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Computational Tools for Chemical Biology [Kõva köide]

Edited by (Centro de Investigaciones Biológicas CIB-CSIC, Spain)
  • Formaat: Hardback, 392 pages, kõrgus x laius: 234x156 mm, kaal: 747 g, No
  • Sari: Chemical Biology Volume 3
  • Ilmumisaeg: 02-Nov-2017
  • Kirjastus: Royal Society of Chemistry
  • ISBN-10: 1782627006
  • ISBN-13: 9781782627005
Teised raamatud teemal:
Computational Tools for Chemical BiologySuurem pilt
  • Formaat: Hardback, 392 pages, kõrgus x laius: 234x156 mm, kaal: 747 g, No
  • Sari: Chemical Biology Volume 3
  • Ilmumisaeg: 02-Nov-2017
  • Kirjastus: Royal Society of Chemistry
  • ISBN-10: 1782627006
  • ISBN-13: 9781782627005
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781782627005.html
Märksõnad:
The rapid development of efficient computational tools has allowed researchers to tackle biological problems and to predict, analyse and monitor, at an atomic level, molecular recognition processes. This book offers a fresh perspective on how computational tools can aid the chemical biology research community and drive new research.
Chapters from internationally renowned leaders in the field introduce concepts and discuss the impact of technological advances in computer hardware and software in explaining and predicting phenomena involving biomolecules, from small molecules to macromolecular systems. Important topics from the understanding of biomolecules to the modification of their functions are addressed, as well as examples of the application of tools in drug discovery, glycobiology, protein design and molecular recognition. Not only are the cutting-the-edge methods addressed, but also their limitations and possible future development.
For anyone wishing to learn how computational chemistry and molecular modelling can provide information not easily accessible through other experimental methods, this book will be a valuable resource. It will be of interest to postgraduates and researchers in the biological and chemical sciences, medicinal and pharmaceutical chemistry, and theoretical chemistry.

This book offers a fresh perspective on how computational tools can aid the chemical biology research community and drive new research.
Chapter 1 Computational Chemistry and Molecular Modelling Basics
1(38)
Samuel Genheden
Anna Reymer
Patricia Saenz-Mendez
Leif A. Eriksson
1.1 Introduction
1(1)
1.2 Techniques in Biomolecular Simulations
2(11)
1.2.1 Molecular Mechanics and Force Fields
2(2)
1.2.2 Basic Simulation Techniques
4(4)
1.2.3 Basic Data Analysis
8(3)
1.2.4 Software
11(1)
1.2.5 Examples
12(1)
1.3 Protein Structure Prediction
13(7)
1.3.1 Sequence Alignment and Secondary Structure Prediction
13(2)
1.3.2 Comparative Modelling Approaches
15(3)
1.3.3 Function Prediction
18(1)
1.3.4 Analysing the Quality of the Modelled Structure
18(1)
1.3.5 Software and Web Based Servers
19(1)
1.4 Computer-based Drug Design
20(19)
1.4.1 Pre-requisites for SBDD---Sampling Algorithms and Scoring Functions
20(4)
1.4.2 Structure Based Drug Design (SBDD)
24(2)
1.4.3 Ligand Based Drug Design (LBDD)
26(1)
1.4.4 Pharmacophores
26(1)
1.4.5 Compound Optimisation
27(2)
1.4.6 Software and Web Based Servers
29(1)
Acknowledgements
30(1)
References
30(9)
Chapter 2 Molecular Dynamics Computer Simulations of Biological Systems
39(30)
James W. Carter
Anna Sofia Tascini
John M. Seddon
Fernando Bresme
2.1 Introduction
39(1)
2.2 The Basics of Molecular Dynamics
40(6)
2.2.1 Force Fields for Biomolecular Simulations
41(3)
2.2.2 Multiscale Modelling
44(1)
2.2.3 Advanced Force Fields
45(1)
2.3 Extracting the Information from MD
46(8)
2.3.1 Free Energy Difference Between Two States
47(1)
2.3.2 Enhanced Configurational Sampling
47(2)
2.3.3 Simulating Rare Events
49(1)
2.3.4 Computing Elastic Properties in Biomolecular Simulations
50(4)
2.4 MD Simulation vs. Experiment
54(4)
2.4.1 NMR and MD: Structure and Dynamics
55(2)
2.4.2 Structure of Biomolecules and Diffraction: Solving the Phase Problem with MD
57(1)
2.5 Future Directions
58(3)
2.6 Conclusion
61(8)
Acknowledgements
63(1)
References
63(6)
Chapter 3 Designing Chemical Tools with Computational Chemistry
69(18)
Silvia Rinaldi
Giorgio Colombo
3.1 Introduction
69(3)
3.2 Structure Based Approaches for Chemical Biology
72(2)
3.3 Structural Dynamics as a Source of Novel Chemical Tools
74(5)
3.4 Combining Bioinformatics, Chemoinformatics and Structural Information to Explore Protein Functions
79(2)
3.5 Deep Networks and Big Data in the Discovery of New Drugs and Chemical Tools
81(2)
3.6 Conclusions and Perspectives
83(4)
References
84(3)
Chapter 4 Computational Design of Protein Function
87(21)
Marc Garcia-Borras
Kendall N. Houk
Gonzalo Jimenez-Oses
4.1 Introduction
87(2)
4.2 The `Inside-out' Design Protocol
89(5)
4.2.1 Description of the Method
89(3)
4.2.2 Enzymes Designed Though the `Inside-out' Approach: Kemp Eliminases
92(2)
4.3 QM/MM Approaches to Enzyme Design
94(7)
4.3.1 Description of the Methods
94(2)
4.3.2 Engineered Butyrylcholinesterase for Cocaine Detoxification
96(3)
4.3.3 Electron Transfer Reactions Catalysed by Metalloproteins
99(2)
4.4 Summary and Outlook
101(7)
Acknowledgements
102(1)
References
102(6)
Chapter 5 Computational Enzymology: Modelling Biological Catalysts
108(37)
Laura Masgrau
Angels Gonzalez-Lafont
Jose M. Lluch
5.1 Introduction
108(1)
5.2 General Framework
109(5)
5.2.1 The Transition State and the Energy Barrier
109(1)
5.2.2 Quantum Mechanics Molecular Mechanics (QM/MM) Methods
110(4)
5.3 Building the Model(s) of the Enzyme-Substrate Complex(es)
114(1)
5.3.1 Starting Structure and System Setup
114(1)
5.3.2 Molecular Dynamics Simulations
114(1)
5.4 Potential Energy Methods
115(7)
5.4.1 Reaction Path Calculation
115(2)
5.4.2 Transition State Localisation
117(1)
5.4.3 Analysis
118(4)
5.5 Free Energy Simulations
122(14)
5.5.1 Umbrella Sampling Method
123(4)
5.5.2 Free Energy Perturbation Theory
127(5)
5.5.3 String Method: Minimum Free Energy Paths
132(4)
5.6 Calculation of the Reaction Rate Constant
136(3)
5.6.1 Ensemble-averaged Variational Transition State Theory with Multi-dimensional Tunnelling (EA-VTST/MT)
136(3)
5.7 Further Considerations about the Relationship between the Activation Free Energy and the Extension of the Sampling of the Configurational Space
139(6)
References
141(4)
Chapter 6 Computational Chemistry Tools in Glycobiology: Modelling of Carbohydrate-Protein Interactions
145(20)
Alessandra Lacetera
M. Alvaro Berbis
Alessandra Nurisso
Jesus Jimenez-Barbero
Sonsoles Martin-Santamaria
6.1 What are the Carbohydrates?
145(2)
6.2 From Mono to Polysaccharides: An Overview of the Increasing Complexity
147(4)
6.2.1 Monosaccharides
147(1)
6.2.2 Disaccharides: The Glycosidic Linkage and the Exo-anomeric Effect
148(1)
6.2.3 Studying the Conformations Around the Glycosidic Linkage
149(1)
6.2.4 Oligosaccharides
149(1)
6.2.5 N-glycans
150(1)
6.2.6 Polysaccharides
150(1)
6.3 Computational Methodologies for the Study of Carbohydrates
151(2)
6.4 Force Fields for Carbohydrates
153(2)
6.5 Modelling Carbohydrate-Protein Interactions
155(4)
6.6 Conclusions
159(6)
Acknowledgements
159(1)
References
159(6)
Chapter 7 Molecular Modelling of Nucleic Acids
165(33)
Hansel Gomez
Jurgen Walther
Leonardo Dane
Ivan Ivani
Pablo D. Dans
Modesto Orozco
7.1 Introduction
165(1)
7.2 QM Methods
166(1)
7.2.1 Basic Methodological Description
166(1)
7.2.2 Examples of Use
167(1)
7.3 Hybrid QM/MM
167(3)
7.3.1 Basic Methodological Description
167(1)
7.3.2 Examples of Use
168(2)
7.4 Atomistic Force-field Simulations
170(7)
7.4.1 Basic Methodological Description
170(2)
7.4.2 Force-field Refinements
172(3)
7.4.3 Recent Examples of Force-field Studies of Nucleic Acids
175(2)
7.5 The Coarse-grain Approach
177(7)
7.5.1 Basic Methodological Description
178(4)
7.5.2 Coarse-grained Methods for Predicting RNA Structures
182(2)
7.6 Mesoscopic Models
184(4)
7.6.1 Basic Methodological Description
185(1)
7.6.2 Nucleosome Fibre Simulations
186(1)
7.6.3 Chromosome Simulations
187(1)
7.7 Conclusions
188(10)
Acknowledgements
188(1)
References
189(9)
Chapter 8 Uncovering GPCR and G Protein Function by Protein Structure Network Analysis
198(23)
Francesca Fanelli
Angelo Felline
8.1 Introduction
198(3)
8.2 Experimental
201(4)
8.2.1 Materials
201(1)
8.2.2 Methods
201(4)
8.3 Results and Discussion
205(11)
8.3.1 Modelling Allosteric Communication in GPCRs
205(8)
8.3.2 Modelling Allosteric Communication in G Proteins
213(3)
8.4 Conclusions
216(5)
Acknowledgements
216(1)
References
217(4)
Chapter 9 Current Challenges in the Computational Modelling of Molecular Recognition Processes
221(26)
Lucia Perez-Regidor
Joan Guzman-Caldentey
Carlos F. Rodriguez
Jean-Marc Billod
Juan Nogales
Sonsoles Martin-Santamaria
9.1 Modelling the Dynamics of the Proteins
221(3)
9.2 Three-dimensional Structure Prediction and Homology Modelling
224(1)
9.3 Modelling of Protein-Protein Interactions
225(1)
9.4 Prediction of Protein-Protein Interactions: Docking
226(3)
9.5 Computational Studies of Complex Protein Systems
229(3)
9.6 Computational Modelling of Nanostructures
232(5)
9.6.1 Modelling of Gold Nanoparticles
233(1)
9.6.2 Modelling of Nanowires
234(1)
9.6.3 Modelling of Nanotubes
235(1)
9.6.4 Modelling of Nanomachines
236(1)
9.7 Models of Signalling Networks
237(10)
Acknowledgements
240(1)
References
240(7)
Chapter 10 Novel Insights into Membrane Transport from Computational Methodologies
247(34)
Victoria Oakes
Carmen Domene
10.1 Introduction
247(1)
10.2 Computational Methods
248(4)
10.3 Unassisted Diffusion Across Lipid Bilayers
252(3)
10.4 Passive Transport by Ion Channels
255(4)
10.5 Facilitated Diffusion by Transporters
259(5)
10.6 Signalling via Receptors
264(4)
10.7 Conclusions
268(13)
Acknowledgements
268(1)
References
269(12)
Chapter 11 Application of Molecular Modelling to Speed-up the Lead Discovery Process
281(36)
Iuni M. L. Trist
Maurizio Botta
Anna Lucia Fallacara
11.1 Introduction
281(5)
11.1.1 The `Pharmaceutical Crisis'
281(1)
11.1.2 The Drug Discovery Process
282(2)
11.1.3 The Contribution of Molecular Modelling to Improve Drug Discovery
284(1)
11.1.4 Quantum and Molecular Mechanics in Drug Design
285(1)
11.1.5 An Introduction to Structure- and Ligand-based Molecular Modelling
285(1)
11.2 Structure-based Molecular Modelling
286(10)
11.2.1 Sources of 3D Structures
286(3)
11.2.2 Docking
289(2)
11.2.3 De Novo Drug Design
291(2)
11.2.4 Introducing Dynamics
293(3)
11.3 Ligand-based Molecular Modelling
296(9)
11.3.1 Similarity Searching: Same Shape, Same Activity
297(2)
11.3.2 Pharmacophore Modelling
299(1)
11.3.3 QSAR
300(4)
11.3.4 Use of In Silico Ligand-based Approaches: A Practical Case Study on Antitubercular Agents
304(1)
11.4 Conclusions
305(12)
Abbreviations
306(1)
Acknowledgements
307(1)
References
307(10)
Chapter 12 Molecular Modelling and Simulations Applied to Challenging Drug Discovery Targets
317(32)
Marco De Vivo
Matteo Masetti
Giulia Rossetti
12.1 Introduction
317(2)
12.2 Deciphering Metalloenzyme Catalysis via Computations
319(4)
12.2.1 Ribonuclease H
319(2)
12.2.2 Epoxide hydrolase
321(2)
12.3 Simulating Membrane Proteins
323(8)
12.3.1 Membrane Enzymes: The Case of FAAH
323(1)
12.3.2 Ion Channels: The Case of the Kv11.1 Channel
324(4)
12.3.3 GPCR: The Case of the Human Adenosine Receptor A2A Embedded in Neuronal-like Membrane
328(3)
12.4 Tackling Target Flexibility Through Simulations
331(7)
12.4.1 Lactate Dehydrogenase
331(2)
12.4.2 Intrinsically Disordered Proteins
333(2)
12.4.3 Targeting RNA in Trinucleotide Repeats Diseases
335(3)
12.5 Conclusions
338(11)
References
338(11)
Chapter 13 The Polypharmacology Gap Between Chemical Biology and Drug Discovery
349(22)
Albert A. Antolin
Jordi Mestres
13.1 Introduction: Chemical Biology and the Limits of Reductionism
349(4)
13.1.1 Polypharmacology in Drug Discovery
349(2)
13.1.2 Selectivity in Chemical Biology
351(2)
13.2 Systems Pharmacology: Databases and Methods
353(2)
13.2.1 Databases of Chemical, Biological and Pharmacological Data
353(1)
13.2.2 Computational Methods to Predict Polypharmacology
354(1)
13.3 Case Study 1: The Impact of Chemical Probe Polypharmacology on PARP Drug Discovery
355(8)
13.3.1 The History of PARP Biology: From Probes to Drugs
355(2)
13.3.2 PJ34: A PARP Chemical Tool Binding to PIM Kinases
357(3)
13.3.3 Differential Off-target Kinase Pharmacology Between Clinical PARP Inhibitors
360(3)
13.4 Case Study 2: Distant Off-target Pharmacology among MLP Chemical Probes
363(2)
13.5 Conclusions and Outlook
365(6)
Acknowledgements
366(1)
References
366(5)
Subject Index 371
Sonsoles Martín-Santamaría completed her PhD in Organic and Pharmaceutical Chemistry in 1998 at the University Complutense of Madrid. Following postdoctoral work at Imperial College London and at the Univercity of Alcalá, she joined the University CEU San Pablo in Madrid as a "Ramón y Cajal" Researcher. Since 2012 she has been the Principal Investigator of the Computational Chemical Biology group at the University CEU San Pablo and, since 2014, has been a staff scientist for CIB-CSIC, Madrid.