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Human Germline Antibody Gene Segments Encode Polyspecific Antibodies

Overview of attention for article published in PLoS Computational Biology, April 2013
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (88th percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

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1 blog
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2 X users
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2 patents

Citations

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76 Dimensions

Readers on

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143 Mendeley
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2 CiteULike
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Title
Human Germline Antibody Gene Segments Encode Polyspecific Antibodies
Published in
PLoS Computational Biology, April 2013
DOI 10.1371/journal.pcbi.1003045
Pubmed ID
Authors

Jordan R. Willis, Bryan S. Briney, Samuel L. DeLuca, James E. Crowe, Jens Meiler

Abstract

Structural flexibility in germline gene-encoded antibodies allows promiscuous binding to diverse antigens. The binding affinity and specificity for a particular epitope typically increase as antibody genes acquire somatic mutations in antigen-stimulated B cells. In this work, we investigated whether germline gene-encoded antibodies are optimal for polyspecificity by determining the basis for recognition of diverse antigens by antibodies encoded by three VH gene segments. Panels of somatically mutated antibodies encoded by a common VH gene, but each binding to a different antigen, were computationally redesigned to predict antibodies that could engage multiple antigens at once. The Rosetta multi-state design process predicted antibody sequences for the entire heavy chain variable region, including framework, CDR1, and CDR2 mutations. The predicted sequences matched the germline gene sequences to a remarkable degree, revealing by computational design the residues that are predicted to enable polyspecificity, i.e., binding of many unrelated antigens with a common sequence. The process thereby reverses antibody maturation in silico. In contrast, when designing antibodies to bind a single antigen, a sequence similar to that of the mature antibody sequence was returned, mimicking natural antibody maturation in silico. We demonstrated that the Rosetta computational design algorithm captures important aspects of antibody/antigen recognition. While the hypervariable region CDR3 often mediates much of the specificity of mature antibodies, we identified key positions in the VH gene encoding CDR1, CDR2, and the immunoglobulin framework that are critical contributors for polyspecificity in germline antibodies. Computational design of antibodies capable of binding multiple antigens may allow the rational design of antibodies that retain polyspecificity for diverse epitope binding.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 143 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 1%
United States 1 <1%
Germany 1 <1%
Canada 1 <1%
Unknown 138 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 42 29%
Researcher 33 23%
Student > Bachelor 15 10%
Student > Master 5 3%
Professor 5 3%
Other 18 13%
Unknown 25 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 49 34%
Biochemistry, Genetics and Molecular Biology 30 21%
Immunology and Microbiology 9 6%
Chemistry 7 5%
Computer Science 6 4%
Other 13 9%
Unknown 29 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 09 January 2021.
All research outputs
#3,016,276
of 25,371,288 outputs
Outputs from PLoS Computational Biology
#2,672
of 8,960 outputs
Outputs of similar age
#24,661
of 205,935 outputs
Outputs of similar age from PLoS Computational Biology
#24
of 134 outputs
Altmetric has tracked 25,371,288 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 8,960 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has gotten more attention than average, scoring higher than 70% 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 205,935 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 88% of its contemporaries.
We're also able to compare this research output to 134 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.