期刊
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
卷 111, 期 23, 页码 8577-8582出版社
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1321126111
关键词
deimmunization; machine learning; biotherapeutics; Rosetta; immunotoxin
资金
- Defense Threat Reduction Agency
- National Institutes of Health, the National Cancer Institute, Center for Cancer Research
Immune responses can make protein therapeutics ineffective or even dangerous. We describe a general computational protein design method for reducing immunogenicity by eliminating known and predicted T-cell epitopes and maximizing the content of human peptide sequences without disrupting protein structure and function. We show that the method recapitulates previous experimental results on immunogenicity reduction, and we use it to disrupt T-cell epitopes in GFP and Pseudomonas exotoxin A without disrupting function.
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