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Computational molecular modelling in structural biology /

Computational Molecular modelling in Structural Biology, Volume 113, the latest release in the Advances in Protein Chemistry and Structural Biology, highlights new advances in the field, with this new volume presenting interesting chapters on charting the Bromodomain BRD4: Towards the Identification...

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Bibliographic Details
Corporate Author: ScienceDirect (Online service)
Other Authors: Karabencheva-Christova, Tatyana (Editor), Christov, Christo (Senior lecturer in computational biochemistry) (Editor)
Format: eBook
Language:English
Published: Cambridge, MA : Academic Press is an imprint of Elsevier, 2018.
Series:Advances in protein chemistry and structural biology ; v. 113.
Subjects:
Computational biology.
Molecular biology > Computer simulation.
Computational Biology
Bio-informatique.
Biologie moléculaire > Simulation par ordinateur.
NATURE > Reference.
SCIENCE > Life Sciences > Biology.
SCIENCE > Life Sciences > General.
Computational biology
Molecular biology > Computer simulation
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Front Cover
  • Computational Molecular Modelling in Structural Biology
  • Copyright
  • Contents
  • Contributors
  • Preface
  • Chapter One: Combined Quantum Mechanics and Molecular Mechanics Studies of Enzymatic Reaction Mechanisms
  • 1. Introduction
  • 2. The QM/MM Method
  • 3. Calculation of QM/MM Energy
  • 4. Treatment of Bonds at the QM/MM Boundary
  • 5. QM/MM Embedding Techniques
  • 6. QM/MM Modeling of Reaction Mechanisms
  • 6.1. Transition State Modeling
  • 6.2. Reaction Path Modeling
  • 6.3. QM/MM Free Energy Calculation Methods
  • 7. QM/MM Applications in Protein-Ligand Docking
  • 8. Practical QM/MM Applications for Enzyme Reactivity
  • 8.1. Reaction Mechanism of Tryptophan 7-Halogenase
  • 8.2. pro-S Hydrogen Abstraction Reaction in Cycoloxygenase-1
  • 8.3. Mechanism of Covalent Addition Between EGFR-Cysteine 797 and N-(4-Anilinoquinazolin-6-yl) Acrylamide
  • 9. Conclusions
  • References
  • Chapter Two: Computational Methods for Efficient Sampling of Protein Landscapes and Disclosing Allosteric Regions
  • 1. Why Is There Need for Developing Efficient Computational Methods for Proteins?
  • 2. Marvels and Limitations of MD Simulations
  • 2.1. Brief Overview of MD Simulations
  • 2.2. Tour-de-force Calculations Employing MD
  • 2.3. Limitations of MD
  • 3. Viewing Proteins as Networks of Interacting Residues
  • 3.1. Construction of RNs
  • 3.2. Network Parameters Useful in RN
  • 3.3. Applications of RN for Function Assignment to Residues
  • 4. Elastic Network Models of Proteins
  • 4.1. Practical Implementations of ANM and Related Approaches
  • 4.2. Extracting Function Related Information From Elastic Network Models
  • 5. Beyond Elastic Networks: PRS
  • 5.1. PRS Methodology
  • 5.2. Selection of Allosteric Sites and Functional Regions via PRS
  • 6. New Directions for Efficient Sampling of Conformational Landscapes
  • Acknowledgments
  • References.
  • Chapter Three: Computational Methods for Epigenetic Drug Discovery: A Focus on Activity Landscape Modeling
  • 1. Introduction
  • 2. Epigenetic Targets
  • 3. Activity Landscape Modeling
  • 3.1. SAS Maps
  • 3.2. Structure-Activity Landscape Index
  • 3.3. SAS Maps of Epigenetic Targets
  • 3.4. Activity Cliffs Generators
  • 3.4.1. Histone Deacetylases
  • 3.4.2. Histone Methyltransferases EHMT2 and DOT1L
  • 3.5. Epigenetic Targets With Continuous SAR
  • 3.6. Epigenetic Targets With Scaffold Hops
  • 4. Conclusions and Perspectives
  • Acknowledgments
  • References
  • Chapter Four: The OECD Principles for (Q)SAR Models in the Context of Knowledge Discovery in Databases (KDD)
  • 1. Introduction
  • 2. Definition of the Goals
  • 2.1. Creation or Selection of a Dataset
  • 2.2. Data Cleaning and Preprocessing
  • 2.3. Data Reduction and Projection
  • 3. Selection of Data Mining Methods
  • 4. Exploratory Analysis and Model/Hypothesis Selection
  • 5. Data Mining
  • 6. Evaluation
  • 7. Interpretation/Utilization
  • 8. Read-Across
  • Acknowledgments
  • References
  • Chapter Five: Computational Methods to Discover Compounds for the Treatment of Chagas Disease
  • 1. Introduction
  • 2. Biological Relevant Space
  • 3. Chemical Space
  • 3.1. Chemical Libraries
  • 3.2. Chemical Space: Diversity and Content
  • 3.2.1. Physicochemical Properties and Visualization of Chemical Space
  • 3.2.2. Molecular Fingerprints
  • 4. Computational Approaches for Lead Identification
  • 4.1. Docking-Based Virtual Screening
  • 4.2. Pharmacophore-Based VS
  • 4.3. Ligand-Based, Data Mining, Similarity Searching
  • 4.4. Combined, Integrative Multistrategy
  • 5. Conclusions
  • 6. Perspectives
  • Acknowledgments
  • References
  • Back Cover.