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Molecular Modeling of Proteins

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Cover of 'Molecular Modeling of Proteins'

Table of Contents

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    Book Overview
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    Chapter 1 Molecular dynamics simulations.
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    Chapter 2 Transition Path Sampling with Quantum/Classical Mechanics for Reaction Rates
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    Chapter 3 Current status of protein force fields for molecular dynamics simulations.
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    Chapter 4 Lipid Membranes for Membrane Proteins
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    Chapter 5 Molecular Dynamics Simulations of Membrane Proteins
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    Chapter 6 Membrane-Associated Proteins and Peptides
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    Chapter 7 Coarse-grained force fields for molecular simulations.
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    Chapter 8 Tackling sampling challenges in biomolecular simulations.
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    Chapter 9 Calculation of Binding Free Energies
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    Chapter 10 The use of experimental structures to model protein dynamics.
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    Chapter 11 Computing ensembles of transitions with molecular dynamics simulations.
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    Chapter 12 Accelerated Molecular Dynamics and Protein Conformational Change: A Theoretical and Practical Guide Using a Membrane Embedded Model Neurotransmitter Transporter
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    Chapter 13 Simulations and Experiments in Protein Folding
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    Chapter 14 Comparative Modeling of Proteins
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    Chapter 15 De Novo Membrane Protein Structure Prediction
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    Chapter 16 NMR-Based Modeling and Refinement of Protein 3D Structures
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    Chapter 17 Methods for Predicting Protein–Ligand Binding Sites
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    Chapter 18 Information-Driven Structural Modelling of Protein–Protein Interactions
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    Chapter 19 Identifying putative drug targets and potential drug leads: starting points for virtual screening and docking.
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    Chapter 20 Molecular Docking to Flexible Targets
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Title
Molecular Modeling of Proteins
Published by
Springer New York, October 2014
DOI 10.1007/978-1-4939-1465-4
ISBNs
978-1-4939-1464-7, 978-1-4939-1465-4
Editors

Kukol, Andreas

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 249 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 249 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 55 22%
Student > Master 31 12%
Student > Bachelor 30 12%
Researcher 26 10%
Student > Doctoral Student 22 9%
Other 23 9%
Unknown 62 25%
Readers by discipline Count As %
Chemistry 64 26%
Biochemistry, Genetics and Molecular Biology 38 15%
Pharmacology, Toxicology and Pharmaceutical Science 18 7%
Engineering 14 6%
Agricultural and Biological Sciences 10 4%
Other 36 14%
Unknown 69 28%