Rationalization and Design of the Complementarity Determining Region Sequences in an Antibody-Antigen Recognition Interface
Resource
PLOS ONE, 7(3), e33340
Journal
PLoS ONE
Pages
e33340
Date Issued
2012
Date
2012
Author(s)
Yu, Chung-Ming
Peng, Hung-Pin
Chen, Ing-Chien
Lee, Yu-Ching
Chen, Jun-Bo
Tsai, Keng-Chang
Chen, Ching-Tai
Chang, Jeng-Yih
Yang, Ei-Wen
Hsu, Po-Chiang
Jian, Jhih-Wei
Hsu, Hung-Ju
Chang, Hung-Ju
Hsu, Wen-Lian
Huang, Kai-Fa
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. ? 2012 Yu et al.
Other Subjects
amino acid; vasculotropin antibody; single chain fragment variable antibody; vasculotropin A; accuracy; antibody combining site; antibody structure; antigen antibody reaction; antigen binding; antigen recognition; article; binding affinity; complementarity determining region; controlled study; crystal structure; experimental design; high throughput screening; intermethod comparison; machine learning; mathematical computing; molecular interaction; molecular recognition; prediction; process development; process optimization; protein structure; quality control; sensitivity and specificity; sequence analysis; three dimensional imaging; antigen antibody reaction; artificial intelligence; chemical structure; chemistry; human; immunology; reproducibility; validation study; X ray crystallography; Animalia; Antigen-Antibody Reactions; Artificial Intelligence; Binding Sites, Antibody; Complementarity Determining Regions; Crystallography, X-Ray; Humans; Models, Molecular; Reproducibility of Results; Single-Chain Antibodies; Vascular Endothelial Growth Factor A
Type
journal article
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