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Computational Protein Design

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Cover of 'Computational Protein Design'

Table of Contents

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    Book Overview
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    Chapter 1 The Framework of Computational Protein Design.
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    Chapter 2 Achievements and Challenges in Computational Protein Design.
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    Chapter 3 Production of Computationally Designed Small Soluble- and Membrane-Proteins: Cloning, Expression, and Purification.
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    Chapter 4 Deterministic Search Methods for Computational Protein Design.
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    Chapter 5 Geometric Potentials for Computational Protein Sequence Design.
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    Chapter 6 Modeling Binding Affinity of Pathological Mutations for Computational Protein Design.
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    Chapter 7 Multistate Computational Protein Design with Backbone Ensembles.
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    Chapter 8 Integration of Molecular Dynamics Based Predictions into the Optimization of De Novo Protein Designs: Limitations and Benefits.
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    Chapter 9 Applications of Normal Mode Analysis Methods in Computational Protein Design.
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    Chapter 10 Computational Protein Design Under a Given Backbone Structure with the ABACUS Statistical Energy Function.
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    Chapter 11 Computational Protein Design Through Grafting and Stabilization.
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    Chapter 12 An Evolution-Based Approach to De Novo Protein Design.
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    Chapter 13 Parallel Computational Protein Design.
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    Chapter 14 Computational Protein Design
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    Chapter 15 OSPREY Predicts Resistance Mutations Using Positive and Negative Computational Protein Design.
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    Chapter 16 Evolution-Inspired Computational Design of Symmetric Proteins.
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    Chapter 17 A Protocol for the Design of Protein and Peptide Nanostructure Self-Assemblies Exploiting Synthetic Amino Acids.
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    Chapter 18 Probing Oligomerized Conformations of Defensin in the Membrane.
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    Chapter 19 Computational Design of Ligand Binding Proteins.
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    Chapter 20 EpiSweep: Computationally Driven Reengineering of Therapeutic Proteins to Reduce Immunogenicity While Maintaining Function.
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    Chapter 21 Computational Tools for Aiding Rational Antibody Design.
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    Chapter 22 Computational Design of Membrane Curvature-Sensing Peptides.
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    Chapter 23 Computational Tools for Allosteric Drug Discovery: Site Identification and Focus Library Design.
Attention for Chapter 5: Geometric Potentials for Computational Protein Sequence Design.
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Chapter title
Geometric Potentials for Computational Protein Sequence Design.
Chapter number 5
Book title
Computational Protein Design
Published in
Methods in molecular biology, January 2017
DOI 10.1007/978-1-4939-6637-0_5
Pubmed ID
Book ISBNs
978-1-4939-6635-6, 978-1-4939-6637-0
Authors

Jie Li, Patrice Koehl

Editors

Ilan Samish

Abstract

Computational protein sequence design is the rational design based on computer simulation of new protein molecules to fold to target three-dimensional structures, with the ultimate goal of designing novel functions. It requires a good understanding of the thermodynamic equilibrium properties of the protein of interest. Here, we consider the contribution of the solvent to the stability of the protein. We describe implicit solvent models, focusing on approximations of their nonpolar components using geometric potentials. We consider the surface area (SA) model in which the nonpolar solvation free energy is expressed as a sum of the contributions of all atoms, assumed to be proportional to their accessible surface areas (ASAs). We briefly review existing numerical and analytical approaches that compute the ASA. We describe in more detail the alpha shape theory as it provides a unifying mathematical framework that enables the analytical calculations of the surface area of a macromolecule represented as a union of balls.

Mendeley readers

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 1 50%
Unknown 1 50%
Readers by discipline Count As %
Agricultural and Biological Sciences 1 50%
Unknown 1 50%