Numerical Computer Methods, Part D, Vol 383. Methods in Enzymology

  • ID: 1768918
  • Book
  • 489 Pages
  • Elsevier Science and Technology
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The aim of Numerical Computer Methods, Part D is to brief researchers of the importance of data analysis in enzymology, and of the modern methods that have developed concomitantly with computer hardware. It is also to validate researchers' computer programs with real and synthetic data to ascertain that the results produced are what they expected.

- Selected Contents: - Prediction of protein structure- Modeling and studying proteins with molecular dynamics- Statistical error in isothermal titration calorimetry- Analysis of circular dichroism data- Model comparison methods

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Editors-In-Chief

Contributors to Volume 383

Preface

Methods In Enzymology

Prediction of Protein Structure

Overview and Perspective

Classifications of Protein Structure

Concepts and Evaluations of Protein Predictions

Process of Extracting Information about Protein Structure from Sequence

Future Directions

Modeling and Studying Proteins with Molecular Dynamics

Introduction

Sampling of CHARMM Capabilities

Program Operation Basics

Example Analysis

Ab Initio Protein Folding Using LINUS

Introduction

Anatomy of LINUS Simulation

Implementation

Simulation Examples

Conclusion

Appendix I

Protein Structure Prediction Using Rosetta

Introduction

Rosetta Strategy

De Novo Structure Prediction with Rosetta

Structure Prediction by Fragment Assembly

Enhancements of Fragment Insertion Strategy

Effectiveness of Conformation Modification Operators for Energy Function Optimization

Conclusions

Supplemental Materials

Appendix I

Appendix II

Poisson-Boltzmann Methods for Biomolecular Electrostatics

Introduction

Numerical Solution of Poisson-Boltzmann Equation

Applications to Biomedical Sciences

Conclusions

Atomic Simulations of Protein Folding, Using the Replica Exchange Algorithm

Introduction

Replica Exchange Molecular Dynamics

Practical Issues

Appendix

DNA Microarray Time Series Analysis: Automated Statistical Assessment of Circadian Rhythms in Gene Expression Patterning

Introduction

Statistical Assessment of Daily Rhythms in Microarray Data

Simulation Procedure

Comparisons of Analytical Results

Summary

Molecular Simulations of Diffusion and Association in Multimacromolecular Systems

Introduction

Theoretical Aspects

Practical Aspects

Some Example Applications

Conclusion

Modeling Lipid-Sterol Bilayers: Applications to Structural Evolution, Lateral Diffusion, and Rafts

Introduction

Theoretical Models

Simulation Methods

Results

Summary and Perspectives

Idealization and Simulation of Single Ion Channel Data

Introduction

Noise

Filtering

Missed Events

Subconductance Levels

Models

Analysis Methods

Simulation

Idealization

Interpretation

Performance

Statistical Error in Isothermal Titration Calorimetry

Introduction

Variance-Covariance Matrix in Least Squares

Monte Carlo Computational Methods

Van't Hoff Analysis of K°(T): Least-Squares Demonstration

Isothermal Titration Calorimetry

Calorimetric Versus Van't Hoff ΔH° from ITC

Conclusion

Analysis of Circular Dichroism Data

Introduction

Summary of Methods to Obtain Secondary Structure of Proteins from Circular Dichroism Data

Determination of Thermodynamics of Protein Folding/Unfolding from CD Data

Determination of Binding Constants from CD Data

Conclusion

Appendix I

Computation and Analysis of Protein Circular Dichroism Spectra

Introduction

Basic Definitions

Computation of Protein CD

Analysis of Protein CD

Model Comparison Methods

Introduction

Statistical Foundations of Model Comparison

Model Comparison Methods

Model Comparison at Work

Conclusion

Practical Robust Fit of Enzyme Inhibition Data

Introduction

Theory

Numerical Example

Implementation Notes

Conclusions

Measuring Period of Human Biological Clock: Infill Asymptotic Analysis of Harmonic Regression Parameter Estimates

Introduction

Theory

Proof

Proof

Proof

Data Analysis

Discussion

Appendix I: Outline of Proof of Proposition 1

Appendix II: Proof of Lemma 1

Appendix III: Proof of Lemma 2

Bayesian Methods to Improve Sample Size Approximations

Introduction

Bayesian Inference

Deriving Sample Size Formulas

Choosing Prior Distributions

Gain from Using Prior Information

Examples

Conclusion

Distribution Functions from Moments and the Maximum-Entropy Method

Introduction

Ligand Binding: Moments

Maximum-Entropy Distributions

Ligand Binding: Distribution Functions

Enthalpy Distributions

Self-Association Distributions

Author Index

Subject Index

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Brand, Ludwig.
Johnson, Michael L.

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