Real-time exploration of the billion-scale human repertoire for precursor frequency analysis and rational antibody design
8 affiliations
- 1Institute for Drug Discovery, Faculty of Medicine, Leipzig University
- 2Center for Scalable Data Analytics and Artificial Intelligence ScaDS.AI
- 3Open Molecular Software Foundation
- 4School of Embedded Composite Artificial Intelligence SECAI
- 5Institute for Computer Science, Wilhelm Ostwald Institute for Physical and Theoretical Chemistry, University Leipzig
- 6Department of Chemistry, Department of Pharmacology, Center for Structural Biology, Institute of Chemical Biology, Center for Applied Artificial Intelligence in Protein Dynamics, Vanderbilt University
- 7Department of Immunology and Microbiology, The Scripps Research Institute
- 8Vaccine and Immunotherapy Center, The Wistar Institute
Abstract
The success of germline-targeting vaccines and rational antibody design depends on quantifying the population-level frequencies of specific antibody features, such as CDR lengths, sequence motifs, germline usage or preferred chain pairings, across the natural human repertoire. The maturation of high-throughput sequencing has generated large database repertoires containing billions of sequences, allowing researchers to assess naïve B-cell precursor frequencies and evaluate biologically plausible chain combinations. However, leveraging this scale of data remains a significant bottleneck, as current databases trap scientists between computationally demanding bulk downloads, restrictive, single-purpose web interfaces or outdated datasets. To address this gap, we introduce ABHunter, an automatically updated, open-access framework designed to explore and filter the healthy-human subset of the Observed Antibody Space (OAS) database, which currently stands at ~1.86-billion-sequence as of January 2026. Accessible via any web browser, the platform executes searches with median times ranging from 3.7 seconds for simple gene usage profiles to 20.8 seconds for complex paratope motifs, with full sequence retrieval completed in 118 seconds for 1 million hits. The interface allows users to quantify antibody feature frequencies to assess the natural abundance of targeted B-cells across various isotypes (e.g., utilizing IgM for precursor B-cell analysis), as well as map native repertoire contexts to scaffold isolated features. Additionally, it predicts biologically plausible inter-chain pairings for therapeutic design by using a statistical co-occurrence model that extrapolates pairing probabilities from the limited natively paired subset comprising 0.03% of the data across six donors. Furthermore, every search generates a comprehensive statistical landscape, detailing underlying complementarity-determining regions length distributions, amino acid compositions, and gene coupling patterns. For custom analysis and integration into bioinformatic pipelines, the optimized data structure reduces the storage footprint to 106 GB, allowing the entire tool to run locally on a standard laptop. ABHunter eliminates the compromise between accessibility and flexibility, transforming large-scale sequencing data into a universally accessible resource for next-generation therapeutic discovery. The platform is freely available at abhunter.org .
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BibTeX
@article{schlegel2026realtime,
title = {{Real-time exploration of the billion-scale human repertoire for precursor frequency analysis and rational antibody design}},
author = {Tom U. Schlegel and Jannis von Blanckenburg de Riz and Jakob R. Riccabona and Franz Dietzmeyer and Julia Koehler Leman and Jens Meiler and Clara T. Schoeder and Torben Schiffner},
journal = {Frontiers in Immunology},
year = {2026},
month = oct,
doi = {10.3389/fimmu.2026.1958625},
url = {https://www.frontiersin.org/articles/10.3389/fimmu.2026.1958625/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Real-time exploration of the billion-scale human repertoire for precursor frequency analysis and rational antibody design
AU - Tom U. Schlegel
AU - Jannis von Blanckenburg de Riz
AU - Jakob R. Riccabona
AU - Franz Dietzmeyer
AU - Julia Koehler Leman
AU - Jens Meiler
AU - Clara T. Schoeder
AU - Torben Schiffner
PY - 2026
DA - 2026/10/16
JO - Frontiers in Immunology
DO - 10.3389/fimmu.2026.1958625
UR - https://www.frontiersin.org/articles/10.3389/fimmu.2026.1958625/full
AB - The success of germline-targeting vaccines and rational antibody design depends on quantifying the population-level frequencies of specific antibody features, such as CDR lengths, sequence motifs, germline usage or preferred chain pairings, across the natural human repertoire. The maturation of high-throughput sequencing has generated large database repertoires containing billions of sequences, allowing researchers to assess naïve B-cell precursor frequencies and evaluate biologically plausible chain combinations. However, leveraging this scale of data remains a significant bottleneck, as current databases trap scientists between computationally demanding bulk downloads, restrictive, single-purpose web interfaces or outdated datasets. To address this gap, we introduce ABHunter, an automatically updated, open-access framework designed to explore and filter the healthy-human subset of the Observed Antibody Space (OAS) database, which currently stands at ~1.86-billion-sequence as of January 2026. Accessible via any web browser, the platform executes searches with median times ranging from 3.7 seconds for simple gene usage profiles to 20.8 seconds for complex paratope motifs, with full sequence retrieval completed in 118 seconds for 1 million hits. The interface allows users to quantify antibody feature frequencies to assess the natural abundance of targeted B-cells across various isotypes (e.g., utilizing IgM for precursor B-cell analysis), as well as map native repertoire contexts to scaffold isolated features. Additionally, it predicts biologically plausible inter-chain pairings for therapeutic design by using a statistical co-occurrence model that extrapolates pairing probabilities from the limited natively paired subset comprising 0.03% of the data across six donors. Furthermore, every search generates a comprehensive statistical landscape, detailing underlying complementarity-determining regions length distributions, amino acid compositions, and gene coupling patterns. For custom analysis and integration into bioinformatic pipelines, the optimized data structure reduces the storage footprint to 106 GB, allowing the entire tool to run locally on a standard laptop. ABHunter eliminates the compromise between accessibility and flexibility, transforming large-scale sequencing data into a universally accessible resource for next-generation therapeutic discovery. The platform is freely available at abhunter.org .
SN - 1664-3224
ER -