The H3 loop of antibodies shows unique structural characteristics
Open accessAbstract
The H3 loop in the Complementarity Determining Region of antibodies plays a key role in their ability to bind the diverse space of potential antigens. It is also exceptionally difficult to model computationally causing a significant hurdle for in silico development of antibody biotherapeutics. In this article, we show that most H3s have unique structural characteristics which may explain why they are so challenging to model. We found that over 75% of H3 loops do not have a sub-Angstrom structural neighbor in the non-antibody world. Also, in a comparison with a nonredundant set of all protein fragments over 30% of H3 loops have a unique structure, with the average for all of other loops being less than 3%. We further observed that this structural difference can be seen at the level of four residue fragments where H3 loops present numerous novel conformations, and also at the level of individual residues with Tyrosine and Glycine often found in energetically unfavorable conformations. Proteins 2017; 85:1311-1318. © 2017 Wiley Periodicals, Inc.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
CDRH3 possesses the highest sequence and structural diversity of the six CDRs.
“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 [33]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Kabsch, W., & Sander, C. (1983) · Biopolymers · doi:10.1002/bip.360221211 · Cited by 15,320 · see citing works
Murzin, A. G., Brenner, S. E., Hubbard, T., & Chothia, C. (1995) · Journal of Molecular Biology · doi:10.1016/s0022-2836(05)80134-2 · Cited by 6,412 · see citing works
THE GENERALIZATION OF ‘STUDENT'S’ PROBLEM WHEN SEVERAL DIFFERENT POPULATION VARLANCES ARE INVOLVED
WELCH, B. L. (1947) · Biometrika · doi:10.1093/biomet/34.1-2.28 · Cited by 4,135 · see citing works
Somatic generation of antibody diversity
Tonegawa, S. (1983) · Nature · doi:10.1038/302575a0 · Cited by 4,005 · see citing works
Somatic generation of antibody diversity.
Matthyssens, G., Hozumi, N., & Tonegawa, S. (1976) · PubMed · Cited by 3,237 · see citing works
The Generalization of `Student's' Problem when Several Different Population Variances are Involved
Welch, B. L. (1947) · Biometrika · doi:10.2307/2332510 · Cited by 1,946 · see citing works
PISCES: a protein sequence culling server
Wang, G., & Dunbrack, R. L. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg224 · Cited by 1,664 · 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
Replacing the complementarity-determining regions in a human antibody with those from a mouse
Jones, P. T., Dear, P. H., Foote, J., Neuberger, M. S., & Winter, G. (1986) · Nature · doi:10.1038/321522a0 · 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
Gough, J., Karplus, K. J., Hughey, R., & Chothia, C. (2001) · Journal of Molecular Biology · doi:10.1006/jmbi.2001.5080 · Cited by 1,200 · see citing works
A discussion of the solution for the best rotation to relate two sets of vectors
Kabsch, W. (1978) · Acta Crystallographica Section A · doi:10.1107/s0567739478001680 · Cited by 1,151 · 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
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
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
ANARCI: antigen receptor numbering and receptor classification
Dunbar, J., & Deane, C. M. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv552 · Cited by 421 · 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
Zemlin, M., Klinger, M., Link, J. M., Zemlin, C., Bauer, K., Engler, J. A., Schroeder, H. W., & Kirkham, P. M. (2003) · Journal of Molecular Biology · doi:10.1016/j.jmb.2003.10.007 · Cited by 327 · see citing works
Honegger, A., & Plückthun, A. (2001) · Journal of Molecular Biology · doi:10.1006/jmbi.2001.4662 · Cited by 249 · see citing works
Birtalan, S., Zhang, Y., Fellouse, F. A., Shao, L., Schaefer, G., & Sidhu, S. S. (2008) · Journal of Molecular Biology · doi:10.1016/j.jmb.2008.01.093 · Cited by 244 · see citing works
Computer-aided antibody design
Kuroda, D., Shirai, H., Jacobson, M. P., & Nakamura, H. (2012) · Protein Engineering Design and Selection · doi:10.1093/protein/gzs024 · Cited by 223 · see citing works
Therapeutic antibodies: past, present and future
Leavy, O. (2010) · Nature reviews. Immunology · doi:10.1038/nri2763 · Cited by 216 · see citing works
Antibody Engineering for the Development of Therapeutic Antibodies
Kim, S. J., Park, Y.-W., & Hong, H. J. (2005) · Molecules and Cells · doi:10.1016/s1016-8478(23)25245-0 · Cited by 167 · see citing works
Second antibody modeling assessment (AMA-II)
Almagro, J. C., Teplyakov, A., Luo, J., Sweet, R. W., Kodangattil, S., Hernandez‐Guzman, F., & Gilliland, G. L. (2014) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24567 · Cited by 144 · see citing works
Kunik, V., Ashkenazi, S., & Ofran, Y. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks480 · Cited by 132 · see citing works
Comparison of systematic search and database methods for constructing segments of protein structure
Fidelis, K., Stern, P. S., Bacon, D., & Moult, J. (1994) · "Protein Engineering, Design and Selection" · doi:10.1093/protein/7.8.953 · Cited by 126 · see citing works
Analysis of temperature factor distribution in high‐resolution protein structures
Parthasarathy, S., & Murthy, M. R. N. (1997) · Protein Science · doi:10.1002/pro.5560061208 · Cited by 113 · see citing works
The Origin of CDR H3 Structural Diversity
Weitzner, B. D., Dunbrack, R. L., & Gray, J. J. (2015) · Structure · doi:10.1016/j.str.2014.11.010 · Cited by 101 · see citing works
Protein structural alignments and functional genomics
Irving, J. A., Whisstock, J. C., & Lesk, A. M. (2001) · Proteins Structure Function and Bioinformatics · doi:10.1002/1097-0134(20010215)42:3 · Cited by 87 · see citing works
Trends in Antibody Sequence Changes during the Somatic Hypermutation Process
Clark, L. A., Ganesan, S., Papp, S., & van Vlijmen, H. (2006) · The Journal of Immunology · doi:10.4049/jimmunol.177.1.333 · Cited by 85 · see citing works
Reconstruction of Protein Backbones from the BriX Collection of Canonical Protein Fragments
Baeten, L., Reumers, J., Tur, V., Stricher, F., Lenaerts, T., Serrano, L. G., Rousseau, F., & Schymkowitz, J. W. H. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000083 · Cited by 64 · see citing works
Structural diversity in a human antibody germline library
Teplyakov, A., Obmolova, G., Malia, T., Luo, J., Muzammil, S., Sweet, R. W., Almagro, J. C., & Gilliland, G. L. (2016) · mAbs · doi:10.1080/19420862.2016.1190060 · Cited by 62 · see citing works
Vincent, K. J., & Zurini, M. G. M. (2012) · Biotechnology Journal · doi:10.1002/biot.201200250 · Cited by 54 · see citing works
Length-independent structural similarities enrich the antibody CDR canonical class model
Nowak, J., BAKER, T. S., Georges, G. J., Kelm, S., Klostermann, S., Shi, J., Sridharan, S., & Deane, C. M. (2016) · mAbs · doi:10.1080/19420862.2016.1158370 · Cited by 54 · see citing works
Babor, M., & Kortemme, T. (2008) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.22293 · Cited by 53 · see citing works
Building a Better Fragment Library for De Novo Protein Structure Prediction
de Oliveira, S. H. P., Shi, J., & Deane, C. M. (2015) · PLoS ONE · doi:10.1371/journal.pone.0123998 · Cited by 30 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
ImmuneBuilder: Deep-Learning models for predicting the structures of immune proteins
Abanades, B., Wong, W. K., Boyles, F., Georges, G. J., Bujotzek, A., & Deane, C. M. (2023) · Communications Biology · doi:10.1038/s42003-023-04927-7 · Cited by 316 · see citing works
Computational approaches to therapeutic antibody design: established methods and emerging trends
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
“Wang and coworkers have examined molecular sequences of commercially available mAb drug products and shown that they contain multiple well defined aggregation prone motifs often located in their CDRs [206].”
BackgroundInfluentialAntibody 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 · see citing works
Deciphering the language of antibodies using self-supervised learning
Leem, J., Mitchell, L., Farmery, J. H. R., Barton, J., & Galson, J. D. (2022) · Patterns · doi:10.1016/j.patter.2022.100513 · Cited by 154 · see citing works
Ig-VAE: Generative modeling of protein structure by direct 3D coordinate generation
Eguchi, R. R., Choe, C. A., & Huang, P.-S. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1010271 · Cited by 141 · see citing works
Third generation antibody discovery methods: in silico rational design
Sormanni, P., Aprile, F. A., & Vendruscolo, M. (2018) · Chemical Society Reviews · doi:10.1039/c8cs00523k · Cited by 136 · see citing works
Assessing developability early in the discovery process for novel biologics
Fernández‐Quintero, M. L., Ljungars, A., Waibl, F., Greiff, V., Andersen, J. T., Gjølberg, T. T., Jenkins, T. P., Voldborg, B. G. R., Grav, L. M., Kumar, S., et al. (2023) · mAbs · doi:10.1080/19420862.2023.2171248 · Cited by 99 · see citing works
Fernández‐Quintero, M. L., Loeffler, J. R., Kraml, J., Kahler, U., Kamenik, A. S., & Liedl, K. R. (2019) · Frontiers in Immunology · doi:10.3389/fimmu.2018.03065 · Cited by 96 · see citing works
Human adaptive immune receptor repertoire analysis—Past, present, and future
Nielsen, S. C. A., & Boyd, S. D. (2018) · Immunological Reviews · doi:10.1111/imr.12667 · Cited by 80 · see citing works
Wilman, W., Wróbel, S., Bielska, W., Deszyński, P., Dudzic, P., Jaszczyszyn, I., Kaniewski, J., Młokosiewicz, J., Rouyan, A., Satława, T., et al. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac267 · Cited by 83 · see citing works
CDR-H3 loop ensemble in solution – conformational selection upon antibody binding
Fernández‐Quintero, M. L., Kraml, J., Georges, G. J., & Liedl, K. R. (2019) · mAbs · doi:10.1080/19420862.2019.1618676 · Cited by 75 · see citing works
How repertoire data are changing antibody science
Marks, C., & Deane, C. M. (2020) · Journal of Biological Chemistry · doi:10.1074/jbc.rev120.010181 · Cited by 68 · see citing works
Jeliazkov, J. R., Sljoka, A., Kuroda, D., Tsuchimura, N., Katoh, N., Tsumoto, K., & Gray, J. J. (2018) · Frontiers in Immunology · doi:10.3389/fimmu.2018.00413 · Cited by 64 · see citing works
Large scale paired antibody language models
Kenlay, H., Dreyer, F. A., Kovaltsuk, A., Miketa, D., Pires, D. E. V., & Deane, C. M. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012646 · Cited by 64 · see citing works
Local and Global Rigidification Upon Antibody Affinity Maturation
Fernández‐Quintero, M. L., Loeffler, J. R., Bacher, L. M., Waibl, F., Seidler, C. A., & Liedl, K. R. (2020) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2020.00182 · Cited by 59 · see citing works
Tabasinezhad, M., Talebkhan, Y., Wenzel, W., Rahimi, H., Omidinia, E., & Mahboudi, F. (2019) · Immunology Letters · doi:10.1016/j.imlet.2019.06.009 · Cited by 59 · see citing works
Antibodies exhibit multiple paratope states influencing VH–VL domain orientations
Fernández‐Quintero, M. L., Pomarici, N. D., Math, B. A., Kroell, K. B., Waibl, F., Bujotzek, A., Georges, G. J., & Liedl, K. R. (2020) · Communications Biology · doi:10.1038/s42003-020-01319-z · Cited by 57 · see citing works
Computational design of antibodies
Fischman, S., & Ofran, Y. (2018) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2018.04.007 · Cited by 56 · see citing works
Challenges in antibody structure prediction
Fernández-Quintero, M. L., Kokot, J., Waibl, F., Fischer, A.-L. M., Quoika, P. K., Deane, C. M., & Liedl, K. R. (2023) · mAbs · doi:10.1080/19420862.2023.2175319 · Cited by 55 · see citing works
Comparison of hydrophobicity scales for predicting biophysical properties of antibodies
Waibl, F., Fernández‐Quintero, M. L., Wedl, F. S., Kettenberger, H., Georges, G. J., & Liedl, K. R. (2022) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2022.960194 · Cited by 49 · see citing works
How B-Cell Receptor Repertoire Sequencing Can Be Enriched with Structural Antibody Data
Kovaltsuk, A., Krawczyk, K., Galson, J. D., Kelly, D. F., Deane, C. M., & Trück, J. (2017) · Frontiers in Immunology · doi:10.3389/fimmu.2017.01753 · Cited by 48 · see citing works
Abanades, B., Olsen, T. H., Raybould, M. I. J., Aguilar-Sanjuan, B., Wong, W. K., Georges, G. J., Bujotzek, A., & Deane, C. M. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad1056 · Cited by 46 · see citing works
Assessment of Therapeutic Antibody Developability by Combinations of In Vitro and In Silico Methods
Pérez, A.-M. W., Lorenzen, N., Vendruscolo, M., & Sormanni, P. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1450-1_4 · Cited by 40 · see citing works
Murin, C. D., Bruhn, J. F., Bornholdt, Z. A., Copps, J., Stanfield, R. L., & Ward, A. B. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.08.009 · Cited by 38 · see citing works
Kovaltsuk, A., Raybould, M. I. J., Wong, W. K., Marks, C., Kelm, S., Snowden, J., Trück, J., & Deane, C. M. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007636 · Cited by 39 · see citing works
Computational design of structured loops for new protein functions
Kundert, K., & Kortemme, T. (2019) · Biological Chemistry · doi:10.1515/hsz-2018-0348 · Cited by 36 · see citing works
In silico prediction of post-translational modifications in therapeutic antibodies
Vatsa, S. (2022) · mAbs · doi:10.1080/19420862.2021.2023938 · Cited by 35 · see citing works
AntiFold: improved structure-based antibody design using inverse folding
Høie, M. H., Hummer, A. M., Olsen, T. H., Aguilar-Sanjuan, B., Nielsen, M., & Deane, C. M. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbae202 · Cited by 47 · see citing works
Antibody CDR loops as ensembles in solution vs. canonical clusters from X-ray structures
Fernández‐Quintero, M. L., Heiss, M. C., Pomarici, N. D., Math, B. A., & Liedl, K. R. (2020) · mAbs · doi:10.1080/19420862.2020.1744328 · Cited by 34 · see citing works
Robustification of RosettaAntibody and Rosetta SnugDock
Jeliazkov, J. R., Frick, R., Zhou, J., & Gray, J. J. (2021) · PLoS ONE · doi:10.1371/journal.pone.0234282 · Cited by 34 · see citing works
ImmuneBuilder: Deep-Learning models for predicting the structures of immune proteinsPreprint
Abanades, B., Wong, W. K., Boyles, F., Georges, G. J., Bujotzek, A., & Deane, C. M. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.04.514231 · Cited by 32 · see citing works
Conformational Ensembles of Antibodies Determine Their Hydrophobicity
Waibl, F., Fernández‐Quintero, M. L., Kamenik, A. S., Kraml, J., Hofer, F., Kettenberger, H., Georges, G. J., & Liedl, K. R. (2020) · Biophysical Journal · doi:10.1016/j.bpj.2020.11.010 · Cited by 31 · see citing works
Yang, Y. X., Wang, P., & Zhu, B. T. (2022) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2022.108364 · Cited by 30 · see citing works
Ensembles in solution as a new paradigm for antibody structure prediction and design
Fernández‐Quintero, M. L., Georges, G. J., Varga, J. M., & Liedl, K. R. (2021) · mAbs · doi:10.1080/19420862.2021.1923122 · Cited by 30 · see citing works
Kaleli, N. E., Karadag, M., & Kalyoncu, S. (2019) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25685 · Cited by 29 · see citing works
Structural Classification of CDR-H3 in Single-Domain VHH Antibodies
Kuroda, D., & Tsumoto, K. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-2609-2_2 · Cited by 27 · see citing works
Ivanochko, D., Fabra-García, A., Teelen, K., van de Vegte‐Bolmer, M., van Gemert, G., Newton, J. C., Semesi, A., de Bruijni, M. H. C., Bolscher, J. M., Ramjith, J., et al. (2023) · Immunity · doi:10.1016/j.immuni.2023.01.013 · Cited by 28 · see citing works
Germline-Dependent Antibody Paratope States and Pairing Specific VH-VL Interface Dynamics
Fernández‐Quintero, M. L., Kroell, K. B., Bacher, L. M., Loeffler, J. R., Quoika, P. K., Georges, G. J., Bujotzek, A., Kettenberger, H., & Liedl, K. R. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.675655 · Cited by 27 · see citing works
Enhancing Antibody Language Models with Structural InformationPreprint
Barton, J., Galson, J. D., & Leem, J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.12.12.569610 · Cited by 29 · see citing works
Antibody affinity maturation by computational design
Kuroda, D., & Tsumoto, K. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-8648-4_2 · Cited by 26 · see citing works
McCoy, K. M., Ackerman, M. E., & Grigoryan, G. (2024) · Protein Science · doi:10.1002/pro.5127 · Cited by 27 · see citing works
Cherepanova, O. A., Srikakulapu, P., Greene, E. S., Chaklader, M., Haskins, R. M., McCanna, M. E., Bandyopadhyay, S., Ban, B., Leitinger, N., McNamara, C. A., et al. (2019) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.119.312771 · Cited by 25 · see citing works
Progress toward improved understanding of antibody maturation
Vajda, S., Porter, K. A., & Kozakov, D. (2021) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2020.11.008 · Cited by 25 · see citing works
SARS-CoV-2 antibodies recognize 23 distinct epitopic sites on the receptor binding domain
Jiang, J., Boughter, C. T., Ahmad, J., Natarajan, K., Boyd, L. F., Meier-Schellersheim, M., & Margulies, D. H. (2023) · Communications Biology · doi:10.1038/s42003-023-05332-w · Cited by 24 · see citing works
Jaszczyszyn, I., Bielska, W., Gawłowski, T., Dudzic, P., Satława, T., Kończak, J., Wilman, W., Janusz, B., Wróbel, S., Chomicz, D., et al. (2023) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2023.1214424 · Cited by 23 · see citing works
Quast, N. P., Abanades, B., Guloglu, B., Karuppiah, V., Harper, S., Raybould, M. I. J., & Deane, C. M. (2025) · Communications Biology · doi:10.1038/s42003-025-07708-6 · Cited by 22 · see citing works
Transitions of CDR-L3 Loop Canonical Cluster Conformations on the Micro-to-Millisecond Timescale
Fernández‐Quintero, M. L., Math, B. A., Loeffler, J. R., & Liedl, K. R. (2019) · Frontiers in Immunology · doi:10.3389/fimmu.2019.02652 · Cited by 22 · see citing works
Uversky, V. N., & Regenmortel, M. H. V. V. (2021) · Critical Reviews in Biochemistry and Molecular Biology · doi:10.1080/10409238.2020.1869683 · Cited by 22 · see citing works
Fernández‐Quintero, M. L., Heiss, M. C., & Liedl, K. R. (2019) · Protein Engineering Design and Selection · doi:10.1093/protein/gzaa004 · Cited by 19 · see citing works
Defining and Studying B Cell Receptor and TCR Interactions
Rappazzo, C. G., Fernández‐Quintero, M. L., Mayer, A., Wu, N. C., Greiff, V., & Guthmiller, J. J. (2023) · The Journal of Immunology · doi:10.4049/jimmunol.2300136 · Cited by 18 · see citing works
Guloglu, B., & Deane, C. M. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1223802 · Cited by 18 · see citing works
Structure and Dynamics Guiding Design of Antibody Therapeutics and Vaccines
Fernández‐Quintero, M. L., Pomarici, N. D., Fischer, A.-L. M., Hoerschinger, V. J., Kroell, K. B., Riccabona, J. R., Kamenik, A. S., Loeffler, J. R., Ferguson, J. A., Perrett, H. R., et al. (2023) · Antibodies · doi:10.3390/antib12040067 · Cited by 19 · see citing works
Nanobody Paratope Ensembles in Solution Characterized by MD Simulations and NMR
Fernández‐Quintero, M. L., DeRose, E. F., Gabel, S. A., Mueller, G. A., & Liedl, K. R. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23105419 · Cited by 16 · see citing works
How to drug a cloud? Targeting intrinsically disordered proteins
Uversky, V. N. (2024) · Pharmacological Reviews · doi:10.1124/pharmrev.124.001113 · Cited by 17 · see citing works
Ren, J., Nettleship, J. E., Harris, G., Mwangi, W. N., Rhaman, N., Grant, C. F. J., Kotecha, A., Fry, E. E., Charleston, B., Stuart, D. I., et al. (2019) · Molecular Immunology · doi:10.1016/j.molimm.2019.04.026 · Cited by 16 · see citing works
Bielska, W., Jaszczyszyn, I., Dudzic, P., Janusz, B., Chomicz, D., Wrobel, S., Greiff, V., Feehan, R., Adolf‐Bryfogle, J., & Krawczyk, K. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1571371 · Cited by 16 · see citing works
Protein sequence modelling with Bayesian flow networks
Atkinson, T. J., Barrett, T. D., Cameron, S., Guloglu, B., Greenig, M., Tan, C. B., Robinson, L., Graves, A., Copoiu, L., & Laterre, A. (2025) · Nature Communications · doi:10.1038/s41467-025-58250-2 · Cited by 14 · see citing works
Physicochemical determinants of antibody-protein interactions
Karadag, M., Arslan, M., Kaleli, N. E., & Kalyoncu, S. (2019) · Advances in protein chemistry and structural biology · doi:10.1016/bs.apcsb.2019.08.011 · Cited by 9 · see citing works
ANARCII: A Generalised Language Model for Antigen Receptor NumberingPreprint
Greenshields‐Watson, A., Agarwal, P., Robinson, S. A., Williams, B. H., Gordon, G. L., Capel, H., Li, Y., Spoendlin, F. C., Aguilar-Sanjuan, B., Boyles, F., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.16.648720 · Cited by 9 · see citing works
Huang, B.-C., Lu, Y., Liao, J., Liu, H.-J., Hong, S.-T., Hsieh, Y.-C., Chuang, C.-H., Chen, H.-J., Liao, T.-Y., Ho, K., et al. (2021) · Chemical Science · doi:10.1039/d1sc01748a · Cited by 8 · see citing works
Gaudreault, F., Corbeil, C. R., Purisima, E. O., & Sulea, T. I. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.884132 · Cited by 6 · see citing works
Woo, H., Kim, Y., & Seok, C. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012239 · Cited by 7 · see citing works
Greenshields-Watson, A., Abanades, B., & Deane, C. M. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1352703 · Cited by 7 · see citing works
On the irrationality of rational design of an HIV vaccine in light of protein intrinsic disorder
Uversky, V. N. (2021) · Archives of Virology · doi:10.1007/s00705-021-04984-5 · Cited by 7 · see citing works
Improved prediction of antibody and their complexes with clustered generative modelling ensembles
Xu, X., Giulini, M., & Bonvin, A. M. J. J. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbaf161 · Cited by 6 · see citing works
A CDR-based approach to generate covalent inhibitory antibody for human rhinovirus protease
Cheng, Y., Wu, J., Han, Y., Xu, J., Da, Y., Zhao, Q., Guo, G., Zhou, Y., Chen, Y., Liu, J., et al. (2021) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2021.116219 · Cited by 6 · see citing works
Saetang, J., Sangkhathat, S., Jangphattananont, N., Khopanlert, W., Julamanee, J., & Tipmanee, V. (2022) · Scientific Reports · doi:10.1038/s41598-022-05742-6 · Cited by 6 · see citing works
A universal in silico V(D)J recombination strategy for developing humanized monoclonal antibodies
Hsieh, Y.-C., Liao, J., Chuang, K., Ho, K., Hong, S.-T., Liu, H.-J., Huang, B.-C., Chen, I., Liu, Y.-L., Wang, J., et al. (2022) · Journal of Nanobiotechnology · doi:10.1186/s12951-022-01259-2 · Cited by 5 · see citing works
Kończak, J., Janusz, B., Młokosiewicz, J., Satława, T., Wróbel, S., Dudzic, P., & Krawczyk, K. (2023) · ImmunoInformatics · doi:10.1016/j.immuno.2023.100028 · Cited by 5 · see citing works
Ünsal, S., Holland, B., Sardag, I., & Timuçin, E. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.25.666870 · Cited by 5 · see citing works
Fight fire with fire: the need for a vaccine based on intrinsic disorder and structural flexibility
Uversky, V. N. (2022) · Exploration of Immunology · doi:10.37349/ei.2022.00079 · Cited by 4 · see citing works
DeepRank-Ab: a scoring function for antibody-antigen complexes based on geometric deep learning
Xu, X., Coratella, I., Reys, V., & Bonvin, A. M. J. J. (2026) · Communications Biology · doi:10.1038/s42003-026-10408-4 · Cited by 4 · see citing works
Grasso, L., Kline, J. B., & Nicolaides, N. C. (2021) · Oncology Letters · doi:10.3892/ol.2021.13120 · Cited by 4 · see citing works
Improved structural modelling of antibodies and their complexes with clustered diffusion ensemblesPreprint
Giulini, M., Xu, X., & Bonvin, A. M. J. J. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.24.639865 · Cited by 4 · see citing works
Unraveling looping efficiency of stochastic Cosserat polymers
Corazza, G., & Singh, R. (2022) · Physical Review Research · doi:10.1103/physrevresearch.4.013071 · Cited by 3 · see citing works
Alternative Methods to Animal Use for Monoclonal Antibody Generation and Production
de Moraes, J. Z., Hamaguchi, B., Braggion, C., Speciale, E. R., Cesar, F. B. V., Soares, G. D. F. D. S., Osaki, J. H., de Aguiar, R. B., & Pereira, T. M. (2021) · IntechOpen eBooks · doi:10.5772/intechopen.95485 · Cited by 4 · see citing works
AbFold -- an AlphaFold Based Transfer Learning Model for Accurate Antibody Structure PredictionPreprint
Peng, C., Wang, Z., Zhao, P., Ge, W., & Huang, C. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.04.20.537598 · Cited by 3 · see citing works
Riziotis, I. G., & Glykos, N. M. (2019) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25758 · Cited by 2 · see citing works
Computational Modeling of Antibody and T-Cell Receptor (CDR3 Loops)
Marin, F. I., & Marcatili, P. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-2609-2_3 · Cited by 2 · see citing works
Structural Characterization of Nanobodies during Germline Maturation
Seidler, C. A., Kokot, J., Fernández‐Quintero, M. L., & Liedl, K. R. (2023) · Biomolecules · doi:10.3390/biom13020380 · Cited by 2 · see citing works
Papageorgiou, L., Maroulis, D., Chrousos, G. P., Eliopoulos, E., & Vlachakis, D. (2020) · Advances in experimental medicine and biology · doi:10.1007/978-3-030-32622-7_4 · Cited by 2 · see citing works
AbBFN2: A flexible antibody foundation model based on Bayesian Flow NetworksPreprint
Guloglu, B., Bragança, M., Graves, A., Cameron, S., Atkinson, T. J., Copoiu, L., Laterre, A., & Barrett, T. D. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.29.651170 · Cited by 2 · see citing works
Improving nanobody structure prediction with self-distillationPreprint
Ali, M., Greenig, M., Jaskółowski, M., Crnogaj, M., Smorodina, E., Zhao, H., Greiff, V., & Sormanni, P. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.01.691162 · Cited by 2 · see citing works
Jann, A., Perez, M. A. S., & Zoete, V. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.19.695451 · Cited by 2 · see citing works
Waury, K., Lelieveld, S., Abeln, S., & van den Ham, H.-J. (2025) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1013057 · Cited by 1 · see citing works
H3BERTa: A CDR-H3-specific language model for antibody repertoire analysis
Rodella, C., & Lemmin, T. (2026) · Patterns · doi:10.1016/j.patter.2026.101561
Machine-learning-based Structural Analysis of Interactions between Antibodies and Antigens
Zhang, G., Kuang, X., Zhang, Y., Liu, Y., Su, Z., Zhang, T., & Wu, Y. (2023) · Biosystems · doi:10.1016/j.biosystems.2024.105264 · Cited by 1 · see citing works
Antibody Modeling, Engineering, and Design
Fischman, S., & Ofran, Y. (2018) · Springer eBooks · doi:10.1007/978-3-642-35943-9_10083-1
Optimized antibody humanization by intra and inter VH-VL binding energy sortingPreprint
Zhong, Q. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.01.06.425645
Ivanochko, D., Fabra-García, A., Teelen, K., van de Vegte‐Bolmer, M., van Gemert, G., Newton, J. C., Semesi, A., de Bruijni, M. H. C., Bolscher, J. M., Ramjith, J., et al. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4199639
Bauer, J., Kube, S., Gupta, P. K., & Kumar, S. (2024) · Springer eBooks · doi:10.1007/978-3-031-62007-2_14
Rossmueller, G., Mirkina, I., Thiele, M., Tarazona, A. A. P., Rüker, F., Kerschbaumer, R. J., & Schinagl, A. (2024) · Antibodies · doi:10.3390/antib13040104
Antibody Affinity Maturation by Computational Design
Abualrous, E. T., Miller, W., & Cannon, D. A. (2026) · Methods in molecular biology · doi:10.1007/978-1-0716-5041-7_4
Robust Conditional Diffusion with Noisy Templates for Antibody Sequence–Structure DesignPreprint
Liu, P., Yan, C., Yan, C., Liu, X., Huang, S., 吴镇, & Zhang, J. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.18.733127
Structural T‐Cell Receptor Analysis in the Age of Machine Learning
Quast, N. P., Raybould, M. I. J., & Deane, C. M. (2026) · Immunological Reviews · doi:10.1111/imr.70168
Barozet, A., Mathieu, M., Papin, D., Cameron, B., Dabdoubi, T., Sévérac, A., Ferrari, P., Siméon, T., Bianciotto, M., & Cortés, J. (2026) · Journal of Structural Biology · doi:10.1016/j.jsb.2026.108368
Heo, R., Lee, D., Jung, J., Kim, B. J., & Park, C. (2026) · mAbs · doi:10.1080/19420862.2026.2753234
- Authors
- Regep, C., Georges, G., Shi, J., Popovic, B., & Deane, C. M.
- Affiliations
- University of Oxford (GB)
- Roche Pharma AG (Germany) (DE)
- UCB Pharma (United Kingdom) (GB)
- MedImmune Ltd., Department of Antibody Discovery and Protein Engineering Cambridge CB21 6GH United Kingdom
- Corresponding author
- Deane, Charlotte M.
- Venue
- Proteins Structure Function and Bioinformatics
- Publisher
- Wiley
- Published
- 2017-04-06
- Volume
- 85
- Issue
- 7
- Pages
- 1311-1318
- ISSN
- 0887-3585 (ISSN-L)
- 1097-0134
- PMC
- PMC5535007
- OpenAlex
- W2603247627
- Cited by
- 99
- References
- 38
- Citations by year
- 2026: 8 · 2025: 9 · 2024: 11 · 2023: 14 · 2022: 16 · 2021: 13 · 2020: 7 · 2019: 13 · 2018: 7 · 2017: 1
- FWCI
- 3.11
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Proteins Structure Function and Bioinformatics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Engineering and Physical Sciences Research Council (EP/G037280/1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- 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
- Keywords
- hypervariable region, antibody structure, antibody-antigen binding
- MeSH terms
- Algorithms, Antibodies, Antigens, Binding Sites, Glycine, Humans, Immunoglobulin Heavy Chains, Protein Binding, Software, Temperature, Thermodynamics, Tyrosine, Protein Structure, Secondary, Protein Folding, Complementarity Determining Regions
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
