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Computational Protein Design

Overview of attention for book
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 12: An Evolution-Based Approach to De Novo Protein Design.
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (51st percentile)
  • Good Attention Score compared to outputs of the same age and source (71st percentile)

Mentioned by

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Citations

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Chapter title
An Evolution-Based Approach to De Novo Protein Design.
Chapter number 12
Book title
Computational Protein Design
Published in
Methods in molecular biology, January 2017
DOI 10.1007/978-1-4939-6637-0_12
Pubmed ID
Book ISBNs
978-1-4939-6635-6, 978-1-4939-6637-0
Authors

Jeffrey R. Brender, David Shultis, Naureen Aslam Khattak, Yang Zhang, Brender, Jeffrey R., Shultis, David, Khattak, Naureen Aslam, Zhang, Yang

Editors

Ilan Samish

Abstract

EvoDesign is a computational algorithm that allows the rapid creation of new protein sequences that are compatible with specific protein structures. As such, it can be used to optimize protein stability, to resculpt the protein surface to eliminate undesired protein-protein interactions, and to optimize protein-protein binding. A major distinguishing feature of EvoDesign in comparison to other protein design programs is the use of evolutionary information in the design process to guide the sequence search toward native-like sequences known to adopt structurally similar folds as the target. The observed frequencies of amino acids in specific positions in the structure in the form of structural profiles collected from proteins with similar folds and complexes with similar interfaces can implicitly capture many subtle effects that are essential for correct folding and protein-binding interactions. As a result of the inclusion of evolutionary information, the sequences designed by EvoDesign have native-like folding and binding properties not seen by other physics-based design methods. In this chapter, we describe how EvoDesign can be used to redesign proteins with a focus on the computational and experimental procedures that can be used to validate the designs.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 31 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 5 16%
Student > Ph. D. Student 5 16%
Student > Bachelor 4 13%
Student > Doctoral Student 2 6%
Professor 2 6%
Other 5 16%
Unknown 8 26%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 14 45%
Agricultural and Biological Sciences 4 13%
Chemistry 2 6%
Chemical Engineering 1 3%
Engineering 1 3%
Other 0 0%
Unknown 9 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 January 2024.
All research outputs
#8,463,721
of 25,263,619 outputs
Outputs from Methods in molecular biology
#2,619
of 14,169 outputs
Outputs of similar age
#146,977
of 433,217 outputs
Outputs of similar age from Methods in molecular biology
#256
of 1,086 outputs
Altmetric has tracked 25,263,619 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 14,169 research outputs from this source. They receive a mean Attention Score of 3.5. This one has done well, scoring higher than 75% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 433,217 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.
We're also able to compare this research output to 1,086 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.