HeavyBuilder: Analysis of High-Throughput of Antibody Heavy Chain Repertoires in the Structural Space

The vast majority of immunoglobulin sequence data publicly available is of antibody heavy chain-only, as it is easier and cheaper to sequence than the paired heavy and light chains. However, structural characterization and analysis of these sequences in scale has been limited, either by not enough resolution in the prediction, or high resource demand. Here, we introduce HeavyBuilder, a deep learning-based tool for rapid and accurate structure prediction of antibody heavy chains. Available as a web server (https://opig.stats.ox.ac.uk/webapps/HeavyBuilder), and python API (https://github.com/oxpig/HeavyBuilder2). Based on the ImmuneBuilder architecture, it predicts up to 1 million structures in 3.13 days using a single GPU, outperforming AlphaFold2 and IgFold in speed while maintaining comparable accuracy. We applied HeavyBuilder to over 11 million sequences from 73 immune repertoires enabling high-throughput structural analysis. Our study reveals widespread convergent structures, that is, structures from genetically distinct clones; and divergent clonotypes, similar sequences adopting multiple structures. Furthermore, we demonstrate that structure-based similarity search recovers more known antibodies than sequence-based methods. HeavyBuilder offers a scalable solution for structural interrogation of large-scale immune repertoires, opening new avenues for antibody discovery and immune repertoire profiling.

Recommended citation: Gervasio et.al. (2025). "HeavyBuilder: Analysis of High-Throughput of Antibody Heavy Chain Repertoires in the Structural Space." Journal of Molecular Biology. 6(12).
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