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 of Novel Inhibitors with Molecular Similarity and Receptor Mapping, and Computational Methods to Discover Compounds for the Treatment of Chagas Disease.
- Provides the authority and expertise of leading contributors from an international board of authors
- Presents the latest release in the Advances in Protein Chemistry and Structural Biology series
- Updated, with the latest information on Computational Molecular Modelling in Structural Biology
| Contributors |
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vii | |
| Preface |
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ix | |
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1 Combined Quantum Mechanics and Molecular Mechanics Studies of Enzymatic Reaction Mechanisms |
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1 | (32) |
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Tatyana G. Karabencheva-Christova |
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2 | (1) |
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3 | (2) |
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3 Calculation of QM/MM Energy |
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5 | (2) |
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4 Treatment of Bonds at the QM/MM Boundary |
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7 | (1) |
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5 QM/MM Embedding Techniques |
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8 | (1) |
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6 QM/MM Modeling of Reaction Mechanisms |
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9 | (4) |
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7 QM/MM Applications in Protein-Ligand Docking |
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13 | (1) |
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8 Practical QM/MM Applications for Enzyme Reactivity |
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14 | (11) |
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25 | (1) |
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26 | (7) |
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2 Computational Methods for Efficient Sampling of Protein Landscapes and Disclosing Allosteric Regions |
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33 | (32) |
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1 Why Is There Need for Developing Efficient Computational Methods for Proteins? |
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34 | (4) |
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2 Marvels and Limitations of MD Simulations |
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38 | (4) |
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3 Viewing Proteins as Networks of Interacting Residues |
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42 | (4) |
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4 Elastic Network Models of Proteins |
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46 | (5) |
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5 Beyond Elastic Networks: PRS |
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51 | (5) |
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6 New Directions for Efficient Sampling of Conformational Landscapes |
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56 | (2) |
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58 | (1) |
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58 | (7) |
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3 Computational Methods for Epigenetic Drug Discovery: A Focus on Activity Landscape Modeling |
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65 | (20) |
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66 | (1) |
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67 | (1) |
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3 Activity Landscape Modeling |
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68 | (12) |
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4 Conclusions and Perspectives |
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80 | (1) |
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80 | (1) |
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81 | (4) |
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4 The OECD Principles for (Q)SAR Models in the Context of Knowledge Discovery in Databases (KDD) |
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85 | (34) |
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86 | (1) |
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2 Definition of the Goals |
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86 | (16) |
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3 Selection of Data Mining Methods |
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102 | (1) |
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4 Exploratory Analysis and Model/Hypothesis Selection |
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103 | (7) |
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110 | (1) |
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111 | (1) |
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7 Interpretation/Utilization |
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111 | (1) |
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112 | (2) |
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114 | (1) |
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114 | (5) |
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5 Computational Methods to Discover Compounds for the Treatment of Chagas Disease |
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119 | |
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Karla Daniela Rodriguez-Hernandez |
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120 | (2) |
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2 Biological Relevant Space |
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122 | (2) |
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124 | (6) |
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4 Computational Approaches for Lead Identification |
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130 | (8) |
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138 | (1) |
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138 | (1) |
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139 | (1) |
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139 | |
Dr. Tatyana Karabencheva-Christova works at the Department of Applied Sciences, University of Northumbria, UK. Dr. Christo Z. Christov teaches at Northumbria University, Ellison Building, Newcastle-upon-Tyne, UK
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