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Identification of Mouse and Human Antibody Repertoires by Next-Generation Sequencing
Published on: March 15, 2019
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Efficient evolution of human antibodies from general protein language models
Brian L Hie1,2, Varun R Shanker3,4, Duo Xu5,3
1Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA. brianhie@stanford.edu.
Nature Biotechnology
|April 24, 2023
Summary
Protein language models accelerate antibody evolution by suggesting plausible mutations. This approach rapidly improves antibody binding affinity, thermostability, and neutralization activity for therapeutic applications.
Area of Science:
- Artificial intelligence
- Protein engineering
- Immunology
Background:
- Natural evolution explores vast sequence spaces for beneficial mutations.
- Learning from evolutionary strategies can guide artificial protein evolution.
- Protein language models (PLMs) offer a novel approach to protein design.
Purpose of the Study:
- To investigate the efficacy of general protein language models in guiding antibody evolution.
- To assess the ability of PLM-suggested mutations to improve antibody binding affinity, stability, and function.
- To determine if PLM-guided evolution is efficient in terms of variants screened and rounds of evolution.
Main Methods:
- Language-model-guided affinity maturation of seven human antibodies.
- Screening of 20 or fewer antibody variants per antibody across two rounds of laboratory evolution.
- Evaluation of binding affinities, thermostability, and viral neutralization activity against Ebola and SARS-CoV-2 pseudoviruses.
Main Results:
- Significant improvements in binding affinities for four mature antibodies (up to sevenfold) and three unmatured antibodies (up to 160-fold).
- Enhanced thermostability and viral neutralization activity observed in many evolved antibody designs.
- Demonstrated successful application of PLMs for antibody evolution without prior information on antigen, specificity, or structure.
Conclusions:
- General protein language models can efficiently guide antibody evolution by suggesting evolutionarily plausible mutations.
- PLM-guided evolution is a rapid and effective method for improving antibody properties, including affinity, stability, and neutralization.
- The approach shows broad applicability across diverse protein families and selection pressures, including antibiotic resistance and enzyme activity.
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