PROTEIN STRUCTURE PREDICTION Bioinformatic Approach

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1 Link to Order: Price: $ Website:. PROTEIN STRUCTURE PREDICTION Bioinformatic Approach edited by IGOR F. TSIGELNY Table of Contents: Preface xv List of Contributors xxi Part I. CONCEPTS OF PROTEIN STRUCRURE PREDICTION 1 A. Prediction Methods and Systems 3 1. Computational Studies of Protein Structure and Function Using Threading Program PROSPECT 5 Dong Xu and Ying Xu 1.1. Introduction Method of PROSPECT Threading Templates Energy Function Threading Algorithm Confidence Assessment of Threading Results Protocols of Using PROSPECT Pre-Processing before Running PROSPECT Running PROSPECT Human Evaluation Manual Refinement Structure-Based Functional Inference Performance of PROSPECT Testing of PROSPECT Using Known Structures in PDB Blind Test in CASP Application of PROSPECT in Protein Studies Human Vitronectin Human DNA-Activated Protein Kinase Yeast PTR3 Protein Summary Bayesian Approach to Protein Fold Recognition: Building Protein Structural Models from Bits and Pieces 43 Jadwiga Bienkowska, Hongxian He, Robert G. Rogers Jr., and Lihua Yu 2.1. Introduction Fundamentals of DSMs and HMMs Representation of Protein Structure by a DSM Mathematical Representation of a DSM Measures of Compatibility of a Protein Sequence with a DSM Automated Generation of Protein Structural Templates Criteria for Selecting Structural Information Candidate Structural Quantities 55 1

2 Classification of Structural States Automated Design of a Structural DSM from a Structural Template Design Principles Secondary Structure Submodels Construction of DSM from the Structural Template Using Structural Alignments and Multiple Structural Templates in Building DSM Automatic Pattern Embedding in a DSM Automated Pattern Generation and Selection Look-Ahead A Bayesian Approach to Fold Recognition The Filtering Algorithm Prior Model Probabilities Results Comparing the Bayesian Approach and Total Alignment Probability with Other Methods Results of Automatic Pattern Embedding Comparison of Different Assignments of Prior Probabilities Strategies for Defeating the Combinatorial Explosion Three-Dimensional Structure Prediction Using Simplified Structure Models and Bayesian Block Fragments 85 Jun Zhu and Roland Lüthy 3.1. Introduction Methods Simplified Backbone Angle Representation of 3D Structures Block Selection Energy Functions Energy Minimization Using Information from Bayesian Blocks Enforcing Secondary Structures Examples Protein Structure Prediction Using Hidden Markov Model Structural Libraries 109 Igor Tsigelny, Yuriy Sharikov, and Lynn F. Ten Eyck 4.1. Introduction Structural Hidden Markov Model Libraries Decision Tree Search for the Best HMM Searching within the Structural Alignment Program Testing Prediction of Unsolved Structures The Role of Sequence Information in Protein Structure Prediction 125 Damien Devos, Florencio Pazos, Osvaldo Olmea, David de Juan, Osvaldo Graña, Jose M. Fernández, and Alfonso Valencia 2

3 5.1. Introduction Information Contained in Multiple Sequence Alignments in Protein Families Automated Generation of Protein Structural Templates Distribution of Informative Positions in Protein Structures Informative Positions in Protein Structure Models A Threading Server That Filters Models with Multiple Sequence Alignments Information A First Field Evaluation of the Server, the CAFASP Results A CAFASP Example of the Use of Sequence Information Training Neural Networks for the Discrimination of Wrong Threading Models Using Sequence Conclusions Protein Fold Recognition and Comparative Modeling Using HOMSTRAD, JOY, and FUGUE 143 Ricardo Núñez Miguel, Jiye Shi, and Kenji Mizuguchi 6.1. Introduction Overview Identification of Homologues Generating Sequence-Structure Alignment Example Searching for Homologues Alignment Modeling Heteroatoms Refinements Model Validation Model Conclusion Fully Automated Protein Tertiary Structure Prediction Using Fourier Transform Spectral Methods 171 Carlos Adriel Del Carpio Muñoz and Atsushi Yoshimori 7.1. Sequence Alignment and Protein Structure Modeling Protein Function and Structure Elucidation by Spectral Analysis Spectral Analysis and Folding Pattern Recognition Spectral Representation of Protein Primary Structures Spectral Alignment and Protein Structure Similarity Automatic Protein Folding Pattern Recognition Automatic Classification of Protein Foldings Dominant Physicochemical Parameters Classification of Protein Folding by Spectral Analysis Protein Folding Pattern Recognition by Spectral Analysis From the Building Blocks Folding Model to Protein Structure Prediction 201 Nurit Haspel, Chung-Jung Tsai, Haim Wolfson, and Ruth Nussinov 8.1. Introduction 203 3

4 8.2. Protein Folding: A Process of Intra-Molecular Building Block Recognition Experimental and Theoretical Support for the Building Block Concept The Building Block Cutting Algorithm The Scoring Function The Cutting Procedure Critical Building Blocks From the Building Block Folding Model to Structure Prediction: The Scheme Conclusions Protein Threading Statistics: An Attempt to Assess the Significance of a Fold Assignment to a Sequence 227 Antoine Marin, Joël Pothier, Karel Zimmermann, and Jean-François Gibrat 9.1. Introduction Method Library of Cores Development of a Score Function Combinatorial Optimization Algorithm Empirical Distribution of Scores Development of a Benchmark Database Results Discussion Use of Filters Difficulty of the Benchmark Statistical Criterion Present Limits of the Method Conclusion Protein Structure Prediction by Threading: Force Field Philosophy, Approaches to Alignment 263 Thomas Huber and Andrew E. Torda Introduction Common Methodology Force Field Based Scoring Parameterizing Force Fields Physically-Based Potential Energies Potentials of Mean Force Optimized Force Fields Alignment Philosophy Common Alignment and Score Methods Sausage Alignments Beyond Pairwise Terms Template Libraries Further Outlook and Speculation Predicting Protein Structure Using SAM, UCSC s Hidden Markov Model Tools 297 Kevin Karplus 4

5 11.1. A Naive View of Protein Structure Prediction Fold Recognition Hidden Markov Models Multitrack Hidden Markov Models Statistical Significance for Hidden Markov Models Using SAM-T2K for Superfamily Modeling Improved Verification of Homology Family-Level Multiple Alignments Modeling Non-Contiguous Domains Building an HMM from a Structural Alignment Improving Existing Multiple Alignments Creating a Multiple Alignment from Unaligned Sequences Conclusions Local Genome Organization, Gene Expression, and Structural Genomics: Evolution at Work 325 Wayne Volkmuth and Nickolai Alexandrov Introduction Methods Genomes Microarray Expression Data Fold Assignment Non-Redundant Set of Proteins Fold Enrichment Along the Genome Fold Enrichment for Genes with Similar Patterns of Expression Results Fold Enrichment Along the Genome Fold Enrichment for Genes with Similar Patterns of Expression Summary and Conclusions Protein Structure Prediction on the Basis of Combinatorial Peptide Library Screening 341 Igor Tsigelny, Yuriy Sharikov, Vladimir Kotlovyi, Michael Kelner, and Lynn F. Ten Eyck Concept of the Comprehensive System HMM-ELONGATOR Problem Description Elongation Strategies 346 B. Consensus Structure Prediction A User s Guide to Fold Recognition 355 Naomi Siew and Daniel Fischer Introduction Examples of Using Fold Recognition for Biological Research Plant Resistance Gene Products Acetohydroxyacid Synthase Endothelial Cell Protein C/Activated Protein C Receptor How to Fold Recognize? Searching for Homologues of Known Structure 364 5

6 Running Your Favorite Fold Recognition Method Running Other Methods Why Run More Than One Method? D-Shotgun Meta-Predictor Summary Structure Prediction Meta Server Leszek Rychlewski Introduction The Meta Server User Input and Job Status Display Job Deposition and Administration Request Submission Queuing Blast-Filter Local and Remote Prediction Services Raw Output Converters Visualization and Linking Interfaces Discussion 390 Part II. METHODS OF STRUCTURE AND SEQUENCE ALIGNMENT Improved Fold Recognition by Using the PCONS Consensus Approach 397 Huisheng Fang, Björn Wallin, Jesper Lundström, Christer von Wowern, and Arne Elofsson Introduction Why are Manual Predictions Better? Biological Knowledge Structural Analysis Consensus Analysis Consensus Predictions in CASP Pcons Collection of Publicly Available Models Structural Comparison Prediction of Quality of the Models Performance of Pcons Performance in LiveBench Why Does Pcons Perform Better? Pcons-II Improvements Using More Servers Speed-Up of Structural Comparisons Using Better Statistics Improvements Using Linear Regression Summary New Insights into Protein Fold Space and Sequence-Structure Relationships 417 Ilya N. Shindyalov and Philip E. Bourne Introduction 419 6

7 17.2. Overview of CE Sequence-Structure Space Scop vs. CE Fold Space Comparison Analysis of Structure Redundancy Size of NR Set as a Function of Criteria Used Characterization of Chains Excluded from the Set Characterization of Similarity Between Chains in the Set Complementary Sequence and Structure NR Sets Combined NR Set A Flexible Method for Structural Alignment: Applications to Structure Prediction Assessments 431 Vladimir Kotlovyi, Igor Tsigelny, and Lynn Ten Eyck Introduction Theoretical Background Algorithms and Their Implementation Representation of Data in XML Forms Timing Web-Servers Illustrative Examples Comparative Analysis of Protein Structure: New Concepts and Approaches for Multiple Structure Alignment 449 Chittibabu Guda, Eric D. Scheeff, Philip E. Bourne, and Ilya N. Shindyalov Introduction Algorithm for Aligning Multiple Protein Structures Using Monte Carlo Optimization Scoring Function Approaches for Optimization of Multiple Structure Alignment Effect of Weights Based on Number of Residues on Alignment Length and Alignment Distance Analysis of Specific Protein Families Analysis of an Alignment of Protein Kinases Analysis of an Alignment of Aspartic Proteinases Summary Comparative Analysis of Protein Structure: Automated vs. Manual Alignment of the Protein Kinase Family 463 Eric D. Scheeff, Philip E. Bourne, and Ilya N. Shindyalov Introduction The Challenge of Automated Protein Structure Alignment A Case Study: Alignment of the Eukaryotic Protein Kinases and Their Relatives An Example of an Automated Alignment: The Combinatorial Extension Algorithm Parameters for the Determination of an Optimal Structure Alignment Comparison of CE Alignments with Manual Alignments 471 7

8 20.7. Conclusion 475 Index 479 8