Removing T-cell Epitopes with Computational Protein Design

May 22, 2014

King, C. et al. PNAS Early Edition (2014)

King_2014A — GFP was chosen for an experimental proof of concept. Shown here is the Rosetta design model for GFP deimmunization. (A) Published coordinates of sfGFP crystal structure. (B) Close-up view of immunodominant epitope. (C) Rosetta deimmunization design of (B). Cyan – design mutations; green – sfGFP; magenta – predicted epitopes.

Baker lab members combine machine learning with computational design to demonstrate immune silencing of protein targets in a recently published PNAS paper entitled “Removing T-cell epitopes with computational protein design”. Proteins represent the fastest-growing class of pharmaceuticals for a diverse range of clinical application. Computational design of new proteins has the potential to create a novel class of therapeutics with tunable biophysical properties. Immune responses, however, can make protein therapeutics ineffective or even dangerous. In this paper, Baker lab researchers describe a general computational protein design method for reducing immunogenicity by eliminating known and predicted T-cell epitopes 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.