The diversity of H 3 loops determines the antigen‐binding tendencies of antibody CDR loops
Open accessAbstract
Of the complementarity-determining regions (CDRs) of antibodies, H3 loops, with varying amino acid sequences and loop lengths, adopt particularly diverse loop conformations. The diversity of H3 conformations produces an array of antigen recognition patterns involving all the CDRs, in which the residue positions actually in contact with the antigen vary considerably. Therefore, for a deeper understanding of antigen recognition, it is necessary to relate the sequence and structural properties of each residue position in each CDR loop to its ability to bind antigens. In this study, we proposed a new method for characterizing the structural features of the CDR loops and obtained the antigen-binding ability of each residue position in each CDR loop. This analysis led to a simple set of rules for identifying probable antigen-binding residues. We also found that the diversity of H3 loop lengths and conformations affects the antigen-binding tendencies of all the CDR loops.
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CDRH3 is very important for antigen recognition.
“CDRH3 possesses the highest sequence and structural diversity of the six CDRs [33] and is very important for antigen recognition [34, 35].”
From Computational approaches to therapeutic antibody design: established methods and emerging trends (2020) · cites this paper as [34]
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Cytoscape: A software Environment for integrated models of biomolecular interaction networks
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003) · Genome Research · doi:10.1101/gr.1239303 · Cited by 51,888 · see citing works
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 10,994 · see citing works
Integration of biological networks and gene expression data using Cytoscape
Cline, M. S., Smoot, M., Cerami, E., Kuchinsky, A., Landys, N., Workman, C., Christmas, R., Avila-Campilo, I., Creech, M., Gross, B., et al. (2007) · Nature Protocols · doi:10.1038/nprot.2007.324 · Cited by 2,507 · see citing works
Satisfying Hydrogen Bonding Potential in Proteins
McDonald, I. K., & Thornton, J. M. (1994) · Journal of Molecular Biology · doi:10.1006/jmbi.1994.1334 · Cited by 2,136 · see citing works
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 · see citing works
Wu, T. T., & Kabat, E. A. (1970) · The Journal of Experimental Medicine · doi:10.1084/jem.132.2.211 · Cited by 1,340 · see citing works
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 1,185 · see citing works
Lefranc, M., Pommie, C., Ruíz, M., Giudicelli, V., Foulquier, E., Truong, L., Contet, V., & Lefranc, G. (2002) · Developmental & Comparative Immunology · doi:10.1016/s0145-305x(02)00039-3 · Cited by 823 · see citing works
Lefranc, M., Pommie, C., Kaas, Q., Duprat, É., Bosc, N., Guiraudou, D., Jean, C., Ruíz, M., da Piedade, I., Rouard, M., et al. (2004) · Developmental & Comparative Immunology · doi:10.1016/j.dci.2004.07.003 · Cited by 808 · see citing works
Standard conformations for the canonical structures of immunoglobulins 1 1Edited by I. A. Wilson
Al‐Lazikani, B., Lesk, A. M., & Chothia, C. (1997) · Journal of Molecular Biology · doi:10.1006/jmbi.1997.1354 · Cited by 707 · see citing works
MUSTANG: A multiple structural alignment algorithm
Konagurthu, A. S., Whisstock, J. C., Stuckey, P. J., & Lesk, A. M. (2006) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.20921 · Cited by 701 · see citing works
SAbDab: the structural antibody database
Dunbar, J., Krawczyk, K., Leem, J., Baker, T., Fuchs, A., Georges, G., Shi, J., & Deane, C. M. (2014) · Nucleic Acids Research · doi:10.1093/nar/gkt1043 · Cited by 674 · see citing works
Conformation of β-hairpins in protein structures
Sibanda, B. L., Blundell, T. L., & Thornton, J. M. (1989) · Journal of Molecular Biology · doi:10.1016/0022-2836(89)90583-4 · Cited by 475 · see citing works
Antibody-antigen Interactions: Contact Analysis and Binding Site Topography
MacCallum, R. M., Martin, A. C. R., & Thornton, J. M. (1996) · Journal of Molecular Biology · doi:10.1006/jmbi.1996.0548 · Cited by 473 · see citing works
A new clustering of antibody CDR loop conformations
North, B., Lehmann, A., & Dunbrack, R. L. (2011) · Journal of Molecular Biology · doi:10.1016/j.jmb.2010.10.030 · Cited by 380 · see citing works
Morea, V., Tramontano, A. L., Rustici, M., Chothia, C., & Lesk, A. M. (1998) · Journal of Molecular Biology · doi:10.1006/jmbi.1997.1442 · Cited by 327 · see citing works
Analyzing and visualizing residue networks of protein structures
Doncheva, N. T., Klein, K., Domingues, F. S., & Albrecht, M. (2011) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2011.01.002 · Cited by 259 · see citing works
Martin, A. C. R., & Thornton, J. M. (1996) · Journal of Molecular Biology · doi:10.1006/jmbi.1996.0617 · Cited by 246 · see citing works
Structural classification of CDR‐H3 in antibodies
Shirai, H., Kidera, A., & Nakamura, H. (1996) · FEBS Letters · doi:10.1016/s0014-5793(96)01252-5 · Cited by 223 · see citing works
H3‐rules: identification of CDR‐H3 structures in antibodies
Shirai, H., Kidera, A., & Nakamura, H. (1999) · FEBS Letters · doi:10.1016/s0014-5793(99)00821-2 · Cited by 166 · see citing works
Identification of specificity‐determining residues in antibodies
PADLAN, E. A., Abergel, C., & Tipper, J. P. (1995) · The FASEB Journal · doi:10.1096/fasebj.9.1.7821752 · Cited by 138 · see citing works
Kunik, V., Ashkenazi, S., & Ofran, Y. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks480 · Cited by 132 · see citing works
Structural classification of CDR‐H3 revisited: A lesson in antibody modeling
Kuroda, D., Shirai, H., Kobori, M., & Nakamura, H. (2008) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.22087 · Cited by 127 · see citing works
eF-site and PDBjViewer: database and viewer for protein functional sites
Kinoshita, K., & Nakamura, H. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth073 · Cited by 122 · see citing works
Structural consensus among antibodies defines the antigen binding site
Kunik, V., Peters, B., & Ofran, Y. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002388 · Cited by 117 · see citing works
A Systematic Comparison of Free and Bound Antibodies Reveals Binding-Related Conformational Changes
Sela‐Culang, I., Alon, S., & Ofran, Y. (2012) · The Journal of Immunology · doi:10.4049/jimmunol.1201493 · Cited by 115 · see citing works
Kunik, V., & Ofran, Y. (2013) · Protein Engineering Design and Selection · doi:10.1093/protein/gzt027 · Cited by 96 · see citing works
Prediction of site-specific interactions in antibody–antigen complexes: the proABC method and server
Olimpieri, P. P., Chailyan, A., Tramontano, A., & Marcatili, P. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt369 · Cited by 90 · see citing works
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Raybould, M. I. J., Marks, C., Krawczyk, K., Taddese, B., Nowak, J., Lewis, A. P., Bujotzek, A., Shi, J., & Deane, C. M. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1810576116 · Cited by 357 · see citing works
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Norman, R. A., Ambrosetti, F., Bonvin, A. M. J. J., Colwell, L. J., Kelm, S., Kumar, S., & Krawczyk, K. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbz095 · Cited by 219 · see citing works
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- Authors
- Tsuchiya, Y., & Mizuguchi, K.
- Affiliations
- National Institute of Biomedical Innovation, Health and Nutrition (JP)
- Corresponding author
- Tsuchiya, Yuko
- Mizuguchi, Kenji
- Venue
- Protein Science
- Publisher
- Wiley
- Published
- 2016-01-20
- Volume
- 25
- Issue
- 4
- Pages
- 815-825
- ISSN
- 0961-8368 (ISSN-L)
- 1469-896X
- PMC
- PMC4941225
- OpenAlex
- W2246074199
- Cited by
- 86
- References
- 28
- Citations by year
- 2026: 18 · 2025: 12 · 2024: 13 · 2023: 7 · 2022: 6 · 2021: 6 · 2020: 5 · 2019: 9 · 2018: 5 · 2017: 2 · 2016: 3
- FWCI
- 5.49
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Protein Science (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Japan Society for the Promotion of Science (Grants-in-Aid for Scientific Research) (25430186 and 25293079)
- Japan Agency for Medical Research and Development (“The adjuvant database project”) to K.M
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Monoclonal and Polyclonal Antibodies Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Keywords
- hypervariable region, antigen recognition, loop length, antibody-antigen binding
- MeSH terms
- Algorithms, Amino Acid Sequence, Antibodies, Antigens, Models, Molecular, Protein Conformation, Crystallography, X-Ray, Complementarity Determining Regions
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