Antibody side chain conformations are position-dependent
Abstract
Abstract Side chain prediction is an integral component of computational antibody design and structure prediction. Current antibody modelling tools use backbone‐dependent rotamer libraries with conformations taken from general proteins. Here we present our antibody‐specific rotamer library, where rotamers are binned according to their immunogenetics (IMGT) position, rather than their local backbone geometry. We find that for some amino acid types at certain positions, only a restricted number of side chain conformations are ever observed. Using this information, we are able to reduce the breadth of the rotamer sampling space. Based on our rotamer library, we built a side chain predictor, position‐dependent antibody rotamer swapper (PEARS). On a blind test set of 95 antibody model structures, PEARS had the highest average χ1 and accuracy (78.7% and 64.8%) compared to three leading backbone‐dependent side chain predictors. Our use of IMGT position, rather than backbone ϕ/ψ, meant that PEARS was more robust to errors in the backbone of the model structure. PEARS also achieved the lowest number of side chain–side chain clashes. PEARS is freely available as a web application at http://opig.stats.ox.ac.uk/webapps/pears .
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An antibody-focused side-chain approach such as PEARS could yield better results than protein-generic approaches such as SCWRL.
“Here, protein-generic approaches such as SCWRL [100] can be employed although it has been demonstrated that an antibody-focused approach, such as PEARS, could yield better results [98].”
From Computational approaches to therapeutic antibody design: established methods and emerging trends (2020) · cites this paper as [98]
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Cites
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970 · see citing works
Comparative Protein Modelling by Satisfaction of Spatial Restraints
Šali, A., & Blundell, T. L. (1993) · Journal of Molecular Biology · doi:10.1006/jmbi.1993.1626 · Cited by 12,977 · 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
CHARMM36 all-atom additive protein force field: Validation based on comparison to NMR data
Huang, J., & MacKerell, A. D. (2013) · Journal of Computational Chemistry · doi:10.1002/jcc.23354 · Cited by 4,484 · see citing works
Structure and function of immunoglobulins
Schroeder, H. W., & Cavacini, L. A. (2010) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2009.09.046 · Cited by 1,923 · see citing works
The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design
Alford, R. F., Leaver-Fay, A., Jeliazkov, J. R., O’Meara, M. J., DiMaio, F. P., Park, H., Shapovalov, M. V., Renfrew, P. D., Mulligan, V. K., Kappel, K., et al. (2017) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00125 · Cited by 1,532 · 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
Improved prediction of protein side‐chain conformations with SCWRL4
Krivov, G. G., Shapovalov, M. V., & Dunbrack, R. L. (2009) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.22488 · Cited by 1,312 · see citing works
Shapovalov, M. V., & Dunbrack, R. L. (2011) · Structure · doi:10.1016/j.str.2011.03.019 · Cited by 1,000 · see citing works
A graph‐theory algorithm for rapid protein side‐chain prediction
Canutescu, A. A., Shelenkov, A. A., & Dunbrack, R. L. (2003) · Protein Science · doi:10.1110/ps.03154503 · Cited by 1,000 · see citing works
Backbone-dependent rotamer library for proteins. Application to side-chain prediction.
Dunbrack, R. L., & Karplus, M. (1993) · Journal of Molecular Biology · doi:10.1006/jmbi.1993.1170 · Cited by 949 · 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
Bayesian statistical analysis of protein side‐chain rotamer preferences
Dunbrack, R. L., & Cohen, F. E. (1997) · Protein Science · doi:10.1002/pro.5560060807 · Cited by 776 · see citing works
Cuff, J. A., & Barton, G. J. (2000) · Proteins Structure Function and Bioinformatics · doi:10.1002/1097-0134(20000815)40:3 · Cited by 768 · see citing works
The dead-end elimination theorem and its use in protein side-chain positioning
Desmet, J., Maeyer, M. D., Hazes, B., & Lasters, I. (1992) · Nature · doi:10.1038/356539a0 · Cited by 699 · 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
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
A new approach to the rapid determination of protein side chain conformations.
Tufféry, P., Etchebest, C., Hazout, S., & Lavery, R. (1991) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.1991.10507882 · Cited by 358 · see citing works
Conformational analysis of the backbone-dependent rotamer preferences of protein sidechains
Dunbrack, R. L., & Karplus, M. (1994) · Nature Structural & Molecular Biology · doi:10.1038/nsb0594-334 · Cited by 347 · see citing works
Computational design of antibody-affinity improvement beyond in vivo maturation
Lippow, S. M., Wittrup, K. D., & Tidor, B. (2007) · Nature Biotechnology · doi:10.1038/nbt1336 · Cited by 342 · see citing works
Reichert, J. M. (2016) · mAbs · doi:10.1080/19420862.2016.1269580 · Cited by 244 · 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
Generation of bispecific IgG antibodies by structure-based design of an orthogonal Fab interface
Lewis, S. M., Wu, X., Pustilnik, A., Sereno, A., Huang, F., Rick, H. L., Guntas, G., Leaver‐Fay, A., Smith, E. M., Ho, C., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.2797 · Cited by 234 · 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
Protein folding and de novo protein design for biotechnological applications
Khoury, G. A., Smadbeck, J. B., Kieslich, C. A., & Floudas, C. A. (2013) · Trends in biotechnology · doi:10.1016/j.tibtech.2013.10.008 · Cited by 162 · see citing works
FREAD revisited: accurate loop structure prediction using a database search algorithm
Choi, Y., & Deane, C. M. (2010) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.22658 · Cited by 161 · 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
The use of position‐specific rotamers in model building by homology
Chinea, G., Padrón, G., Hooft, R. W. W., Sander, C., & Vriend, G. (1995) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.340230315 · Cited by 142 · see citing works
Disulfide Bond Introduction for General Stabilization of Immunoglobulin Heavy-Chain Variable Domains
Saerens, D., Conrath, K., Govaert, J., & Muyldermans, S. (2008) · Journal of Molecular Biology · doi:10.1016/j.jmb.2008.01.022 · Cited by 142 · 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
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 · see citing works
Practical conversion from torsion space to Cartesian space for in silico protein synthesis
Parsons, J., Holmes, J. B., Rojas, J. M., Tsai, J. W., & Strauss, C. E. M. (2005) · Journal of Computational Chemistry · doi:10.1002/jcc.20237 · Cited by 97 · see citing works
Pantazes, R. J., & Maranas, C. D. (2010) · Protein Engineering Design and Selection · doi:10.1093/protein/gzq061 · Cited by 97 · see citing works
Human Germline Antibody Gene Segments Encode Polyspecific Antibodies
Willis, J. R., Briney, B. S., DeLuca, S., Crowe, J. E., & Meiler, J. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003045 · Cited by 88 · see citing works
One Size Does Not Fit All: The Limits of Structure-Based Models in Drug Discovery
Ross, G. A., Morris, G. M., & Biggin, P. C. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct4004228 · Cited by 77 · see citing works
Antibody humanization by structure-based computational protein design
Choi, Y., Hua, C. K., Sentman, C. L., Ackerman, M. E., & Bailey‐Kellogg, C. (2015) · mAbs · doi:10.1080/19420862.2015.1076600 · Cited by 77 · see citing works
Prediction of protein–protein binding free energies
Vreven, T., Hwang, H., Pierce, B. G., & Weng, Z. (2012) · Protein Science · doi:10.1002/pro.2027 · Cited by 76 · see citing works
Fera, D., Schmidt, A. G., Haynes, B. F., Gao, F., Liao, H., Kepler, T. B., & Harrison, S. C. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1409954111 · Cited by 71 · see citing works
Nagata, K., Randall, A. Z., & Baldi, P. (2011) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.23170 · Cited by 66 · see citing works
OPUS‐Rota: A fast and accurate method for side‐chain modeling
Lu, M., Dousis, A. D., & Ma, J. (2008) · Protein Science · doi:10.1110/ps.035022.108 · Cited by 61 · see citing works
RASP: rapid modeling of protein side chain conformations
Miao, Z., Cao, Y., & Jiang, T. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr538 · Cited by 59 · see citing works
Beyond rotamers: a generative, probabilistic model of side chains in proteins
Harder, T., Boomsma, W., Paluszewski, M., Frellsen, J., Johansson, K. E., & Hamelryck, T. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-306 · Cited by 58 · see citing works
New Dynamic Rotamer Libraries: Data-Driven Analysis of Side-Chain Conformational Propensities
Towse, C., Rysavy, S. J., Vulovic, I., & Daggett, V. (2016) · Structure · doi:10.1016/j.str.2015.10.017 · Cited by 50 · see citing works
Liang, S., Zhou, Y., Grishin, N. V., & Standley, D. M. (2011) · Journal of Computational Chemistry · doi:10.1002/jcc.21747 · Cited by 37 · see citing works
Igawa, T., Tsunoda, H., Kikuchi, Y., Yoshida, M., Tanaka, M., Koga, A., Sekimori, Y., Orita, T., Aso, Y., Hattori, K., et al. (2010) · Protein Engineering Design and Selection · doi:10.1093/protein/gzq034 · Cited by 36 · see citing works
Assessment of protein side-chain conformation prediction methods in different residue environments
Peterson, L. X., Kang, X., & Kihara, D. (2014) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24552 · Cited by 35 · see citing works
Protein side chain conformation predictions with an MMGBSA energy function
Gaillard, T., Panel, N., & Simonson, T. (2016) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25030 · Cited by 25 · see citing works
Protein side-chain packing problem: is there still room for improvement?
Colbes, J. D., Corona, R. I., Lezcano, C., Rodríguez, D., & Brizuela, C. A. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbw079 · Cited by 10 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [38]KABAT, E., WU, T., PERRY, H., GOTTESMAN, K., AND KOELER, C. 1991. Sequences of proteins of immunological interest. U.S. Dept. of Health and Human Services, Public Health Service, National Institutes of Health, 5th edition.
Cited by
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“The fourth step involves building and refining of the side-chains [98].”
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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
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Schneider, C., Buchanan, A., Taddese, B., & Deane, C. M. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btab660 · Cited by 102 · see citing works
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Li, L., Chen, S., Miao, Z., Liu, Y., Liu, X., Xiao, Z. J., & Cao, Y. (2019) · Protein Science · doi:10.1002/pro.3633 · Cited by 62 · see citing works
Computational reconstruction of atomistic protein structures from coarse-grained models
Badaczewska-Dawid, A. E., Koliński, A., & Kmiecik, S. (2019) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2019.12.007 · Cited by 56 · see citing works
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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
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- Authors
- Leem, J., Georges, G., Shi, J., & Deane, C. M.
- Affiliations
- University of Oxford (GB)
- Pharma Research and Early Development, Large Molecule Research, Roche Innovation Center Munich, Nonnenwald 2 Penzberg 82377 Germany
- UCB Pharma (United Kingdom) (GB)
- Corresponding author
- Deane, Charlotte M.
- Venue
- Proteins Structure Function and Bioinformatics
- Publisher
- Wiley
- Published
- 2018-01-25
- Volume
- 86
- Issue
- 4
- Pages
- 383-392
- ISSN
- 0887-3585 (ISSN-L)
- 1097-0134
- OpenAlex
- W2783531632
- Cited by
- 22
- References
- 50
- Citations by year
- 2025: 3 · 2024: 2 · 2023: 4 · 2022: 4 · 2021: 3 · 2020: 1 · 2019: 5
- FWCI
- 0.80
- Type
- journal article
- Language
- en
- Update notices
- erratum (2018-05-14)
- Open access
- closed
- Locations
- Proteins Structure Function and Bioinformatics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Engineering and Physical Sciences Research Council
- Medical Research Council (EP/G037280/1, EP/L016044/1)
- 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
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Keywords
- rotamer library, pear, antibody structure prediction
- MeSH terms
- Algorithms, Animals, Antibodies, Disulfides, Humans, Hydrogen Bonding, Immunoglobulin Variable Region, Models, Molecular, Protein Conformation, Databases, Protein
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