Humanization of antibodies using a machine learning approach on large-scale repertoire data
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Abstract
MOTIVATION: Monoclonal antibody (mAb) therapeutics are often produced from non-human sources (typically murine), and can therefore generate immunogenic responses in humans. Humanization procedures aim to produce antibody therapeutics that do not elicit an immune response and are safe for human use, without impacting efficacy. Humanization is normally carried out in a largely trial-and-error experimental process. We have built machine learning classifiers that can discriminate between human and non-human antibody variable domain sequences using the large amount of repertoire data now available. RESULTS: Our classifiers consistently outperform the current best-in-class model for distinguishing human from murine sequences, and our output scores exhibit a negative relationship with the experimental immunogenicity of existing antibody therapeutics. We used our classifiers to develop a novel, computational humanization tool, Hu-mAb, that suggests mutations to an input sequence to reduce its immunogenicity. For a set of therapeutic antibodies with known precursor sequences, the mutations suggested by Hu-mAb show substantial overlap with those deduced experimentally. Hu-mAb is therefore an effective replacement for trial-and-error humanization experiments, producing similar results in a fraction of the time. AVAILABILITY AND IMPLEMENTATION: Hu-mAb (humanness scoring and humanization) is freely available to use at opig.stats.ox.ac.uk/webapps/humab. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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The Deane group used nearly 2 billion antibody sequences available in OAS across organisms, labeling all human sequences as positive and others as negative, and trained a random forest classifier model to produce a 'humanness score'.
“Moreover, the Deane group used nearly 2 billion antibody sequences, available in OAS, across organisms where all human sequences were labeled as positive class, while others were labeled as negative class, and trained a random forest classifier model to produce a 'humanness score'.”
From Computational and artificial intelligence-based methods for antibody development (2023) · cites this paper as [32]
The random forest classifier consists of 200 decision trees and each decision tree individually separates the input sequences into classes (human or other species) using their features.
“The random forest classifier consists of 200 decision trees and each decision tree individually separates the input sequences into classes (human or other species) using their features.”
From Computational and artificial intelligence-based methods for antibody development (2023) · cites this paper as [32]
The score was used to construct a humanization tool called Hu-mAb, which suggests mutations to reduce immunogenicity risk.
“Furthermore, the score was used to construct a humanization tool called Hu-mAb, which suggests mutations to reduce immunogenicity risk [32].”
From Computational and artificial intelligence-based methods for antibody development (2023) · cites this paper as [32]
The output score not only distinguishes human V genes from non-human sequences in the variable region but also indicates the level of immunogenicity risks.
“The output score not only distinguishes human V genes from non-human sequences in the variable region but also indicates the level of immunogenicity risks [32].”
From Computational and artificial intelligence-based methods for antibody development (2023) · cites this paper as [32]
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A humanized anti-human adenovirus 55 monoclonal antibody with good neutralization ability
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Designing stable humanized antibodies
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An antibody developability triaging pipeline exploiting protein language models
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Matrixed CDR grafting: A neoclassical framework for antibody humanization and developability
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Molecular Modeling Methods in the Development of Affine and Specific Protein-Binding Agents
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An adaptive autoregressive diffusion approach to design active humanized antibodies and nanobodies
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An adaptive autoregressive diffusion approach to design active humanized antibody and nanobodyPreprint
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Antibody immunogenicity prediction and optimization with ImmunoSeqPreprint
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Computer‐Aided Technology for Bioactive Protein Design and Clinical Application
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Antibody Humanization via Protein Language Model and Neighbor RetrievalPreprint
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AbBFN2: A flexible antibody foundation model based on Bayesian Flow NetworksPreprint
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Artificial intelligence advancements in monoclonal antibody development technology
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Single-Cell B-Cell Sequencing to Generate Natively Paired scFab Yeast Surface Display Libraries
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Repurposing of therapeutic antibodies against dengue virus envelope protein receptor binding domain
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- Authors
- Marks, C., Hummer, A. M., Chin, M., & Deane, C. M.
- Affiliations
- University of Oxford (GB)
- Corresponding author
- Deane, Charlotte M
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2021-06-10
- Volume
- 37
- Issue
- 22
- Pages
- 4041-4047
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC8760955
- OpenAlex
- W3169434891
- Cited by
- 124
- References
- 24
- Citations by year
- 2026: 24 · 2025: 32 · 2024: 31 · 2023: 19 · 2022: 14 · 2021: 4
- FWCI
- 8.94
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Medical Research Council (MR/N013468/1)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Monoclonal and Polyclonal Antibodies Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- antibody humanization, monoclonal antibodies, immunogenicity, murine antibodies, antibody variable domains, machine learning classification, mutation prediction, therapeutic antibodies
- MeSH terms
- Machine Learning, Animals, Antibodies, Monoclonal, Mice
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