Comparison of Moments in Shape Descriptors;159
11.4; Binding Ligand Prediction Using the Pocket Descriptors;160
11.5; Benchmark Results of Binding Ligand Prediction;162
11.6; Performance with Ligand-Free Pockets and Predicted Pockets;164
11.7; Computational Time of Pocket-Surfer;166
11.8; Pocket Comparison with Local Surface 3D Zernike Descriptors;166
11.9; Summary;168
11.10;References;168
12;Computational Methods for Predicting DNA-Binding Sites at a Genomic Scale;172
12.1; Introduction;172
12.2; Data Sources;173
12.2.1; Protein-DNA Complexes;174
12.2.2; Thermodynamics and In Vitro Experiments;174
12.2.3; Functionally Annotated Data Sets;175
12.2.4; Control Data Sets;175
12.3; Computational Techniques;175
12.4; Methods for Predicting DNA-Binding Sites;176
12.4.1; Definition of a Binding Site;176
12.4.2; Residue Propensities;177
12.4.3; Sequence-Based Prediction of DNA-Binding Sites;177
12.4.4; Can We Use Conservation Score to Predict DNA-Binding Sites;180
12.4.5; Clusters of Conserved Residues (CCRs) and Binding Hot Spots;182
12.4.6; Predicting Specificity of Protein--DNA Interaction;183
12.5; Recent Advances and Current Directions;185
12.6; Conclusion;186
12.7;References;186
13;Electrostatic Properties for Protein Functional Site Prediction;190
13.1; Introduction;190
13.2; Methods;192
13.2.1; THEMATICS;192
13.2.2; POOL;194
13.3; Discussion;195
13.3.1; What Is the Basis for the Success of THEMATICS?;195
13.3.2; Applications;198
13.3.3; Precision;198
13.3.4; Future Directions;200
13.4;References;200
14;Function Prediction of Genes: From Molecular Function to Cellular Function;204
14.1; Introduction;204
14.2; Molecular Function;205
14.2.1; Global Fold Similarity;206
14.2.2; Local Atomic Configurations;207
14.2.3; Molecular Surface Similarity;208
14.2.4; Beyond the Simple Similarity Search;209
14.2.5; Limitations of Structure Based Approaches: Protein Disorder;210
14.3; Cellular Function;211
14.3.1; Protein--Protein Interactions;211
14.3.2; Measuring Gene-Coexpression;212
14.3.3; Two Approaches in Gene-Coexpression Analyses;214
14.4; Conclusion;217
14.5;References;217
15;Predicting Gene Function Using Omics Data: From Data Preparation to Data Integration;222
15.1; Introduction;222
15.2; Omics Data Preparation;224
15.2.1; Genomics;224
15.2.2; Transcriptomics;227
15.2.3; Proteomics;228
15.2.4; Metabolomics;229
15.2.5; Phenomics;229
15.3; Computational Algorithms to Integrate Omics Data for Gene Function Prediction;230
15.3.1; Sequence-Based Algorithms for Gene Function Prediction;230
15.3.2; Non-network Based Omics Data Integration for Gene Function Prediction;231
15.3.3; Network-Based Omics Data Integration for Gene Function Prediction;232
15.4; Funckenstein, a Combined Algorithm for Omics-Based Gene Function Prediction;235
15.5; Current Limitations and Potential Improvements;238
15.5.1; Omics Data Are Not Thoroughly Used;238
15.5.2; Omics Data Sharing Is Urgent and Needs to Be Standardized;240
15.5.3; Is a Complex Model Better than a Simple Model?;241
15.5.4; Model Driven or Biology Driven?;241
15.6; Prospective of Future Directions;242
15.6.1; Non-coding RNA Function Prediction;242
15.6.2; Gene Function in a Dynamic Context;242
15.7;References;242
16;Protein Function Prediction Using Proteinx2013;Protein InteractionINTnl; Networks;250
16.1; Introduction;250
16.2; Protein--Protein Interactions;251
16.2.1; Physical Interactions;251
16.2.2; Genetic Interactions;251
16.3; Function Prediction Using ProteinProtein Interactions;252
16.3.1; Indirect Association of Protein Function;253
16.3.1.1; Functional Association Between Indirect Neighbors;253
16.3.1.2; Estimating Function Similarity Between Interacting Proteins;254
16.3.1.3; Functional Association and Experimental Assays;255
16.3.1.4; Function Prediction Using Indirect Association;257
16.3.1.5; Evaluation on Function Prediction;257
16.3.2; Prediction of Gene Ontology Functional Annotations on Multiple Species;258
16.3.2.1; Data Availability;260
16.3.2.2; Protein--Protein Interactions vs. Sequence Homology;260
16.3.2.3; Function Prediction Performance;262
16.4; Indirect Functional Association and Complex Discovery;262
16.4.1; Protein Complex Discovery;262
16.4.1.1; Protein Complexes with Limited Interactions;264
16.4.1.2; Approaches for Protein Complex Prediction;264
16.4.1.3; Modifying the Interaction Network with FS-Weight;265
16.4.1.4; A New Complex Prediction Approach;266
16.4.1.5; Performance Evaluation;267
16.4.1.6; Complex Prediction Performance;268
16.5; Improving the Reliability of Interactions;268
16.5.1; Iterative Scoring;270
16.5.2; Complex Discovery Using AdjustCDk Weighted Interactions;271
16.5.2.1; The CMC Algorithm;271
16.5.2.2; Performance Evaluation;271
16.5.3; Robustness Against Noise in the Interaction Network;272
16.6; Conclusions;272
16.7;References;274
17;KEGG and GenomeNet Resources for Predicting Protein Function from Omics Data Including KEGG PLANT Resource;278
17.1; Introduction;278
17.2; Outline of KEGG Resource;279
17.2.1; Overview of KEGG Database;279
17.2.2; KEGG Orthology (KO): Basis of Genome Annotation in KEGG;281
17.2.3; PATHWAY and BRITE: Systems Representation in KEGG;281
17.2.4; Color Objects in KEGG Pathways and BRITE Hierarchies;282
17.2.5; KEGG REACTION: Chemical Structure Transformation Information in KEGG;282
17.3; KEGG Resources and GenomeNet Bioinformatics Tools for Predicting Protein Function;283
17.3.1; KEGG EDRUG and KEGG PLANT Resource;283
17.3.1.1; Overview of KEGG EDRUG and KEGG PLANT Resource;283
17.3.1.2; Pathway Maps of Plant Secondary Metabolite Biosynthesis;283
17.3.1.3; KEGG GENES and EGENES of Plants: Sequence Information of Plant Species;285
17.3.1.4; Classification of Plant Secondary Metabolites;286
17.3.1.5; KEGG EDRUG Database;286
17.3.1.6; Pathway Prediction for Plant Secondary Metabolism;287
17.3.2; PathPred: Pathway Prediction Server;288
17.3.3; E-zyme for Prediction of Enzymatic Reactions;290
17.3.4; KAAS -- KEGG Automatic Annotation Server;292
17.3.5; KegArray;292
17.4; Summary;294
17.5;References;295
18;Towards Elucidation of the Escherichia coli K-12 Unknowneome;296
18.1; Defining the Unknowneome;296
18.2; The Need for Gene Ontologies;297
18.3; Systematic Screening for Gene Functions Using the Keio Collection Single-Gene Deletion Library;297
18.4; Systematic Screening of Single-Gene Deletion Mutants for Phenotypes Using Phenotype MicroArray Technology;302
18.5; Systematic Screening for Genetic Interactions;304
18.6; Information Resources;306
18.7; Future Perspectives;308
18.8;References;309
19;Index;313