Evidence map›Paper›PMID 22457753›Full record

ArticlePloS one2012

Rationalization and design of the complementarity determining region sequences in an antibody-antigen recognition interface.

Chung-Ming Yu, Hung-Pin Peng, Ing-Chien Chen, Yu-Ching Lee, Jun-Bo Chen, Keng-Chang Tsai, Ching-Tai Chen, Jeng-Yih Chang, Ei-Wen Yang, Po-Chiang Hsu and 7 more

Open access · goldAbstract readValidation Study
In one paragraph

Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed
6.0field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

32 citing papers in PubMed, 48 citations in OpenAlex.

  1. Prediction of Paratope-Epitope Pairs Using Convolutional Neural Networks.International journal of molecular sciences · 2024
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  13. Anti-ROR1 scFv-EndoG as a Novel Anti-Cancer Therapeutic DrugAsian Pacific journal of cancer prevention : APJCP · 2018
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  18. Rational design of antibodies targeting specific epitopes within intrinsically disordered proteins.Proceedings of the National Academy of Sciences of the United States of America · 2015
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors at 5 institutions in 1 country.

Chung-Ming YuGenomics Research Center, Academia Sinica, Taipei, Taiwan.
Hung-Pin Peng
Ing-Chien Chen
Yu-Ching Lee
Jun-Bo Chen
Keng-Chang Tsai
Ching-Tai Chen
Jeng-Yih Chang
Ei-Wen Yang
Po-Chiang Hsu
Jhih-Wei Jian
Hung-Ju Hsu
Hung-Ju Chang
Wen-Lian Hsu
Kai-Fa Huang
Alex Che Ma
An-Suei Yang
Genomics Research Center, Academia Sinica · TWInstitute of Biological Chemistry, Academia Sinica · TWInstitute of Information Science, Academia Sinica · TWNational Taiwan University · TWNational Yang Ming Chiao Tung University · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Protein-protein interactions are critical determinants in biological systems. Engineered proteins binding to specific areas on protein surfaces could lead to therapeutics or diagnostics for treating diseases in humans. But designing epitope-specific protein-protein interactions with computational atomistic interaction free energy remains a difficult challenge. Here we show that, with the antibody-VEGF (vascular endothelial growth factor) interaction as a model system, the experimentally observed amino acid preferences in the antibody-antigen interface can be rationalized with 3-dimensional distributions of interacting atoms derived from the database of protein structures. Machine learning models established on the rationalization can be generalized to design amino acid preferences in antibody-antigen interfaces, for which the experimental validations are tractable with current high throughput synthetic antibody display technologies. Leave-one-out cross validation on the benchmark system yielded the accuracy, precision, recall (sensitivity) and specificity of the overall binary predictions to be 0.69, 0.45, 0.63, and 0.71 respectively, and the overall Matthews correlation coefficient of the 20 amino acid types in the 24 interface CDR positions was 0.312. The structure-based computational antibody design methodology was further tested with other antibodies binding to VEGF. The results indicate that the methodology could provide alternatives to the current antibody technologies based on animal immune systems in engineering therapeutic and diagnostic antibodies against predetermined antigen epitopes.

Indexed as

Antigen-Antibody ReactionsComplementarity Determining RegionsArtificial IntelligenceBinding Sites, AntibodyCrystallography, X-RayHumansModels, MolecularReproducibility of ResultsSingle-Chain AntibodiesVascular Endothelial Growth Factor AComplementarity Determining RegionsSingle-Chain AntibodiesVascular Endothelial Growth Factor A

Identifiers

PMID22457753
PMCPMC3310866
OpenAlexW2076095273

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.