Prediction of VH-VL domain orientation for antibody variable domain modeling
Abstract
The antigen-binding site of antibodies forms at the interface of their two variable domains, VH and VL, making VH-VL domain orientation a factor that codetermines antibody specificity and affinity. Preserving VH-VL domain orientation in the process of antibody engineering is important in order to retain the original antibody properties, and predicting the correct VH-VL orientation has also been recognized as an important factor in antibody homology modeling. In this article, we present a fast sequence-based predictor that predicts VH-VL domain orientation with Q(2) values ranging from 0.54 to 0.73 on the evaluation set. We describe VH-VL orientation in terms of the six absolute ABangle parameters that have recently been proposed as a means to separate the different degrees of freedom of VH-VL domain orientation. In order to assess the impact of adjusting VH-VL orientation according to our predictions, we use the set of antibody structures of the recently published Antibody Modeling Assessment (AMA) II study. In comparison to the original AMAII homology models, we find an improvement in the accuracy of VH-VL orientation modeling, which also translates into an improvement in the average root-mean-square deviation with regard to the crystal structures.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
Accurate determination of the relative orientation of the VH and VL domains is crucial to determine the correct shape of the paratope.
“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 [107]
Cites
Cd-hit: Accelerated for clustering the next-generation sequencing data
Fu, L., Niu, B., Zhu, Z., Wu, S., & Li, W. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts565 · Cited by 11,175 · 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
Sequences of proteins of immunological interest
Kabat, B. E. A. (1984) · Analytical Biochemistry · doi:10.1016/0003-2697(84)90805-4 · Cited by 3,387 · 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
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
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
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
Prediction of protein conformational freedom from distance constraints
de Groot, B. L., van Aalten, D. M. F., Scheek, R. M., Amadei, A., Vriend, G., & Berendsen, H. J. C. (1997) · Proteins Structure Function and Bioinformatics · doi:10.1002/(sici)1097-0134(199710)29:2 · Cited by 269 · see citing works
Honegger, A., & Plückthun, A. (2001) · Journal of Molecular Biology · doi:10.1006/jmbi.2001.4662 · Cited by 249 · 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
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
Zhu, K., Day, T., Warshaviak, D., Murrett, C. S., Friesner, R., & Pearlman, D. (2014) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.24551 · Cited by 206 · see citing works
Structural determinants in the sequences of immunoglobulin variable domain 1 1Edited by A. R. Fersht
Chothia, C., Gelfand, I. M., & Kister, A. E. (1998) · Journal of Molecular Biology · doi:10.1006/jmbi.1998.1653 · Cited by 176 · see citing works
Almagro, J. C., Beavers, M. P., Hernandez‐Guzman, F., Maier, J., Shaulsky, J., Butenhof, K., Labute, P., Thorsteinson, N., Kelly, K., Teplyakov, A., et al. (2011) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.23130 · Cited by 131 · see citing works
ABangle: characterising the VH-VL orientation in antibodies
Dunbar, J., Fuchs, A., Shi, J., & Deane, C. M. (2013) · Protein Engineering Design and Selection · doi:10.1093/protein/gzt020 · Cited by 125 · see citing works
Chailyan, A., Marcatili, P., & Tramontano, A. L. (2011) · FEBS Journal · doi:10.1111/j.1742-4658.2011.08207.x · Cited by 106 · see citing works
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 97 · see citing works
Li, Y., Li, H., Smith‐Gill, S. J., & Mariuzza, R. A. (2000) · Biochemistry · doi:10.1021/bi000054l · Cited by 92 · see citing works
Maier, J. K. X., & Labute, P. (2014) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24576 · Cited by 91 · see citing works
Analysis and prediction of VH/VL packing in antibodies
Abhinandan, K. R., & Martin, A. C. R. (2010) · Protein Engineering Design and Selection · doi:10.1093/protein/gzq043 · Cited by 88 · see citing works
IG, TR and IgSF, MHC and MhcSF: what do we learn from the IMGT Colliers de Perles?
Kaas, Q., Ehrenmann, F., & Lefranc, M. (2008) · Briefings in Functional Genomics and Proteomics · doi:10.1093/bfgp/elm032 · Cited by 81 · see citing works
Antibody modeling assessment II. Structures and models
Teplyakov, A., Luo, J., Obmolova, G., Malia, T. J., Sweet, R., Stanfield, R. L., Kodangattil, S., Almagro, J. C., & Gilliland, G. L. (2014) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.24554 · Cited by 72 · see citing works
Narayanan, A., Sellers, B. D., & Jacobson, M. P. (2009) · Journal of Molecular Biology · doi:10.1016/j.jmb.2009.03.043 · Cited by 41 · see citing works
Pan, R., Sampson, J. M., Chen, Y., Vaine, M., Wang, S., Lu, S., & Kong, X. (2013) · Journal of Virology · doi:10.1128/jvi.00843-13 · Cited by 39 · see citing works
Automated antibody structure prediction using Accelrys tools: Results and best practices
Fasnacht, M., Butenhof, K. J., Lamy, A., Hernandez-Guzman, F. G., Huang, H., & Yan, L. (2014) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24604 · Cited by 35 · see citing works
Automated Aufbau of antibody structures from given sequences using Macromoltek's SmrtMolAntibody
Berrondo, M., Kaufmann, S., & Berrondo, M. (2014) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24595 · Cited by 19 · see citing works
Antigen recognition by antibody C836 through adjustment of VL/VHpacking
Teplyakov, A., Obmolova, G., Malia, T., & Gilliland, G. L. (2011) · Acta Crystallographica Section F Structural Biology and Crystallization Communications · doi:10.1107/s1744309111027746 · Cited by 16 · see citing works
Crystal structure determination of anti‐DNA Fab A52
Stanfield, R. L., & Eilat, D. (2014) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24514 · Cited by 8 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Modeling and docking of antibody structures with Rosetta
Weitzner, B. D., Jeliazkov, J. R., Lyskov, S., Marze, N. A., Kuroda, D., Frick, R., Adolf‐Bryfogle, J., Biswas, N., Dunbrack, R. L., & Gray, J. J. (2017) · Nature Protocols · doi:10.1038/nprot.2016.180 · Cited by 266 · see citing works
ABodyBuilder: automated antibody structure prediction with data-driven accuracy estimation
Leem, J., Dunbar, J., Georges, G., Shi, J., & Deane, C. M. (2016) · mAbs · doi:10.1080/19420862.2016.1205773 · Cited by 238 · 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
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 138 · see citing works
DLAB-deep learning methods for structure-based virtual screening of antibodies
Schneider, C., Buchanan, A., Taddese, B., & Deane, C. M. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btab660 · Cited by 102 · see citing works
MIB–MIP is a mycoplasma system that captures and cleaves immunoglobulin G
Arfi, Y., Minder, L., Primo, C. D., Roy, A. L., Ebel, C., Coquet, L., Claverol, S., Vashee, S., Jores, J., Blanchard, A., et al. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1600546113 · Cited by 99 · 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
In silico methods for design of biological therapeutics
Roy, A., Nair, S. A., Sen, N., Soni, N., & Madhusudhan, M. S. (2017) · Methods · doi:10.1016/j.ymeth.2017.09.008 · Cited by 62 · 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
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
Improved prediction of antibody VL–VHorientation
Marze, N. A., Lyskov, S., & Gray, J. J. (2016) · Protein Engineering Design and Selection · doi:10.1093/protein/gzw013 · Cited by 55 · see citing works
Benschop, R. J., Chow, C.-K., Tian, Y., Nelson, J., Barmettler, B., Atwell, S., Clawson, D. K., Chai, Q., Jones, B. E., Fitchett, J. R., et al. (2019) · mAbs · doi:10.1080/19420862.2019.1624463 · Cited by 52 · 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
Predicting antibody binders and generating synthetic antibodies using deep learning
Lim, Y. W., Adler, A. S., & Johnson, D. S. (2022) · mAbs · doi:10.1080/19420862.2022.2069075 · Cited by 49 · see citing works
Weitzner, B. D., & Gray, J. J. (2016) · The Journal of Immunology · doi:10.4049/jimmunol.1601137 · Cited by 45 · see citing works
Madsen, A. V., Mejias-Gomez, O., Pedersen, L. E., Morth, J. P., Kristensen, P. K., Jenkins, T. P., & Goletz, S. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.11.056 · Cited by 45 · see citing works
Differential performance of RoseTTAFold in antibody modeling
Liang, T., Jiang, C., Yuan, J., Othman, Y., Xie, X., & Feng, Z. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac152 · Cited by 41 · see citing works
VH-VL orientation prediction for antibody humanization candidate selection: A case study
Bujotzek, A., Lipsmeier, F., Harris, S. F., Benz, J., Kuglstatter, A., & Georges, G. J. (2015) · mAbs · doi:10.1080/19420862.2015.1117720 · Cited by 40 · see citing works
VH‐VL interdomain dynamics observed by computer simulations and NMR
Fernández‐Quintero, M. L., Hoerschinger, V. J., Lamp, L. M., Bujotzek, A., Georges, G. J., & Liedl, K. R. (2020) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25872 · Cited by 32 · see citing works
Immune repertoire mining for rapid affinity optimization of mouse monoclonal antibodies
Hsiao, Y.-C., Shang, Y., DiCara, D. M., Yee, A., Lai, J. J., Kim, S. H., Ellerman, D. A., Corpuz, R. T., Chen, Y., Rajan, S., et al. (2019) · mAbs · doi:10.1080/19420862.2019.1584517 · 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
Nottelet, P., Bataille, L., Gourgues, G., Anger, R., Lartigue, C., Sirand‐Pugnet, P., Marza, E., Fronzes, R., & Arfi, Y. (2021) · Science Advances · doi:10.1126/sciadv.abf2403 · Cited by 29 · 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
Mutation of Framework Residue H71 Results in Different Antibody Paratope States in Solution
Fernández‐Quintero, M. L., Kroell, K. B., Hofer, F., Riccabona, J. R., & Liedl, K. R. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.630034 · Cited by 26 · see citing works
Computational tools for aiding rational antibody design
Krawczyk, K., Dunbar, J., & Deane, C. M. (2017) · Methods in molecular biology · doi:10.1007/978-1-4939-6637-0_21 · Cited by 26 · see citing works
Surprisingly Fast Interface and Elbow Angle Dynamics of Antigen-Binding Fragments
Fernández‐Quintero, M. L., Kroell, K. B., Heiss, M. C., Loeffler, J. R., Quoika, P. K., Waibl, F., Bujotzek, A., Moessner, E., Georges, G. J., & Liedl, K. R. (2020) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2020.609088 · Cited by 23 · 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
MoFvAb: Modeling the Fv region of antibodies
Bujotzek, A., Fuchs, A., Qu, C., Benz, J., Klostermann, S., Antes, I., & Georges, G. J. (2015) · mAbs · doi:10.1080/19420862.2015.1068492 · Cited by 22 · see citing works
VHH Structural Modelling Approaches: A Critical Review
Vishwakarma, P., Vattekatte, A. M., Shinada, N. K., Diharce, J., Martins, C., Cadet, F., Gardebien, F., Etchebest, C., Nadaradjane, A. A., & de Brevern, A. G. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23073721 · Cited by 19 · 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
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
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 · see citing works
Dudzic, P., Chomicz, D., Bielska, W., Jaszczyszyn, I., Zieliński, M., Janusz, B., Wróbel, S., Pannérer, M.-M. L., Phillips, A., Prabakaran, P., et al. (2025) · Communications Biology · doi:10.1038/s42003-025-08388-y · Cited by 15 · see citing works
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 · see citing works
Fernández‐Quintero, M. L., Kroell, K. B., Grunewald, L., Fischer, A.-L. M., Riccabona, J. R., & Liedl, K. R. (2022) · mAbs · doi:10.1080/19420862.2021.2024118 · Cited by 13 · see citing works
The Contorsbody, an antibody format for agonism: Design, structure, and function
Georges, G., Dengl, S., Bujotzek, A., Hesse, F., Fischer, J., Gärtner, A., Benz, J., Lauer, M. E., Ringler, P., Stahlberg, H., et al. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.05.007 · Cited by 14 · see citing works
Prediction of polyspecificity from antibody sequence data by machine learning
Éliás, S., Wrzodek, C., Deane, C. M., Tissot, A. C., Klostermann, S., & Ros, F. (2024) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2023.1286883 · Cited by 11 · see citing works
Hoffmann, T., Marion, A., & Antes, I. (2017) · BMC Structural Biology · doi:10.1186/s12900-016-0071-7 · Cited by 11 · see citing works
Antibody numbering schemes: advances, comparisons and tools for antibody engineering
Zhu, Z., Ashrafian, H., Mohammadian-Tabrizi, N., Matas, E. R., Girard, L., Ma, H., & Nice, E. C. (2025) · Protein Engineering Design and Selection · doi:10.1093/protein/gzaf005 · Cited by 10 · 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
Back-to-Germline (B2G) Procedure for Antibody Devolution
Schrade, A., Bujotzek, A., Spick, C., Wagner, M., Goerl, J., Wezler, X., Georges, G. J., Kontermann, R. E., & Brinkmann, U. (2019) · Antibodies · doi:10.3390/antib8030045 · Cited by 7 · see citing works
Comparing Antibody Interfaces to Inform Rational Design of New Antibody Formats
Fernández‐Quintero, M. L., Quoika, P. K., Wedl, F. S., Seidler, C. A., Kroell, K. B., Loeffler, J. R., Pomarici, N. D., Hoerschinger, V. J., Bujotzek, A., Georges, G. J., et al. (2022) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2022.812750 · Cited by 8 · see citing works
Homology Modeling of Antibody Variable Regions: Methods and Applications
Bansia, H., & Ramakumar, S. (2023) · Methods in molecular biology · doi:10.1007/978-1-0716-2974-1_16 · Cited by 5 · see citing works
OCD.py - Characterizing immunoglobulin inter-domain orientationsPreprint
Hoerschinger, V. J., Fernández‐Quintero, M. L., Waibl, F., Kraml, J., Bujotzek, A., Georges, G. J., & Liedl, K. R. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.03.15.435379 · Cited by 5 · see citing works
Wang, N., Xiao, Y., Liu, X., Li, Y., Yu, D. D., Guo, J., Lü, P., & Zhang, X. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26104922 · Cited by 4 · see citing works
Cross‐linking disulfide bonds govern solution structures of diabodies
Math, B. A., Waibl, F., Lamp, L. M., Fernández‐Quintero, M. L., & Liedl, K. R. (2023) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26509 · Cited by 4 · see citing works
Protein Structure Prediction and Homology Modeling
Leach, A. R., & Thomas, P. J. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-409547-2.12350-9 · Cited by 3 · 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
abYpap: improvements to the prediction of antibody VH/VL packing using gradient boosted regression
Boron, V. A., & Martin, A. C. R. (2023) · Protein Engineering Design and Selection · doi:10.1093/protein/gzad021 · Cited by 2 · see citing works
Spanke, V. A., Hoerschinger, V. J., Seidler, C. A., Kroell, K. B., Wieser, V., Imhof-Jung, S., Weiche, B., Bujotzek, A., Georges, G. J., & Liedl, K. R. (2026) · mAbs · doi:10.1080/19420862.2026.2627669 · Cited by 1 · see citing works
Engineering of acidic pH-responsive anti-CD3 binding antibodies
Sala, G. L., Kroell, K. B., Pincha, M., Gassner, C., Dehò, L., Moessner, E., Guéripel, X., Borin, N., Classen, M., Benz, J., et al. (2026) · mAbs · doi:10.1080/19420862.2026.2658902 · Cited by 1 · see citing works
Data-optimal scaling of paired antibody language modelsPreprint
Neyestanak, M. S., Burbach, S. M., Ng, K., Gangavarapu, P., Hurtado, J., Magura, J., Ismail, N., Muema, D., Ndung'u, T., Ward, A., et al. (2025) · bioRxiv · doi:10.1101/2025.09.02.673765 · 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
Leach, A. R., & Mobarec, J. C. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-29808-0.00086-8
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
Structure-based Antibody RenumberingPreprint
del Alamo, D. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.09.16.751788
- Authors
- Bujotzek, A., Dunbar, J., Lipsmeier, F., Schäfer, W., Antes, I., Deane, C. M., & Georges, G.
- Affiliations
- Roche Pharma AG (Germany) (DE)
- University of Oxford (GB)
- Technical University of Munich (DE)
- Corresponding author
- Bujotzek, Alexander
- Venue
- Proteins Structure Function and Bioinformatics
- Publisher
- Wiley
- Published
- 2015-02-05
- Volume
- 83
- Issue
- 4
- Pages
- 681-695
- ISSN
- 0887-3585 (ISSN-L)
- 1097-0134
- OpenAlex
- W2167980959
- Cited by
- 56
- References
- 28
- Citations by year
- 2026: 5 · 2025: 5 · 2024: 1 · 2023: 7 · 2022: 5 · 2021: 6 · 2020: 5 · 2019: 6 · 2018: 3 · 2017: 6 · 2016: 5 · 2015: 2
- FWCI
- 2.31
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Proteins Structure Function and Bioinformatics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Roche (RPF-ID 260)
- 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
- antibody variable domains, sequence-based prediction, antibody engineering, antibody binding sites
- MeSH terms
- Immunoglobulin Variable Region, Immunoglobulin Heavy Chains, Models, Molecular, Protein Engineering, Sequence Analysis, Protein, Databases, Protein, Structural Homology, Protein
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
