Humanization of antibodies using a statistical inference approach
Open accessAbstract
Antibody humanization is a key step in the preclinical phase of the development of therapeutic antibodies, originally developed and tested in non-human models (most typically, in mouse). The standard technique of Complementarity-Determining Regions (CDR) grafting into human Framework Regions of germline sequences has some important drawbacks, in that the resulting sequences often need further back-mutations to ensure functionality and/or stability. Here we propose a new method to characterize the statistical distribution of the sequences of the variable regions of human antibodies, that takes into account phenotypical correlations between pairs of residues, both within and between chains. We define a "humanness score" of a sequence, comparing its performance in distinguishing human from murine sequences, with that of some alternative scores in the literature. We also compare the score with the experimental immunogenicity of clinically used antibodies. Finally, we use the humanness score as an optimization function and perform a search in the sequence space, starting from different murine sequences and keeping the CDR regions unchanged. Our results show that our humanness score outperforms other methods in sequence classification, and the optimization protocol is able to generate humanized sequences that are recognized as human by standard homology modelling tools.
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More sophisticated methods that take positional correlations between residues into account could be superior to Humanness score and HSC.
“Humanness score and HSC are primarily sequence distance-based approaches and it has been demonstrated that more sophisticated methods that take the positional correlations between residues into account could be superior [187, 188].”
From Computational approaches to therapeutic antibody design: established methods and emerging trends (2020) · cites this paper as [187]
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- Authors
- Clavero-Álvarez, A., Di Mambro, T., Perez-Gaviro, S., Magnani, M., & Bruscolini, P.
- Affiliations
- Universidad de Zaragoza (ES)
- University of Urbino (IT)
- Corresponding author
- Bruscolini, Pierpaolo
- Venue
- Scientific Reports
- Published
- 2018-10-04
- Volume
- 8
- Issue
- 1
- Pages
- 14820-14820
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC6172228
- OpenAlex
- W2893755931
- Cited by
- 47
- References
- 34
- Citations by year
- 2026: 2 · 2025: 10 · 2024: 9 · 2023: 3 · 2022: 4 · 2021: 7 · 2020: 6 · 2019: 5 · 2018: 1
- FWCI
- 3.28
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- CINECA IRIS Institutional Research information system (University of Urbino) (submittedVersion, cc-by-nc-nd)
- Zaguan (University of Zaragoza Repository) (submittedVersion, cc-by)
- 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
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- antibody humanization, hypervariable region, CDR grafting, immunogenicity, homology modeling, murine antibodies, statistical inference
- MeSH terms
- Animals, Antibodies, Monoclonal, Humans, Immunoglobulin Variable Region, Immunologic Factors, Recombinant Proteins, Computational Biology, Complementarity Determining Regions, Mice, Biostatistics
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