Description
An in-depth overview of what computation can do in bioinorganic chemistry, written for experimentalists and theoreticians
The last decades have shown the emergence of numerous exiting fields in bioinorganic chemistry, such as the design of de novo metalloenzymes, the discovery of new bioactive metallodrugs, or the characterization of molecular mechanisms by which living organisms acquire their metal. In parallel, the computational chemistry community has been working hard on optimizing its framework to deal with biometallic systems; a phenomenon magnified by the increase of computational power and the advent of AI approaches.
Computational Bioinorganics: From Description to Prediction provides an updated view on the current state-of-the-art of the field. The book first intends to clarify how computational and experimental researchers in bioinorganic chemistry can now collaborate under this new computational paradigm. It then follows with a series of chapters that cover a wide range of computational approaches, strategies, and applications. Contributions from a team of experts in computational chemistry expose methods that range from structural bioinformatics, quantum chemistry, large-scale molecular dynamics or multi-scale strategies. They illustrate how these tools can be applied to a wide variety of topics such as the modeling of metal-mediated folding processes, the computer-aided design of metalloenzymes, spectroscopic analysis, the prediction of metal binding sites in proteins or the characterization of the interaction of metallodrugs with biomolecules.
Edited by a recognized leader in the field, Computational Bioinorganics: From Description to Prediction is an essential resource for academic and industrial researchers working in the fields of bioinorganic chemistry, coordination chemistry, biochemistry, computational chemistry, biophysics, bioinformatics, and protein engineering.
Table of Contents
Preface ix
1 What Could Bring Theory to Modern Bioinorganics: A Conversation? 1
Gerard Roelfes and Jean-Didier Maréchal
2 Computational Prediction and Modeling of Metal-binding Sites in Proteins 9
José-Emilio Sánchez-Aparicio, Giuseppe Sciortino, and Jean-Didier Maréchal
2.1 Introduction 9
2.2 Experimental Tools 10
2.3 Computational Tools 10
2.3.1 Pattern Recognition Algorithms 11
2.3.2 The Backbone Preorganization Hypothesis 14
2.3.3 Protein–Ligand Dockings 17
2.3.4 GaudiMM Platform 19
2.3.5 Quantum Mechanical and Hybrid Approaches 21
2.3.6 Molecular Dynamics, Integrative or Multiscale Approaches 22
2.4 Conclusions 25
References 25
3 Quantum-mechanics Approaches in Bioinorganic Chemistry: Targeting the Oxidation State 33
Marcel Swart
3.1 Introduction 33
3.2 Experimental Observations 35
3.3 Quantum-chemistry Approaches 37
3.3.1 Chemical Bonding Analyses 38
3.3.2 Oxidation States from Wavefunctions or Densities 41
3.4 Classical Examples 43
3.5 Intriguing Examples 46
3.6 Summary and Outlook 48
Acknowledgments 49
References 49
4 Computation and Spectroscopy 57
Eugenio Garribba
4.1 Introduction 57
4.2 Software, Computational Techniques, and Statistical Indicators 58
4.3 Electron Paramagnetic Resonance 61
4.4 Electron Spin Echo Envelope Modulation and Electron-nuclear Double Resonance 67
4.5 Nuclear Magnetic Resonance 69
4.6 Electron Absorption Spectroscopy 77
4.7 Circular Dichroism and Magnetic Circular Dichroism 81
4.8 Vibrational Spectroscopy 83
4.9 Mossbauer Spectroscopy 84
4.10 Conclusions 88
References 89
5 Multiscale Computational Modeling for the Discovery and Characterization of New Metalloenzyme-catalyzed Reactions 99
Ferran Feixas and Marc Garcia-Borràs
5.1 Importance of Metalloenzymes 99
5.2 Multi-scale Computational Microscope for Modeling Metalloenzymatic Reaction Mechanisms 100
5.2.1 Classical Molecular Dynamics Simulations 102
5.2.2 Truncated Active Site Models at the Quantum Mechanics Level 103
5.2.3 Hybrid Quantum Mechanics/Molecular Mechanics 103
5.3 Practical Considerations for Computational Modeling of Metalloenzymes 104
5.3.1 Selecting the Starting Structure 104
5.3.2 Initial Exploration of Conformational Dynamics 105
5.3.3 Placement of Substrate or Reaction Intermediate 106
5.4 The Mechanistic Versatility of P450s Through the Lens of a Computational Microscope: Practical Examples 107
5.4.1 Practical Example: Anti-Markovnikov Alkene Oxidations Catalyzed by P450s 108
5.4.2 Molecular Dynamics Simulations for Modeling the Protein Environment 112
5.4.3 Insightful Revelations from QM/MM Calculations 114
5.4.4 The Role of Local Electric Fields in Enzymatic Reactions 116
5.5 Conclusions 117
Acknowledgments 118
References 119
6 Force Fields and Metals 123
José-Emilio Sánchez-Aparicio, Lorena Roldán-Martín, Jean-Didier Maréchal, and Giuseppe Sciortino
6.1 Introduction 123
6.2 Overview on Molecular Mechanics and How to Deal with Metals 124
6.3 Bonded Model 126
6.3.1 Cu(II) Binding to Polyhistidine Peptides 126
6.3.2 Interaction of Oxaliplatin with Insulin 127
6.3.3 Artificial Metallohydratase 128
6.4 Nonbonded Models 131
6.4.1 The Dummy Atom 131
6.5 The 12-6(-4) Lennard–Jones Potentials 132
6.6 Conclusions 134
References 135
7 Modeling Thermally Activated Processes 139
Pietro Vidossich and Alessandra Magistrato
7.1 Introduction 139
7.2 Modeling Transitions 140
7.2.1 Theoretical Framework 141
7.2.2 Collective Variables 142
7.2.3 Biased Methods 144
7.3 Illustrative Studies 147
7.3.1 Metal Coordination 147
7.3.2 Enzymatic Reactions 150
7.4 Conclusion 153
References 154
8 Metal-induced Folding of Oligopeptides 159
L. Roldán-Martín, L. Rodríguez-Santiago, J. Alí-Torres, J. D. Maréchal, and M. Sodupe
8.1 Introduction 159
8.2 Metal–Ligand Interactions with Short Peptides (n = 2–8) 160
8.2.1 Gly n and Ala n Metal Complexes 160
8.2.2 Metal–Peptide Complexes Involving Residues Other than Gly and Ala 163
8.3 Metal-oligopeptides (n >8) 165
8.3.1 Construction of Three-dimensional Models. Application to Cu 2+ -Aβ 1–16 166
8.3.2 Impact of Cu 2+ and Al 3+ Binding on the Aβ 1–42 Conformational Landscape 169
8.4 Conclusions 172
References 172
9 Computational Studies of Metallodrugs 179
James A. Platts and Matthew Turner
9.1 Introduction 179
9.2 QM Studies of Metallodrugs and Their Solvation 179
9.3 QM Studies of Metallodrug–Biomolecule Interactions 181
9.4 QM/MM Modeling of Drug–Biomolecule Interactions 184
9.5 Classical MM Description of Drugs and Their Interactions 186
9.6 Docking and QSAR Studies 190
9.7 Conclusion 191
References 192
10 Computational Design of Artificial Metalloenzymes 195
Laura Tiessler-Sala, Maria Fatima Lucas, and Emanuele Monza
10.1 Introduction 195
10.2 Typical Computational Pipeline 196
10.3 Critical Aspects and Frontiers of ArM Design 198
10.3.1 Challenges in Modeling Metallic Centers 198
10.3.2 Unnatural Amino Acids Can Unlock New Reactivity 200
10.3.3 Beyond TS Stabilization 202
10.3.4 The New Era: De Novo Protein Design with DL 204
10.4 Concluding Remarks 205
References 206
11 How Computational Chemistry Can Contribute to the Understanding of the Effects of Metal Ions in Biological Systems: Aluminum as a Case Study 213
J.I. Mujika and X. Lopez
11.1 Introduction 213
11.2 Aluminum 215
11.2.1 Aluminum Speciation in Water 216
11.2.2 Mapping Aluminum Speciation with Biological Building Molecules 217
11.3 The Pro-oxidant Activity of Aluminum 217
11.4 Unveiling the Al(III) − Aβ Complex 223
11.4.1 Step 1: Most Favorable First Coordination Spheres of Aluminum 225
11.4.2 Step 2: The Building of Preliminary Al.AβComplexes 227
11.4.3 Step 3: Refinement of the Al.AβComplexes 228
11.5 In Silico Design of Novel Aluminum Chelators 231
11.6 Concluding Remarks 237
References 237
Index 239
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