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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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Gapped BLAST and PSI-BLAST: a new generation of protein database search programs
Altschul, S. (1997) · Nucleic Acids Research · doi:10.1093/nar/25.17.3389 · Cited by 73,302
Thompson, J. D., Higgins, D. G., & Gibson, T. J. (1994) · Nucleic Acids Research · doi:10.1093/nar/22.22.4673 · Cited by 64,617
WebLogo: A Sequence Logo Generator: Figure 1Preprint
Crooks, G. E., Hon, G. C., Chandonia, J., & Brenner, S. E. (2004) · Genome Research · doi:10.1101/gr.849004 · Cited by 12,418
Clustering by Passing Messages Between Data Points
Frey, B. J., & Dueck, D. (2007) · Science · doi:10.1126/science.1136800 · Cited by 6,855
Eddy, S. R. (1998) · Bioinformatics · doi:10.1093/bioinformatics/14.9.755 · Cited by 5,515
Biological sequence analysis probabilistic models of proteins and nucleic acids
Durbin, R. M., Eddy, S. R., Krogh, A., & Mitchison, G. (1998) · Cited by 3,565
Sequences of proteins of immunological interest
Kabat, B. E. A. (1984) · Analytical Biochemistry · doi:10.1016/0003-2697(84)90805-4 · Cited by 3,363
PISCES: a protein sequence culling server
Wang, G., & Dunbrack, R. L. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg224 · Cited by 1,664
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
A NEW GENERATION OF HOMOLOGY SEARCH TOOLS BASED ON PROBABILISTIC INFERENCE
Eddy, S. R. (2009) · International Conference on Genome Informatics (GIW) · doi:10.1142/9781848165632_0019 · Cited by 1,275
Flagged first, then by citations
Research works printed but not linked: 1
- Martin, 1996, Structural families in loops of homologous proteins: automatic classification, modelling and application to antibodies, J. Mol. Biol., 263, 800, 10.1006/jmbi.1996.0617 · DOI
Cited by
Sorted by citations
Items 201–300 of 382
Tsuchiya, Y., Namiuchi, Y., Wako, H., & Tsurui, H. (2017) · Immunology · doi:10.1111/imm.12849 · Cited by 17
Dynamic allostery in the peptide/MHC complex enables TCR neoantigen selectivity
Ma, J., Ayres, C. M., Brambley, C. A., Chandran, S., Rosales, T. J., Perera, W. W. J. G., Eldaly, B., Murray, W. T., Corcelli, S. A., Kovrigin, E. L., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-56004-8 · Cited by 10
Lu, Z., Rynkiewicz, M. J., Yang, C., Madico, G. E., Perkins, H. M., Wang, Q., Costello, C. E., Zaia, J., Seaton, B. A., & Sharon, J. (2013) · Immunology · doi:10.1111/imm.12150 · Cited by 16
Nicely, N. I., Wiehe, K. J., Kepler, T. B., Jaeger, F. H., Dennison, S. M., Rerks‐Ngarm, S., Nitayaphan, S., Pitisuttithum, P., Kaewkungwal, J., Robb, M. L., et al. (2015) · EBioMedicine · doi:10.1016/j.ebiom.2015.06.016 · Cited by 17
Antibody–antigen complex modelling in the era of immunoglobulin repertoire sequencing
Raybould, M. I. J., Wong, W. K., & Deane, C. M. (2019) · Molecular Systems Design & Engineering · doi:10.1039/c9me00034h · Cited by 14
Maruthachalam, B. V., El‐Sayed, A., Liu, J., Sutherland, A. R., Hill, W., Alam, M. K., Pastushok, L., Fonge, H., Barreto, K., & Geyer, C. R. (2017) · ChemBioChem · doi:10.1002/cbic.201700279 · Cited by 16
Anti-phosphotyrosine antibody PY20-4D5 Fab complexed with sulfate
Mou, K. Y., Leung, K., & Wells, J. A. (2018) · Worldwide Protein Data Bank · doi:10.2210/pdb6dez/pdb · Cited by 16
A penultimate classification of canonical antibody CDR conformationsPreprint
Kelow, S. P., Faezov, B., Xu, Q., Parker, M. I., Adolf‐Bryfogle, J., & Dunbrack, R. L. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.12.511988 · Cited by 17
Quantifying conformational changes in the TCR:pMHC-I binding interface
McMaster, B., Thorpe, C. J., Rossjohn, J., Deane, C. M., & Koohy, H. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1491656 · Cited by 17
Barozet, A., Bianciotto, M., Siméon, T., Minoux, H., & Cortés, J. (2018) · Immunology Letters · doi:10.1016/j.imlet.2018.06.002 · Cited by 15
High-Throughput Antibody Structure Modeling and Design Using ABodyBuilder
Leem, J., & Deane, C. M. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-8736-8_21 · Cited by 15
Lee, H. E., Cho, A. H., Hwang, J., Kim, J. W., Yang, H. R., Ryu, T., Jung, Y., & Lee, S. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25094791 · Cited by 13
Putelli, A., Kiefer, J. D., Zadory, M., Matasci, M., & Neri, D. (2014) · Journal of Molecular Biology · doi:10.1016/j.jmb.2014.07.023 · Cited by 15
Boschert, V., Frisch, C. M., Back, J. W., van Pee, K., Weidauer, S. E., Muth, E.-M., Schmieder, P., Beerbaum, M., Knappik, A., Timmerman, P., et al. (2016) · Open Biology · doi:10.1098/rsob.160120 · Cited by 17
Jing, X., Yuan, Y., Wu, Y., Wu, D., Gong, P., & Gao, M. (2018) · Protein Science · doi:10.1002/pro.3561 · Cited by 15
Lauro, A. D., Rienzo, L. D., Miotto, M., Olimpieri, P. P., Milanetti, E., & Ruocco, G. (2022) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2022.874296 · Cited by 15
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Blackler, R. J., Müller‐Loennies, S., Pokorny-Lehrer, B., Legg, M. S. G., Brade, L., Brade, H., Kosma, P., & Evans, S. V. (2022) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2022.101901 · Cited by 14
Olesen, H. G., Michailidou, I., Zelek, W. M., Vreijling, J. P., Ruizendaal, P., de Klein, F., Marquart, J. A., Kuipers, T. B., Mei, H., Zhang, Y., et al. (2022) · Journal of Innate Immunity · doi:10.1159/000524587 · Cited by 13
Biophysical Highlights from 54 Years of Macromolecular Crystallography
Richardson, J. S., & Richardson, D. C. (2014) · Biophysical Journal · doi:10.1016/j.bpj.2014.01.001 · Cited by 13
Polyphony: superposition independent methods for ensemble-based drug discovery
Pitt, W. R., Montalvão, R. W., & Blundell, T. L. (2014) · BMC Bioinformatics · doi:10.1186/1471-2105-15-324 · Cited by 14
Non-H3 CDR template selection in antibody modeling through machine learning
Long, X., Jeliazkov, J. R., & Gray, J. J. (2019) · PeerJ · doi:10.7717/peerj.6179 · Cited by 14
Hsu, H.-J., Tung, C.-P., Yu, C.-M., Chen, C.-Y., Chen, H.-S., Huang, Y.-C., Tsai, P., Lin, S.-I., Peng, H.-P., Chiu, Y.-K., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-94902-1 · Cited by 11
Krishnarjuna, B., Sugiki, T., Morales, R. A. V., Seow, J., Fujiwara, T., Wilde, K. L., Norton, R. S., & MacRaild, C. A. (2018) · Communications Biology · doi:10.1038/s42003-018-0063-1 · Cited by 14
Structural Analysis of a Protective Epitope of the Francisella tularensis O-Polysaccharide
Rynkiewicz, M. J., Lu, Z., Hui, J. H., Sharon, J., & Seaton, B. A. (2012) · Biochemistry · doi:10.1021/bi201711m · Cited by 13
Crystal Structure of Two Anti-Porphyrin Antibodies with Peroxidase Activity
Robles, V. M., Maréchal, J., Bahloul, A., Sari, M., Mahy, J., & Golinelli‐Pimpaneau, B. (2012) · PLoS ONE · doi:10.1371/journal.pone.0051128 · Cited by 13
Antibodies as stratagems against cancer
Papageorgiou, L., Cuong, N. T., & Vlachakis, D. (2015) · Molecular BioSystems · doi:10.1039/c5mb00699f · Cited by 13
Li, H., Zeitelhofer, M., Nilsson, I., Liu, X., Allan, L. C., Gloria, B., Perani, A., Murone, C., Catimel, B., Neville, A. M., et al. (2018) · PLoS ONE · doi:10.1371/journal.pone.0201089 · Cited by 16
Bonadio, A., & Shifman, J. M. (2021) · Protein Engineering Design and Selection · doi:10.1093/protein/gzab020 · Cited by 12
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Teplyakov, A., Obmolova, G., Malia, T., Raghunathan, G., Martinez, C., Fransson, J., Edwards, W., Connor, J., Husovsky, M., Beck, H., et al. (2017) · mAbs · doi:10.1080/19420862.2017.1412026 · Cited by 13
AbFlex: designing antibody complementarity determining regions with flexible CDR definition
Jeon, W., & Kim, D. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae122 · Cited by 14
D’Souza, J. W., Reddy, S., Goldsmith, L. E., Shchaveleva, I., Marks, J. D., Litwin, S., & Robinson, M. K. (2014) · PLoS ONE · doi:10.1371/journal.pone.0112376 · Cited by 12
Crystal structure of tissue factor in complex with antibody 10H10 reveals the signaling epitope
Teplyakov, A., Obmolova, G., Malia, T., Wu, B., Zhao, Y., Taudte, S., Anderson, G. M., & Gilliland, G. L. (2017) · Cellular Signalling · doi:10.1016/j.cellsig.2017.05.004 · Cited by 12
Specificity, Polyspecificity and Heterospecificity of Antibody-Antigen Recognition
Regenmortel, M. H. V. V. (2019) · Springer eBooks · doi:10.1007/978-3-030-32459-9_4 · Cited by 11
Evaluating the Conformations and Dynamics of Peptoid Macrocycles
Eastwood, J. R. B., Jiang, L., Bonneau, R., Kirshenbaum, K., & Renfrew, P. D. (2022) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.2c01669 · Cited by 13
Antibody Recognition of Chlamydia LPS: Structural Insights of Inherited Immune Responses
Blackler, R. J., Müller‐Loennies, S., Brade, L., Kosma, P., Brade, H., & Evans, S. V. (2011) · Springer eBooks · doi:10.1007/978-3-7091-0870-3_4 · Cited by 12
AbBERT: Learning Antibody Humanness via Masked Language ModelingPreprint
Vashchenko, D., Nguyen, S., Gonçalves, A. R., da Silva, F. L., Petersen, B. K., Desautels, T., & Faissol, D. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.08.02.502236 · Cited by 13
Repertoire-based diagnostics using statistical biophysics
Arora, R., Kaplinsky, J., Li, A., & Arnaout, R. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/519108 · Cited by 10
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Cutting, D., Dreyer, F. A., Errington, D., Schneider, C., & Deane, C. M. (2024) · Journal of Computational Biology · doi:10.1089/cmb.2024.0768 · Cited by 22
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Marinova, V., Dodd, L., Lee, S.-J., Wood, G. P. F., Marziano, I., & Salvalaglio, M. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01387 · Cited by 11
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Mille, L. S., Driscoll, C. L., Wang, J. N., Dai, H., Widatalla, T., Zhang, J. L., Zhang, X., Rao, B., Feng, L., Hie, B., et al. (2026) · Nature Biotechnology · doi:10.1038/s41587-026-03187-0 · Cited by 40
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Siegert, T. R., Bird, M. J., & Kritzer, J. A. (2017) · Methods in molecular biology · doi:10.1007/978-1-4939-6798-8_15 · Cited by 10
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Choong, Y. S., Lee, Y. V., Soong, J. X., Law, C. T., & Lim, Y. Y. (2017) · Advances in experimental medicine and biology · doi:10.1007/978-3-319-72077-7_11 · Cited by 10
Shine, P., Shankar, K. M., Abhiman., B. F. S., Sudheer, N. S., & Patil, R. (2020) · Molecular and Cellular Probes · doi:10.1016/j.mcp.2020.101508 · Cited by 9
Wang, B., Swaminathan, S., & Bhattacharyya, M. K. (2015) · PLoS ONE · doi:10.1371/journal.pone.0145156 · Cited by 10
Accurate positioning of functional residues with robotics-inspired computational protein design
Krivacic, C. T., Kundert, K., Pan, X., Pache, R. A., Liu, L., Conchúir, S. Ó., Jeliazkov, J. R., Gray, J. J., Thompson, M. C., Fraser, J. S., et al. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2115480119 · Cited by 8
Eronen, V., Tullila, A., Iljin, K., Rouvinen, J., Nevanen, T. K., & Hakulinen, N. (2023) · Journal of Structural Biology · doi:10.1016/j.jsb.2023.107966 · Cited by 11
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Nikoloudis, D., Pitts, J. E., & Saldanha, J. W. (2014) · PeerJ · doi:10.7717/peerj.456 · Cited by 9
Ultsch, M. H., Li, W., Eigenbrot, C. W., Lello, P. D., Lipari, M. T., Gerhardy, S., AhYoung, A. P., Quinn, J. G., Franke, Y., Chen, Y., et al. (2019) · Journal of Molecular Biology · doi:10.1016/j.jmb.2018.11.025 · Cited by 10
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White, B., Huh, I., & Brooks, C. L. (2019) · BMC Research Notes · doi:10.1186/s13104-019-4197-0 · Cited by 9
Rudolph, M. J., Vance, D. J., Kelow, S. P., Angalakurthi, S. K., Nguyen, S., Davis, S. A., Rong, Y., Middaugh, C. R., Weis, D. D., Dunbrack, R. L., et al. (2018) · Protein Engineering Design and Selection · doi:10.1093/protein/gzy022 · Cited by 9
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Tan, P., Li, S., Huang, J., Zhou, Z., & Hong, L. (2025) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2025.12.017 · Cited by 10
KONDO, H. X., Kiribayashi, R., Kuroda, D., Kohda, J., Kugimiya, A., Nakano, Y., Tsumoto, K., & Takano, Y. (2019) · Scientific Reports · doi:10.1038/s41598-019-56154-y · Cited by 8
Mitropoulou, A. N., Ceska, T., Heads, J. T., Beavil, A. J., Henry, A. J., McDonnell, J. M., Sutton, B. J., & Davies, A. M. (2020) · Acta Crystallographica Section F Structural Biology Communications · doi:10.1107/s2053230x20001466 · Cited by 8
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Rienzo, L. D., Milanetti, E., Ruocco, G., & Lepore, R. (2021) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2021.749784 · Cited by 8
Kostareva, O. S., Kolyadenko, I., Ulitin, A. B., Ekimova, V., Evdokimov, S., Garber, M., Tishchenko, S., & Gabdulkhakov, A. G. (2019) · Crystals · doi:10.3390/cryst9030177 · Cited by 8
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Stanfield, R. L., & Eilat, D. (2014) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24514 · Cited by 8
Zhu, Z., Olson, K. S., & Magliery, T. J. (2024) · Antibodies · doi:10.3390/antib13040099 · Cited by 7
Gaudreault, F., Corbeil, C. R., Purisima, E. O., & Sulea, T. I. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.884132 · Cited by 6
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Vattekatte, A. M., Diharce, J., Rebehmed, J., Cadet, F., Gardebien, F., Etchebest, C., & de Brevern, A. G. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24054511 · Cited by 7
Greenshields-Watson, A., Abanades, B., & Deane, C. M. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1352703 · Cited by 7
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Teplyakov, A., Obmolova, G., Malia, T., Luo, J., & Gilliland, G. L. (2014) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24522 · Cited by 7
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Manieri, T. M., Magalhães, C. G., Takata, D. Y., Batalha-Carvalho, J. V., & Moro, A. M. (2020) · IntechOpen eBooks · doi:10.5772/intechopen.94366 · Cited by 8
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del Alamo, D., Frick, R., Truan, D., & Karpiak, J. D. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.09.653228 · Cited by 7
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Peter, A. S., Hoffmann, D. S., Klier, J., Lange, C. M., Moeller, J., Most, V., Wüst, C. K., Beining, M., Güleşen, S., Junker, H., et al. (2024) · Infection Genetics and Evolution · doi:10.1016/j.meegid.2024.105626 · Cited by 6
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Xie, X., Valiente, P. A., Lee, J. S., Kim, J., & Kim, P. M. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c00711 · Cited by 7
Elli, S., Alekseeva, A., Ramakrishnan, B., Koch, T., Wollacott, A. M., Viswanathan, K., Li, K., Delaney, J. C., Shriver, Z. H., Plante, O. J., et al. (2020) · Biochemistry · doi:10.1021/acs.biochem.0c00642 · Cited by 6
Corbeil, C. R., Manenda, M. S., Sulea, T. I., Baardsnes, J., Picard, M.-È., Hogues, H., Gaudreault, F., Deprez, C., Shi, R., & Purisima, E. O. (2021) · Scientific Reports · doi:10.1038/s41598-021-00669-w · Cited by 7
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Son, Y.-H., Shin, D.-H., Han, J.-W., Won, S.-H., & Kam, T. (2022) · IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia) · doi:10.1109/icce-asia57006.2022.9954877 · Cited by 6
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Maritan, M., Cozzi, R., Surdo, P. L., Veggi, D., Bottomley, M. J., & Malito, E. (2017) · Acta Crystallographica Section F Structural Biology Communications · doi:10.1107/s2053230x17006021 · Cited by 6
Antibody Fragments Humanization: Beginning with the End in Mind
Aubrey, N., & Billiald, P. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-8958-4_10 · Cited by 6
Seesuay, W., Phanthong, S., Densumite, J., Mahasongkram, K., Sookrung, N., & Chaicumpa, W. (2021) · Vaccines · doi:10.3390/vaccines9080893 · Cited by 6
Antibody Engineering and Therapeutics
Almagro, J. C., Gilliland, G. L., Breden, F. J., Scott, J. K., Sok, D., Pauthner, M. G., Reichert, J. M., Helguera, G. F., Andrabi, R., Mabry, R., et al. (2014) · mAbs · doi:10.4161/mabs.28421 · Cited by 6
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
Clark, T., Subramanian, V., Jayaraman, A., Fitzpatrick, E. G., Gopal, R., Pentakota, N., Rurak, T. T., Anand, S., Viglione, A., Raman, R., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.06.02.543458 · Cited by 6
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Xu, X., Giulini, M., & Bonvin, A. M. J. J. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbaf161 · Cited by 9
Aguilar, E. N., Sagar, S., Murray, B. R., Rajesh, C., Lei, E. K., Michaud, S. A., Goodlett, D. R., Caffrey, T. C., Grandgenett, P. M., Swanson, B., et al. (2024) · Molecular Cancer Therapeutics · doi:10.1158/1535-7163.mct-23-0868 · Cited by 5
Yi, J., Ryu, J., Jeong, Y., Cho, Y., Kim, M. Y., Jeon, M., Park, H. H., Hwang, N. S.-Y., Jeong, H., & Sung, C. (2024) · Analytica Chimica Acta · doi:10.1016/j.aca.2024.342887 · Cited by 5
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Zhao, G., Richaud, A. D., Williamson, R. T., Feig, M., & Roche, S. P. (2024) · ACS Chemical Biology · doi:10.1021/acschembio.4c00236 · Cited by 5
Koivuniemi, A., Takkinen, K., & Nevanen, T. K. (2016) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25226 · Cited by 5
Nguyen, N. M., Nakao, K., Kobayashi, R., Taniguchi, H., Yokoyama, F., Horiuchi, J., & Kumada, Y. (2024) · Journal of Bioscience and Bioengineering · doi:10.1016/j.jbiosc.2024.07.009 · Cited by 4
Engineering Antibodies for Cancer Therapy
Goldsmith, L. E., & Robinson, M. K. (2011) · Cell engineering · doi:10.1007/978-94-007-1257-7_10 · Cited by 5
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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
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.
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