Protein Bioinformatics: An Algorithmic Approach to Sequence and Structure Analysis

ISBN-13: 9780470848395 / Angielski / Twarda / 2004 / 376 str.

Ingvar Eidhammer;Jonassen Inge; William R. Taylor

Opis

Genomics and bioinformatics play an increasingly important and transformative role in medicine, society and agriculture. The mapping of the human genome has revealed 35,000 or so genes which might code for more than one protein, resulting in 100,000 proteins for the humans alone. Since proteins are attractive targets for developing drugs, efforts are now underway to map sequences and assign functions to many novel proteins. This book takes the novel approach to cover both the sequence and structure analysis of proteins in one volume and from an algorithmic perspective. Key features of the book include:

• Provides a comprehensive introduction to the analysis of protein sequence and structure analysis.
• Takes an algorithmic approach, relying on computational methods rather than theoretical.
• Provides an integrated presentation of theory, examples, exercises and applications.
• Includes coverage of both protein structure, and sequence, analysis.
• Accessible enough for biologists, yet rigorous enough for computer scientists and mathematicians.
• Supported by a Web site featuring exercises, solutions, images, and computer programs.

Visit this website for exercises with solutions, computer programs, errata and additional material: http: //www.ii.uib.no/proteinbioinformatics/

Spis treści

Preface.

Acknowledgements.

Part I: SEQUENCE ANALYSIS.

1. Pairwise Global Alignment of Sequences.

1.1 Alignment and Evolution.

1.2 What is an Alignment?

1.3 A Scoring Scheme for the Model.

1.4 Finding Highest–Scoring Alignments with Dynamic Programming.

1.4.1 Determine Hi,j.

1.4.2 Use of matrices.

1.4.3 Finding the alignments that give the highest score.

1.4.4 Gaps.

1.5 Scoring Matrices.

1.6 Scoring Gaps: Gap Penalties.

1.7 Dynamic Programming for General Gap Penalty.

1.8 Dynamic Programming for Affine Gap Penalty.

1.9 Alignment Score and Sequence Distance.

1.10 Exercises.

1.11 Bibliographic notes.

2 Pairwise Local Alignment and Database Search.

2.1 The Basic Operation: Comparing Two Sequences.

2.2 Dot Matrices.

2.2.1 Filtering.

2.2.2 Repeating segments.

2.3 Dynamic Programming.

2.3.1 Initialization.

2.3.2 Finding the best local alignments.

2.3.3 Algorithms.

2.3.4 Scoring matrices and gap penalties.

2.4 Database Search: BLAST.

2.4.1 The procedure.

2.4.2 Preprocess the query: make the word list.

2.4.3 Scanning the database sequences.

2.4.4 Extending to HSP.

2.4.5 Introducing gaps.

2.4.6 Algorithm.

2.5 Exercises.

2.6 Bibliographic notes.

3. Statistical Analysis.

3.1 Hypothesis Testing for Sequence Homology.

3.1.1 Random generation of sequences.

3.1.2 Use of Z values for estimating the statistical significance.

3.2 Statistical Distributions.

3.2.1 Poisson probability distribution.

3.2.2 Extreme value distributions.

3.3 Theoretical Analysis of Statistical Significance.

3.3.1 The P value has an extreme value distribution.

3.3.2 Theoretical analysis for database search.

3.4 Probability Distributions for Gapped Alignments.

3.5 Assessing and Comparing Programs for Database Search.

3.5.1 Sensitivity and specificity.

3.5.2 Discrimination power.

3.5.3 Using more sequences as queries.

3.6 Exercises.

3.7 Bibliographic notes.

4 .Multiple Global Alignment and Phylogenetic Trees.

4.1 Dynamic Programming.

4.1.1 SP score of multiple alignments.

4.1.2 A pruning algorithm for the DP solution.

4.2 Multiple Alignments and Phylogenetic Trees.

4.3 Phylogeny.

4.3.1 The number of different tree topologies.

4.3.2 Molecular clock theory.

4.3.3 Additive and ultrametric trees.

4.3.4 Different approaches for reconstructing phylogenetic trees.

4.3.5 Distance–based construction.

4.3.6 Rooting of trees.

4.3.7 Statistical test: bootstrapping.

4.4 Progressive Alignment.

4.4.1 Aligning two subset alignments.

4.4.2 Clustering.

4.4.3 Sequence weights.

4.4.4 CLUSTAL.

4.5 Other Approaches.

4.6 Exercises.

4.7 Bibliographic notes.

5. Scoring Matrices.

5.1 Scoring Matrices Based on Physio–Chemical Properties.

5.2 PAM Scoring Matrices.

5.2.1 The evolutionary model.

5.2.2 Calculate substitution matrix.

5.2.3 Matrices for general evolutionary time.

5.2.4 Measuring sequence similarity by use of M .

5.2.5 Odds matrices.

5.2.6 Scoring matrices (log–odds matrices).

5.2.7 Estimating the evolutionary distance.

5.3 BLOSUM Scoring Matrices.

5.3.1 Log–odds matrix.

5.3.2 Developing scoring matrices for different evolutionary distances.

5.4 Comparing BLOSUM and PAM Matrices.

5.5 Optimal Scoring Matrices.

5.5.1 Analysis for one sequence.

5.6 Exercises.

5.7 Bibliographic notes.

6. Profiles.

6.1 Constructing a Profile.

6.1.1 Notation.

6.1.2 Removing rows and columns.

6.1.3 Position weights.

6.1.4 Sequence weights.

6.1.5 Treating gaps.

6.2 Searching Databases with Profiles.

6.3 Iterated BLAST: PSI–BLAST.

6.3.1 Making the multiple alignment.

6.3.2 Constructing the profile.

6.4 HMM Profile.

6.4.1 Definitions for an HMM.

6.4.2 Constructing a profile HMM for a protein family.

6.4.3 Comparing a sequence with an HMM.

6.4.4 Protein family databases.

6.5 Exercises.

6.6 Bibliographic notes.

7. Sequence Patterns.

7.1 The PROSITE Language.

7.2 Exact/Approximate Matching.

7.3 Defining Pattern Classes by Imposing Constraints.

7.4 Pattern Scoring: Information Theory.

7.4.1 Information theory.

7.4.2 Scoring patterns.

7.5 Generalization and Specialization.

7.6 Pattern Discovery: Introduction.

7.7 Comparison–Based Methods.

7.7.1 Pivot–based methods.

7.7.2 Tree progressive methods.

7.8 Pattern–Driven Methods: Pratt.

7.8.1 The main procedure.

7.8.2 Preprocessing.

7.8.3 The pattern space.

7.8.4 Searching.

7.8.5 Ambiguous components.

7.8.6 Specialization.

7.8.7 Pattern scoring.

7.9 Exercises.

7.10 Bibliographic notes.

Part II: STRUCTURE ANALYSIS

8. Structures and Structure Descriptions.

8.1 Units of Structure Descriptions.

8.2 Coordinates.

8.3 Distance Matrices.

8.4 Torsion Angles.

8.5 Coarse Level Description.

8.5.1 Line segments (sticks).

8.5.2 Ellipsoid.

8.5.3 Helices.

8.5.4 Strands and sheets.

8.5.5 Topology of Protein Structure (TOPS).

8.6 Identifying the SSEs.

8.6.1 Use of distance matrices.

8.6.2 Define Secondary Structure of Proteins (DSSP).

8.7 Structure Comparison.

8.7.1 Structure descriptions for comparison.

8.7.2 Structure representation.

8.8 Framework for Pairwise Structure Comparison.

8.9 Exercises.

8.10 Bibliographic notes.

9. Superposition and Dynamic Programming.

9.1 Superposition.

9.1.1 Coordinate RMSD.

9.1.2 Distance RMSD.

9.1.3 Using RMSD as scoring of structure similarities.

9.2 Alternating Superposition and Alignment.

9.3 Double Dynamic Programming.

9.3.1 Low–level scoring matrices.

9.3.2 High–level scoring matrix.

9.3.3 Iterated double dynamic programming.

9.4 Similarity of the Methods.

9.5 Exercises.

9.6 Bibliographic notes.

10. Geometric Techniques.

10.1 Geometric Hashing.

10.1.1 Two–dimensional geometric hashing.

10.1.2 Geometric hashing for structure comparison.

10.1.3 Geometric hashing for SSE representation.

10.1.4 Clustering.

10.2 Distance Matrices.

10.2.1 Measuring the similarity of distance (sub)matrices.

10.3 Exercises.

10.4 Bibliographic notes.

11. Clustering: Combining Local Similarities.

11.1 Compatibility and Consistency.

11.2 Searching for Seed Matches.

11.3 Consistency.

11.3.1 Test for consistency.

11.3.2 Overlapping clusters.

11.4 Clustering Algorithms.

11.4.1 Linear clustering.

11.4.2 Hierarchical clustering.

11.5 Clustering by Use of Transformations.

11.5.1 Comparing transformations.

11.5.2 Calculating the new transformation.

11.5.3 Algorithm.

11.6 Clustering by Use of Relations.

11.6.1 How many relations to compare?

11.6.2 Geometric relation.

11.6.3 Distance relation.

11.6.4 Use of graph theory.

11.7 Refinement.

11.8 Exercises.

11.9 Bibliographic notes.

12. Significance and Assessment of Structure Comparisons.

12.1 Constructing Random Structure Models.

12.1.1 Use of distance geometry.

12.2 Use of Structure Databases.

12.2.1 Constructing nonredundant subsets.

12.2.2 Demarcation line for similarity.

12.3 Reversing the Protein Chain.

12.4 Randomized Alignment Models.

12.5 Assessing Comparison and Scoring Methods.

12.6 Is RMSD Suitable for Scoring?

12.7 Scoring and Biological Significance.

12.8 Exercises.

12.9 Bibliographic notes.

13. Multiple Structure Comparison.

13.1 Multiple Superposition.

13.2 Progressive Structure Alignment.

13.2.1 Scoring.

13.2.2 Construction of consensus.

13.3 Finding a Common Core from a Multiple Alignment.

13.4 Discovering Common Cores.

13.4.1 Finding the multiple seed matches.

13.4.2 Pairwise clustering.

13.4.3 Determining common cores.

13.4.4 Scoring clusters.

13.5 Local Structure Patterns.

13.5.1 Local packing patterns.

13.5.2 Discovering packing patterns.

13.5.3 The approach.

13.5.4 Scoring the packing motifs.

13.6 Exercises.

13.7 Bibliographic notes.

14. Protein Structure Classification.

14.1 Protein Domains.

14.2 An Ising Model for Domain Identification.

14.3 Domain Classes.

14.3.1 Mainly–? domains.

14.3.2 Mainly–? domains.

14.3.3 ? ? domains.

14.4 Folds.

14.5 Automatic Approaches to Classification.

14.6 Databases for Structure Classification.

14.7 FSSP–Dali Domain Dictionary.

14.8 CATH.

14.8.1 Domains.

14.8.2 Class.

14.8.3 Architecture.

14.8.4 Topology (fold family).

14.8.5 Homologous superfamily.

14.8.6 Sequence families.

14.8.7 The CATH classification procedure.

14.9 Classification Based on Sticks.

14.10 Exercises.

14.11 Bibliographic notes.

Part III: SEQUENCE–STRUCTUREANALYSIS.

15. Structure Prediction: Threading.

15.1 Protein Secondary Structure Prediction.

15.1.1 Artificial neural networks.

15.1.2 The PHD program.

15.1.3 Accuracy in secondary structure prediction.

15.2 Threading.

15.3 Methods Based on Sequence Alignment.

15.3.1 The 3D 1D matching method.

15.3.2 The FUGUE method.

15.4 Methods Using 3D Interactions.

15.4.1 Potentials of mean force.

15.4.2 Towards modelling methods.

15.5 Alignment Methods.

15.5.1 Frozen approximation.

15.5.2 Double Dynamic Programming.

15.6 Multiple Sequence/Structure Threading.

15.6.1 Simple multiple sequence threading.

15.7 Combined Sequence/Threading Methods.

15.8 Assessment of Threading Methods.

15.8.1 Fold recognition.

15.8.2 Alignment accuracy.

15.8.3 CASP and CAFASP.

15.9 Bibliographic notes.

Appendix A: Basics in Mathematics, Probability and Algorithms.

A.1 Mathematical Formulae and Notation.

A.2 Boolean Algebra.

A.3 Set Theory.

A.4 Probability.

A.4.1 Permutation and combination.

A.4.2 Probability distributions.

A.4.3 Expected value.

A.5 Tables, Vectors and Matrices.

A.6 Algorithmic Language.

A.6.1 Alternatives.

A.6.2 Loops.

A.7 Complexity.

Appendix B: Introduction to Molecular Biology.

B.1 The Cell and the Molecules of Life: DNA RNA Proteins.

B.2 Chromosomes and Genes.

B.3 The Central Dogma of Molecular Biology.

B.4 The Genetic Code.

B.5 Protein Function.

B.5.1 The gene ontology.

B.6 Protein Structure.

B.7 Evolution.

B.8 Insulin Example.

B.9 Bibliographic notes.

References.

Index.

Blurb

Bioinformatics is the study of biological information and biological systems such as of the relationships between the sequence, structure and function of genes and proteins. The subject has seen tremendous development in recent years, and there are ever–increasing needs for good understanding of quantitative methods in the study of proteins. Protein Bioinformatics: An Algorithmic Approach to Sequence and Structure Analysis takes the novel approach of covering both the sequence and structure analysis of proteins in one volume and from an algorithmic perspective.

• Provides a comprehensive introduction to the analysis of protein sequences and structures.
• Provides an integrated presentation of methodology, examples, exercises and applications.
• Emphasises the algorithmic rather than mathematical aspects of the methods described.
• Covers comparison and alignment of protein sequences and structures as well as protein structure prediction focusing on threading approaches.
• Written in an accessible yet rigorous style, suitable for biologists, mathematicians and computer scientists alike.
• Suitable both for developers and users of bioinformatics tools.
• Supported by a Web site featuring exercises, solutions, images, and computer programs.

Protein Bioinformatics: An Algorithmic Approach to Sequence and Structure Analysis is ideally suited for advanced undergraduate and graduate students of bioinformatics, statistics, mathematics and computer science. It also provides an excellent introduction and reference source on the subject for practitioners and researchers.