Fine-tuning Boltz for Antibody-Antigen Binding
Prediction
Name
kim-jiwonckm-meng-eecs-2025-thesis.pdf
Description
Thesis PDF
Size
3.09 MB
Format
Adobe PDF
Checksum (MD5)
cbfbe2c90c4bf79bd4db46f9af9cb61c
Author(s)
Kim, Ji Won
Advisor(s)
Barzilay, Regina
Date Issued
September 2025
Publisher
Massachusetts Institute of Technology
Abstract
Accurate prediction of antibody-antigen binding is a central challenge in computational immunology. Its direct implication for therapeutic antibody design and vaccine development has made it one of the most rapidly growing fields. Recent advances in protein language models and structure prediction have provided new tools for modeling, yet these approaches often fall short in capturing the fine-grained features that drive binding specificity in antibody and antigens. This thesis evaluates multiple strategies for improving predictive performance. First, we investigate a custom multiple sequence alignment (MSA) experiment. Standard Boltz-2 training relies on MSAs from broad protein databases, which capture global diversity but under-represent lineage-specific constraints. To address this, we constructed antibody-specific MSAs to test whether restricting the search space to antibody repertoires improves model learning. Unfortunately, gains in downstream binding prediction were limited, suggesting that further work needs to be done in training models for specific databases in the first place. Our second line of investigation focused on fine-tuning Boltz-2, a generative structural foundation model, using curated antibody–antigen data. By leveraging Boltz-2’s internal sequence embeddings, we trained a predictive model for binding affinity. This approach yielded stronger ROC performance compared to baseline models, achieving a validation AUROC of 0.645, demonstrating the advantages of structural generative priors for antibody–antigen binding prediction.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
In Copyright - Educational Use Permitted
Copyright retained by author(s)
Persistent DSpace Link
