Potential aggregation-prone regions in complementarity-determining regions of antibodies and their contribution towards antigen recognition: a computational analysis
Open accessAbstract
Purpose To analyze contribution of short aggregation-prone regions (APRs), which may self-associate via cross-beta motif and were earlier identified in therapeutic mAbs, towards antigen recognition via structural analyses of antibody-antigen complexes.Methods A dataset of 29 publically available high-resolution crystal structures of Fab-antigen complexes was collected. Contribution of APRs towards the surface areas of the Fabs buried by the cognate antigens was computed. Propensities of amino acids to occur in APRs and to be involved in antigen binding were compared. Coincidence between APRs and individual CDR loops was examined.Results All Fabs in the dataset contain at least one APR in CDR loops and adjacent framework beta-strands. The average contribution of APRs towards buried surface area of Fabs is 16.0 +/- 10.7%. Aggregation and antigen recognition may be coupled via aromatic residues (Tyr, Trp), which occur with high propensities in both APRs and antigen binding sites. APRs are infrequent in the heavy chain CDR 3 (H3) loops (7%), but are frequent in H2 loops (45%).Conclusions Co-incidence of APRs with antigen recognition sites can potentially lead to the loss of function upon aggregation. Rational structure-based design or selection strategies are suggested for biotherapeutics with improved druggability while maintaining potency.
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Framework residues can bury a substantial amount of surface area upon antigen-antibody complex formation.
“Analyses of high resolution crystal structures of antigen-antibody complexes show that the framework residues can bury a substantial amount of surface area upon complex formation [159, 160].”
From Computational approaches to therapeutic antibody design: established methods and emerging trends (2020) · cites this paper as [160]
CDR-located APRs also contribute significantly towards antigen binding.
“These CDR-located APRs also contribute significantly towards antigen binding [160], which help us rationalize how antibodies may lose potency upon aggregation and suggest potential strategies for selecting APRs for disruption without impacting biological activity.”
From Computational approaches to therapeutic antibody design: established methods and emerging trends (2020) · cites this paper as [160]
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References 78 · Cited by 79
- Authors
- Wang, X., Singh, S. K., & Kumar, S.
- Affiliations
- Pfizer (United States) (US)
- Corresponding author
- Kumar, Sandeep
- Venue
- Pharmaceutical Research
- Published
- 2010-04-27
- Volume
- 27
- Issue
- 8
- Pages
- 1512-1529
- ISSN
- 0724-8741 (ISSN-L)
- 1573-904X
- PMC
- PMC7088613
- OpenAlex
- W2068530958
- Cited by
- 79
- References
- 78
- Citations by year
- 2026: 5 · 2025: 2 · 2024: 1 · 2023: 1 · 2022: 3 · 2021: 6 · 2020: 6 · 2019: 5 · 2018: 6 · 2017: 2 · 2016: 4 · 2015: 8 · 2014: 4 · 2013: 8 · 2012: 11
- FWCI
- 6.76
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Pharmaceutical Research (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Pfizer
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Monoclonal and Polyclonal Antibodies Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein purification and stability
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Keywords
- aggregation-prone regions, hypervariable region, immune complexes, Fab fragment, antigen recognition, buried surface area, aromatic residues, CDR3 loop, therapeutic monoclonal antibodies, antibody aggregation, structure-based design, biologic therapy
- MeSH terms
- Amino Acid Sequence, Antibodies, Monoclonal, Antigen-Antibody Complex, Computer Simulation, Humans, Immunoglobulin Heavy Chains, Immunoglobulin Light Chains, Models, Molecular, Molecular Sequence Data, Protein Binding, Drug Design, Sequence Alignment, Complementarity Determining Regions
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