Journal
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume 111, Issue 23, Pages 8577-8582Publisher
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1321126111
Keywords
deimmunization; machine learning; biotherapeutics; Rosetta; immunotoxin
Categories
Funding
- Defense Threat Reduction Agency
- National Institutes of Health, the National Cancer Institute, Center for Cancer Research
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available