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A New Clustering of Antibody CDR Loop Conformations
Open accessAbstract
Previous analyses of the complementarity-determining regions (CDRs) of antibodies have focused on a small number of "canonical" conformations for each loop. This is primarily the result of the work of Chothia and coworkers, most recently in 1997. Because of the widespread utility of antibodies, we have revisited the clustering of conformations of the six CDR loops with the much larger amount of structural information currently available. In this work, we were careful to use a high-quality data set by eliminating low-resolution structures and CDRs with high B-factors or high conformational energies. We used a distance function based on directional statistics and an effective clustering algorithm with affinity propagation. With this data set of over 300 nonredundant antibody structures, we were able to cover 28 CDR-length combinations (e.g., L1 length 11, or "L1-11" in our CDR-length nomenclature) for L1, L2, L3, H1, and H2. The Chothia analysis covered only 20 CDR-lengths. Only four of these had more than one conformational cluster, of which two could easily be distinguished by gene source (mouse/human; κ/λ) and one could easily be distinguished purely by the presence and the positions of Pro residues (L3-9). Thus, using the Chothia analysis does not require the complicated set of "structure-determining residues" that is often assumed. Of our 28 CDR-lengths, 15 have multiple conformational clusters, including 10 for which the Chothia analysis had only one canonical class. We have a total of 72 clusters for non-H3 CDRs; approximately 85% of the non-H3 sequences can be assigned to a conformational cluster based on gene source and/or sequence. We found that earlier predictions of "bulged" versus "nonbulged" conformations based on the presence or the absence of anchor residues Arg/Lys94 and Asp101 of H3 have not held up, since all four combinations lead to a majority of conformations that are bulged. Thus, the earlier analyses have been significantly enhanced by the increased data. We believe that the new classification will lead to improved methods for antibody structure prediction and design.
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“Conversely, CDR-H3 loops have high conformational diversity, even in cases where sequence similarity is high [37], and so CDR-H3 modeling is a significantly less constrained, more difficult problem.”
From Computational and artificial intelligence-based methods for antibody development (2023) · cites this paper as [37]
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Metadata
- Authors
- North, B., Lehmann, A., & Dunbrack, R. L.
- Affiliations
- Fox Chase Cancer Center (US)
- Corresponding author
- Dunbrack, Roland L.
- Venue
- Journal of Molecular Biology
- Publisher
- Elsevier BV
- Published
- 2011-02
- Volume
- 406
- Issue
- 2
- Pages
- 228-256
- ISSN
- 0022-2836 (ISSN-L)
- PMC
- PMC3065967
- OpenAlex
- W1978499958
- Cited by
- 380
- References
- 38
- Citations by year
- 2026: 12 · 2025: 14 · 2024: 24 · 2023: 22 · 2022: 24 · 2021: 29 · 2020: 33 · 2019: 39 · 2018: 25 · 2017: 29 · 2016: 24 · 2015: 27 · 2014: 40 · 2013: 15 · 2012: 11
- FWCI
- 4.95
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Molecular Biology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Cancer Institute (T32CA009035)
- National Institute of General Medical Sciences (R01-GM84453, P20 GM76222, P20GM076222, R01GM084453)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Immunology and Microbiology › Immunology › Monoclonal and Polyclonal Antibodies Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
- Keywords
- hypervariable region, conformational clustering, circular statistics, affinity propagation, antibody structure prediction, antibody design
- MeSH terms
- Algorithms, Amino Acid Sequence, Animals, Antibodies, Humans, Immunoglobulin Variable Region, Immunoglobulin Heavy Chains, Markov Chains, Molecular Sequence Data, Protein Conformation, Complementarity Determining Regions, Mice
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