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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 19: Computational Design of Ligand Binding Proteins.
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Chapter title
Computational Design of Ligand Binding Proteins.
Chapter number 19
Book title
Computational Protein Design
Published in
Methods in molecular biology, January 2017
DOI 10.1007/978-1-4939-6637-0_19
Pubmed ID
Book ISBNs
978-1-4939-6635-6, 978-1-4939-6637-0
Authors

Christine E. Tinberg, Sagar D. Khare

Editors

Ilan Samish

Abstract

The ability to design novel small-molecule binding sites in proteins is a stringent test of our understanding of the principles of molecular recognition, and would have many practical applications, in synthetic biology and medicine. Here, we describe a computational method in the context of the macromolecular modeling suite Rosetta to designing proteins with sites featuring predetermined interactions to ligands of choice. The required inputs for the method are a model of the small molecule and the desired interactions (e.g., hydrogen bonding, electrostatics, steric packing), and a set of crystallographic structures of proteins containing existing or predicted binding pockets. Constellations of backbones surrounding the putative pocket are searched for compatibility with the desired binding site conception using RosettaMatch and surrounding amino acid side chain identities are optimized using RosettaDesign. Validation of the design is performed using metrics that evaluate the interface energy of the predicted binding pose, the preformation of key binding site features in the apo-state, and the local compatibility of the designed sequence changes with the wild type backbone structure, and top-ranking candidate designs are generated for experimental validation. This approach can allow for the creation of novel binding sites and for the rational tuning of specificity for congeneric ligands by altering the programmed interactions by design, thus offering a general computational protocol for construction and modulation of protein-small molecule interfaces.

Mendeley readers

The data shown below were compiled from readership statistics for 175 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 2%
Uruguay 1 <1%
Germany 1 <1%
Canada 1 <1%
United Kingdom 1 <1%
Unknown 168 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 43 25%
Researcher 38 22%
Student > Master 21 12%
Student > Bachelor 19 11%
Student > Doctoral Student 11 6%
Other 26 15%
Unknown 17 10%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 53 30%
Agricultural and Biological Sciences 42 24%
Chemistry 31 18%
Computer Science 7 4%
Pharmacology, Toxicology and Pharmaceutical Science 5 3%
Other 16 9%
Unknown 21 12%