| Literature DB >> 24843166 |
Chris King1, Esteban N Garza2, Ronit Mazor3, Jonathan L Linehan4, Ira Pastan3, Marion Pepper2, David Baker5.
Abstract
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.Entities:
Keywords: Rosetta; biotherapeutics; deimmunization; immunotoxin; machine learning
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Year: 2014 PMID: 24843166 PMCID: PMC4060723 DOI: 10.1073/pnas.1321126111
Source DB: PubMed Journal: Proc Natl Acad Sci U S A ISSN: 0027-8424 Impact factor: 12.779