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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.
Overall attention for this book and its chapters
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (84th percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

Mentioned by

twitter
20 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
6 Dimensions

Readers on

mendeley
14 Mendeley
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Title
Computational Protein Design
Published by
Methods in molecular biology, January 2017
DOI 10.1007/978-1-4939-6637-0
ISBNs
978-1-4939-6635-6, 978-1-4939-6637-0
Editors

Ilan Samish

Twitter Demographics

The data shown below were collected from the profiles of 20 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 14 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 43%
Student > Ph. D. Student 3 21%
Student > Doctoral Student 1 7%
Student > Master 1 7%
Student > Bachelor 1 7%
Other 1 7%
Unknown 1 7%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 5 36%
Agricultural and Biological Sciences 2 14%
Immunology and Microbiology 2 14%
Environmental Science 1 7%
Chemistry 1 7%
Other 1 7%
Unknown 2 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 21 January 2018.
All research outputs
#1,743,600
of 15,390,521 outputs
Outputs from Methods in molecular biology
#310
of 9,024 outputs
Outputs of similar age
#40,656
of 266,129 outputs
Outputs of similar age from Methods in molecular biology
#1
of 10 outputs
Altmetric has tracked 15,390,521 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,024 research outputs from this source. They receive a mean Attention Score of 2.6. This one has done particularly well, scoring higher than 96% 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 266,129 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 84% of its contemporaries.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them