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ABangle: characterising the VH-VL orientation in antibodies
Abstract
The binding site of an antibody is formed between the two variable domains, VH and VL, of its antigen binding fragment (Fab). Understanding how VH and VL orientate with respect to one another is important both for studying the mechanisms of antigen specificity and affinity and improving antibody modelling, docking and engineering. Different VH-VL orientations are commonly described using relative measures such as root-mean-square deviation. Recently, the orientation has also been characterised using the absolute measure of a VH-VL packing angle. However, a single angle cannot fully describe all modes of orientation. Here, we present a method which fully characterises VH-VL orientation in a consistent and absolute sense using five angles (HL, HC1, LC1, HC2 and LC2) and a distance (dc). Additionally, we provide a computational tool, ABangle, to allow the VH-VL orientation for any antibody to be automatically calculated and compared with all other known structures. We compare previous studies and show how the modes of orientation being identified relate to movements of different angles. Thus, we are able to explain why different studies identify different structural clusters and different residues as important. Given this result, we then identify those positions and their residue identities which influence each of the angular measures of orientation. Finally, by analysing VH-VL orientation in bound and unbound forms, we find that antibodies specific for protein antigens are significantly more flexible in their unbound form than antibodies specific for hapten antigens. ABangle is freely available at http://opig.stats.ox.ac.uk/webapps/abangle.
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“The second step involves accurate determination of the relative orientation of the VH and VL domains, which is crucial to determine the correct shape of the paratope [107, 109].”
From Computational approaches to therapeutic antibody design: established methods and emerging trends (2020) · cites this paper as [109]
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
The PyMOL Molecular Graphics System
DeLano, W. L. (2002) · Medical Entomology and Zoology · Cited by 18,147
Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences
Li, W.-Z., & Godzik, A. (2006) · Bioinform. · doi:10.1093/bioinformatics/btl158 · Cited by 10,996
The protein data bank: A computer-based archival file for macromolecular structures
Bernstein, F. C., Koetzle, T. F., Williams, G. J. B., Meyer, E. F., Brice, M. D., Rodgers, J. R., Kennard, O., Shimanouchi, T., & Tasumi, M. (1977) · Journal of Molecular Biology · doi:10.1016/s0022-2836(77)80200-3 · Cited by 8,574
The Kolmogorov-Smirnov Test for Goodness of Fit
Massey, F. J. (1951) · Journal of the American Statistical Association · doi:10.1080/01621459.1951.10500769 · Cited by 7,210
TM-align: a protein structure alignment algorithm based on the TM-score
Zhang, Y. (2005) · Nucleic Acids Research · doi:10.1093/nar/gki524 · Cited by 3,774
Reshaping human antibodies for therapy
Riechmann, L., Clark, M., Waldmann, H., & Winter, G. P. (1988) · Nature · doi:10.1038/332323a0 · Cited by 1,565
Canonical structures for the hypervariable regions of immunoglobulins
Chothia, C., & Lesk, A. M. (1987) · Journal of Molecular Biology · doi:10.1016/0022-2836(87)90412-8 · Cited by 1,406
Wu, T. T., & Kabat, E. A. (1970) · The Journal of Experimental Medicine · doi:10.1084/jem.132.2.211 · Cited by 1,340
Conformations of immunoglobulin hypervariable regions
Chothia, C., Lesk, A. M., Tramontano, A. L., Levitt, M., Smith‐Gill, S. J., Air, G. M., Sheriff, S., PADLAN, E. A., Davies, D. R., Tulip, W. R., et al. (1989) · Nature · doi:10.1038/342877a0 · Cited by 1,191
Research works printed but not linked: 1
- 1908, BULL SOC VAUDENSE SCI NATURELLES, 44, 223
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Hie, B., Shanker, V. R., Xu, D., Bruun, T. U. J., Weidenbacher, P. A. B., Tang, S., Wu, W. C. H., Pak, J. E., & Kim, P. S. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01763-2 · Cited by 472
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“Specific algorithms have been developed for this and incorporated into available software packages such as AbAngle [109].”
BackgroundMethodAntibody structure prediction using interpretable deep learning
Ruffolo, J. A., Sulam, J., & Gray, J. J. (2022) · Patterns · doi:10.1016/j.patter.2021.100406 · Cited by 216
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Dunbar, J., Krawczyk, K., Leem, J., Marks, C., Nowak, J., Regep, C., Georges, G. J., Kelm, S., Popovic, B., & Deane, C. M. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw361 · Cited by 202
Parks, T., Mirabel, M., Kado, J. H., Auckland, K., Nowak, J., Rautanen, A., Mentzer, A. J., Marijon, É., Jouven, X., Perman, M. L., et al. (2017) · Nature Communications · doi:10.1038/ncomms14946 · Cited by 142
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Robin, G., Sato, Y., Desplancq, D., Rochel, N., Weiss, É., & Martineau, P. (2014) · Journal of Molecular Biology · doi:10.1016/j.jmb.2014.08.013 · Cited by 112
Koenig, P., Lee, C. V., Walters, B. T., Janakiraman, V., Stinson, J., Patapoff, T. W., & Fuh, G. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1613231114 · Cited by 107
Dondelinger, M., Filée, P., Sauvage, E., Quinting, B., Muyldermans, S., Galleni, M., & Vandevenne, M. S. (2018) · Frontiers in Immunology · doi:10.3389/fimmu.2018.02278 · Cited by 107
Weitzner, B. D., Kuroda, D., Marze, N. A., Xu, J., & Gray, J. J. (2014) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24534 · Cited by 94
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Kuroda, D., & Gray, J. J. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw197 · Cited by 81
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Cisneros, A., Nargi, R. S., Parrish, E. H., Haliburton, C. M., Meiler, J., & Crowe, J. E. (2019) · Molecular Systems Design & Engineering · doi:10.1039/c8me00080h · Cited by 7
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Sangha, A. K., Dong, J., Williamson, L. E., Hashiguchi, T., Saphire, E. O., Crowe, J. E., & Meiler, J. (2017) · Structure · doi:10.1016/j.str.2017.10.005 · Cited by 6
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Metadata
- Authors
- Dunbar, J., Fuchs, A., Shi, J., & Deane, C. M.
- Affiliations
- University of Oxford (GB)
- UCB Pharma (United Kingdom) (GB)
- ORCID
- Venue
- Protein Engineering Design and Selection
- Publisher
- Oxford University Press (OUP)
- Published
- 2013-05-24
- Volume
- 26
- Issue
- 10
- Pages
- 611-620
- ISSN
- 0269-2139 (ISSN-L)
- 1741-0126
- 1741-0134
- OpenAlex
- W2154135618
- Cited by
- 125
- References
- 40
- Citations by year
- 2026: 5 · 2025: 11 · 2024: 6 · 2023: 22 · 2022: 11 · 2021: 12 · 2020: 8 · 2019: 11 · 2018: 7 · 2017: 9 · 2016: 7 · 2015: 7 · 2014: 7 · 2013: 2
- FWCI
- 3.43
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Protein Engineering Design and Selection (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Engineering and Physical Sciences Research Council
- 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
- antibody structure, Fab fragment, antibody binding sites, structural clusters, antibody flexibility, antibody engineering
- MeSH terms
- Animals, Antibodies, Haptens, Humans, Immunoglobulin Variable Region, Immunoglobulin Light Chains, Models, Molecular, Protein Conformation, Consensus Sequence, Computational Biology, Mice, Rats
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