Structure-based Antibody Renumbering
- 1Takeda Development Center Americas, Inc.
Abstract
Antibody numbering schemes like IMGT and Chothia assign each residue in the variable domain a consistent index based on substructural position. These annotations standardize sequences with different lengths, facilitating tasks ranging from engineering of individual molecules during drug development to large-scale curation of training data for de novo antibody design. Yet existing algorithms for performing this annotation process, termed renumbering, rely exclusively on amino acid sequence for inference. Consequently, these can fail when presented with unnatural or unusual features such as long CDRs or engineered insertions. To address this gap, this work introduces Structure-based Antibody Renumbering, abbreviated SAbR, a method that assigns these annotations from structure alone. SAbR shows comparable performance to sequence-based renumbering methods on held-out expert-annotated structures, as well as high agreement with sequence-based methods in a larger benchmark of diverse structures. It also outperforms peer methods on de novo-designed molecules, and shows higher success rates than renumbering by structural alignment. However, limited generalization performance is observed in more distantly related systems. Overall, these results establish structure-based renumbering as a robust alternative for natural and engineered antibodies when such data is available. Code and model weights are available on GitHub.
Related
- Relation-Aware Graph-Hypergraph Networks for Antibody Sequence-Structure Co-Design
- AbRefine: Framework-Conditioned Refinement for Structure-Guided Antibody CDR Design
- Protein Design Viz (PDV): lightweight protein structure visualization studio with validated quantitative analytics and antibody-specific toolkit
- Comprehensive evaluation of antibody language models across multiple antibody design tasks
- Distinct profiles and antibody production by splenic and lymph node antibody-secreting cells in Trypanosoma cruzi infection
BibTeX
@misc{alamo2026structurebased,
title = {{Structure-based Antibody Renumbering}},
author = {D. del Alamo},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.16.751788},
url = {https://www.biorxiv.org/content/10.64898/2026.09.16.751788v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Structure-based Antibody Renumbering
AU - D. del Alamo
PY - 2026
DA - 2026/09/16
PB - bioRxiv
DO - 10.64898/2026.09.16.751788
UR - https://www.biorxiv.org/content/10.64898/2026.09.16.751788v1
AB - Antibody numbering schemes like IMGT and Chothia assign each residue in the variable domain a consistent index based on substructural position. These annotations standardize sequences with different lengths, facilitating tasks ranging from engineering of individual molecules during drug development to large-scale curation of training data for de novo antibody design. Yet existing algorithms for performing this annotation process, termed renumbering, rely exclusively on amino acid sequence for inference. Consequently, these can fail when presented with unnatural or unusual features such as long CDRs or engineered insertions. To address this gap, this work introduces Structure-based Antibody Renumbering, abbreviated SAbR, a method that assigns these annotations from structure alone. SAbR shows comparable performance to sequence-based renumbering methods on held-out expert-annotated structures, as well as high agreement with sequence-based methods in a larger benchmark of diverse structures. It also outperforms peer methods on de novo-designed molecules, and shows higher success rates than renumbering by structural alignment. However, limited generalization performance is observed in more distantly related systems. Overall, these results establish structure-based renumbering as a robust alternative for natural and engineered antibodies when such data is available. Code and model weights are available on GitHub.
ER -