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Antibodies are proteins that can protect against disease using a variety of mechanisms, including binding to pathogens and targeting them for destruction. Structural modeling of antibody binding to the SARS-Cov-2 spike protein and how mutations might allow viruses to escape antibody neutralization has been previously investigated in Antibody Engineering Hackathons. The procedure for investigating immune escape can be used for students in affordable and accessible Course-Based Undergraduate Research Experiences (CUREs). In this work, we adapted and expanded the SARS-Cov-2 protocol to address new pathogens, including hookworms, Respiratory Syncytial Virus (RSV), Influenza, and Enterovirus D68. We found each presented unique challenges; however, these challenges present opportunities for student research. We describe how modifications to the SARS-Cov-2 protocol designed for SARS-CoV-2 could allow students to investigate the impact of mutations in each of these pathogens when binding to antibodies.
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This content will become publicly available on April 24, 2026
Bayesian Optimization of Antibodies Informed by a Generative Model of Evolving Sequences
To build effective therapeutics, biologists iteratively mutate antibody sequences to improve binding and stability. Proposed mutations can be informed by previous measurements or by learning from large antibody databases to predict only typical antibodies. Unfortunately, the space of typical antibodies is enormous to search, and experiments often fail to find suitable antibodies on a budget. We introduce Clone-informed Bayesian Optimization (CloneBO), a Bayesian optimization procedure that efficiently optimizes antibodies in the lab by teaching a generative model how our immune system optimizes antibodies. Our immune system makes antibodies by iteratively evolving specific portions of their sequences to bind their target strongly and stably, resulting in a set of related, evolving sequences known as a clonal family. We train a large language model, CloneLM, on hundreds of thousands of clonal families and use it to design sequences with mutations that are most likely to optimize an antibody within the human immune system. We propose to guide our designs to fit previous measurements with a twisted sequential Monte Carlo procedure. We show that CloneBO optimizes antibodies substantially more efficiently than previous methods in realistic in silico experiments and designs stronger and more stable binders in in vitro wet lab experiments.
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- Award ID(s):
- 2145492
- PAR ID:
- 10591497
- Publisher / Repository:
- International Conference on Learning Representations
- Date Published:
- Subject(s) / Keyword(s):
- Bayesian optimization, generative model, antibody, biological sequence
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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