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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 22: Computational Design of Membrane Curvature-Sensing Peptides.
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Chapter title
Computational Design of Membrane Curvature-Sensing Peptides.
Chapter number 22
Book title
Computational Protein Design
Published in
Methods in molecular biology, January 2017
DOI 10.1007/978-1-4939-6637-0_22
Pubmed ID
Book ISBNs
978-1-4939-6635-6, 978-1-4939-6637-0
Authors

Armando Jerome de Jesus, Hang Yin, de Jesus, Armando Jerome, Yin, Hang

Editors

Ilan Samish

Abstract

Computer simulations have become an indispensable tool in studying molecular biological systems. The unmatched spatial and temporal resolution that it offers enables for microscopic-level views into the dynamics and mechanics of biological systems. Recent advances in hardware resources have also opened up to computer simulations the investigation of longer timescale biological processes and larger systems. The study of membrane proteins or peptides especially benefits from simulations due to difficulties related to crystallization of such proteins in a membrane environment. In this chapter, we outline the method of molecular dynamics and how it is applied to simulations that involve a peptide and lipid bilayers. In particular, the simulation of a membrane-curvature sensing peptide is examined, and ways of employing computational simulations to design such peptides are discussed.

Mendeley readers

The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 28 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 6 21%
Student > Ph. D. Student 4 14%
Other 3 11%
Researcher 2 7%
Student > Postgraduate 2 7%
Other 5 18%
Unknown 6 21%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 6 21%
Agricultural and Biological Sciences 3 11%
Computer Science 2 7%
Engineering 2 7%
Business, Management and Accounting 1 4%
Other 5 18%
Unknown 9 32%