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Related Experiment Video

Updated: Sep 19, 2025

Homogeneous Glycoconjugate Produced by Combined Unnatural Amino Acid Incorporation and Click-Chemistry for Vaccine Purposes

Homogeneous Glycoconjugate Produced by Combined Unnatural Amino Acid Incorporation and Click-Chemistry for Vaccine Purposes

Published on: December 19, 2020

Machine-guided dual-objective protein engineering for deimmunization and therapeutic functions.

Eric Wolfsberg¹, Jean-Sebastien Paul², Josh Tycko³

¹Department of Chemical Engineering, Stanford University, Stanford, CA 94305, USA.

|June 4, 2025

Summary

This study introduces a workflow to reduce immune responses to cell and gene therapies by optimizing protein domains. It uses machine learning to minimize immunogenic peptides, enhancing therapy safety and effectiveness.

Keywords:

protein design synthetic biology

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Last Updated: Sep 19, 2025

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Published on: March 25, 2014

Creating Highly Specific Chemically Induced Protein Dimerization Systems by Stepwise Phage Selection of a Combinatorial Single-Domain Antibody Library

Creating Highly Specific Chemically Induced Protein Dimerization Systems by Stepwise Phage Selection of a Combinatorial Single-Domain Antibody Library

Published on: January 14, 2020

Area of Science:

Biotechnology
Immunology
Genomic Medicine

Background:

Cell and gene therapies can trigger immune responses due to nonhuman proteins.
Modified human protein domains may still contain immunogenic peptides, posing a challenge for therapy development.

Purpose of the Study:

To develop a modular workflow for optimizing protein function and minimizing immunogenicity in therapeutic proteins.
To create safer and more effective cell and gene therapies by reducing anti-therapy immune responses.

Main Methods:

Utilized machine learning models to predict protein function and peptide-MHC presentation.
Applied workflow to remove immunogenic MHC II epitopes from transcriptional activation and RNA-binding domains.
Developed deimmunized zinc-finger arrays for targeted gene upregulation.

Main Results:

Successfully removed potentially immunogenic MHC II epitopes from protein domains.
Generated small-molecule-controllable transcription factors with human-derived DNA-binding domains.
Demonstrated upregulation of utrophin (UTRN) and SCN1A genes using deimmunized zinc-finger arrays.

Conclusions:

The presented modular workflow offers a strategy to enhance the safety and efficacy of cell and gene therapies.
Integration of machine learning for immunogenicity prediction and protein engineering is a promising approach for next-generation therapeutics.