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Computer-Aided Antibody Design

Overview of attention for book
Cover of 'Computer-Aided Antibody Design'

Table of Contents

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    Book Overview
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    Chapter 1 Antibody Sequence and Structure Analyses Using IMGT ® : 30 Years of Immunoinformatics
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    Chapter 2 Structural Classification of CDR-H3 in Single-Domain V H H Antibodies
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    Chapter 3 Computational Modeling of Antibody and T-Cell Receptor (CDR3 Loops)
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    Chapter 4 Molecular Dynamics Simulation for Investigating Antigen–Antibody Interaction
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    Chapter 5 Molecular Dynamics Methods for Antibody Design
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    Chapter 6 Probing Conformational Dynamics of Antibodies with Geometric Simulations
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    Chapter 7 PITHA: A Webtool to Predict Immunogenicity for Humanized and Fully Human Therapeutic Antibodies
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    Chapter 8 Thermal Stability Estimation of Single Domain Antibodies Using Molecular Dynamics Simulations
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    Chapter 9 Assessing and Engineering Antibody Stability Using Experimental and Computational Methods
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    Chapter 10 In Silico Prediction Method for Protein Asparagine Deamidation
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    Chapter 11 Structure-Based Optimization of Antibody-Based Biotherapeutics for Improved Developability: A Practical Guide for Molecular Modelers
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    Chapter 12 B-Cell Epitope Predictions Using Computational Methods
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    Chapter 13 Computational Epitope Prediction and Design for Antibody Development and Detection
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    Chapter 14 Information-Driven Antibody–Antigen Modelling with HADDOCK
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    Chapter 15 Structural Modeling of Adaptive Immune Responses to Infection
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    Chapter 16 Protein–Protein Interaction Modelling with the Fragment Molecular Orbital Method
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    Chapter 17 Structural Considerations in Affinity Maturation of Antibody-Based Biotherapeutic Candidates
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    Chapter 18 Structure-Based Affinity Maturation of Antibody Based on Double-Point Mutations
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    Chapter 19 Antibody Affinity Maturation Using Computational Methods: From an Initial Hit to Small-Scale Expression of Optimized Binders
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    Chapter 20 Optimizing Antibody–Antigen Binding Affinities with the ADAPT Platform
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    Chapter 21 Using Graph-Based Signatures to Guide Rational Antibody Engineering
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    Chapter 22 A Computational Framework for Determining the Breadth of Antibodies Against Highly Mutable Pathogens
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    Chapter 23 Analytical Method for Experimental Validation of Computer-Designed Antibody
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    Chapter 24 Computational Analysis of Antibody Paratopes for Antibody Sequences in Antibody Libraries
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    Chapter 25 Bioinformatic Analysis of Natively Paired VH:VL Antibody Repertoires for Antibody Discovery
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    Chapter 26 Analyzing Antibody Repertoire Using Next-Generation Sequencing and Machine Learning
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    Chapter 27 A Computational Pipeline for Predicting Cancer Neoepitopes
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
  • Good Attention Score compared to outputs of the same age (76th percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

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Title
Computer-Aided Antibody Design
Published by
Methods in molecular biology, January 2023
DOI 10.1007/978-1-0716-2609-2
ISBNs
978-1-07-162608-5, 978-1-07-162609-2
Editors

Tsumoto, Kouhei, Kuroda, Daisuke

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The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 10 November 2023.
All research outputs
#6,245,346
of 25,339,932 outputs
Outputs from Methods in molecular biology
#1,673
of 14,185 outputs
Outputs of similar age
#112,892
of 477,566 outputs
Outputs of similar age from Methods in molecular biology
#45
of 722 outputs
Altmetric has tracked 25,339,932 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 14,185 research outputs from this source. They receive a mean Attention Score of 3.5. This one has done well, scoring higher than 88% 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 477,566 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 76% of its contemporaries.
We're also able to compare this research output to 722 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 93% of its contemporaries.