Removing T-cell epitopes with computational protein design
| Autor: | Christopher King, Jonathan L. Linehan, David Baker, Ronit Mazor, Esteban N. Garza, Marion Pepper, Ira Pastan |
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| Rok vydání: | 2014 |
| Předmět: |
Models
Molecular Support Vector Machine Virulence Factors Bacterial Toxins Green Fluorescent Proteins Molecular Sequence Data Protein design Epitopes T-Lymphocyte Exotoxins Computational biology Biology Protein Engineering Epitope Green fluorescent protein Mice HLA Antigens Immunotoxin Cell Line Tumor Animals Humans Pseudomonas exotoxin Deimmunization ADP Ribose Transferases Genetics Multidisciplinary Sequence Homology Amino Acid Immunotoxins Immunogenicity Proteins Protein engineering Biological Sciences Flow Cytometry Protein Structure Tertiary Mice Inbred C57BL Spectrometry Fluorescence Computer-Aided Design Immunization |
| Zdroj: | Proceedings of the National Academy of Sciences. 111:8577-8582 |
| ISSN: | 1091-6490 0027-8424 |
| DOI: | 10.1073/pnas.1321126111 |
| Popis: | Significance Proteins represent the fastest-growing class of pharmaceuticals for a diverse range of clinical applications. Computational protein design has the potential to create a novel class of therapeutics with tunable biophysical properties. However, the immune system reacts to T-cell epitope sequences in non-human proteins, leading to neutralization and elimination by the immune system. Here, we combine machine learning with structure-based protein design to identify and redesign T-cell epitopes without disrupting function of the target protein. We test the method experimentally, removing T-cell epitopes from GFP and Pseudomonas exotoxin A while maintaining function. |
| Databáze: | OpenAIRE |
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