The DOI matches its Crossref record; 116 of the 120 references it cites were found in a registry or an index.
RosettaAntibodyDesign (RAbD): A general framework for computational antibody design
Open accessPublished version of a preprint
Abstract
A structural-bioinformatics-based computational methodology and framework have been developed for the design of antibodies to targets of interest. RosettaAntibodyDesign (RAbD) samples the diverse sequence, structure, and binding space of an antibody to an antigen in highly customizable protocols for the design of antibodies in a broad range of applications. The program samples antibody sequences and structures by grafting structures from a widely accepted set of the canonical clusters of CDRs (North et al., J. Mol. Biol., 406:228-256, 2011). It then performs sequence design according to amino acid sequence profiles of each cluster, and samples CDR backbones using a flexible-backbone design protocol incorporating cluster-based CDR constraints. Starting from an existing experimental or computationally modeled antigen-antibody structure, RAbD can be used to redesign a single CDR or multiple CDRs with loops of different length, conformation, and sequence. We rigorously benchmarked RAbD on a set of 60 diverse antibody-antigen complexes, using two design strategies-optimizing total Rosetta energy and optimizing interface energy alone. We utilized two novel metrics for measuring success in computational protein design. The design risk ratio (DRR) is equal to the frequency of recovery of native CDR lengths and clusters divided by the frequency of sampling of those features during the Monte Carlo design procedure. Ratios greater than 1.0 indicate that the design process is picking out the native more frequently than expected from their sampled rate. We achieved DRRs for the non-H3 CDRs of between 2.4 and 4.0. The antigen risk ratio (ARR) is the ratio of frequencies of the native amino acid types, CDR lengths, and clusters in the output decoys for simulations performed in the presence and absence of the antigen. For CDRs, we achieved cluster ARRs as high as 2.5 for L1 and 1.5 for H2. For sequence design simulations without CDR grafting, the overall recovery for the native amino acid types for residues that contact the antigen in the native structures was 72% in simulations performed in the presence of the antigen and 48% in simulations performed without the antigen, for an ARR of 1.5. For the non-contacting residues, the ARR was 1.08. This shows that the sequence profiles are able to maintain the amino acid types of these conserved, buried sites, while recovery of the exposed, contacting residues requires the presence of the antigen-antibody interface. We tested RAbD experimentally on both a lambda and kappa antibody-antigen complex, successfully improving their affinities 10 to 50 fold by replacing individual CDRs of the native antibody with new CDR lengths and clusters.
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“RosettaAntibodyDesign (RAbD) [89] relies on two algorithmic loops referred to the outer and inner loops of design.”
From Computational and artificial intelligence-based methods for antibody development (2023) · cites this paper as [89]
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Gentleman R: R: A language for data analysis and graphics
Ihaka, R., & Gentleman, R. (1996) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.1996.10474713 · Cited by 11,747
Amino acid substitution matrices from protein blocks.
Henikoff, S., & Henikoff, J. G. (1992) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.89.22.10915 · Cited by 6,218
R: A Language for Data Analysis and Graphics
Ihaka, R., & Gentleman, R. C. (1996) · Journal of Computational and Graphical Statistics · doi:10.2307/1390807 · Cited by 4,245
Smith, G. P. (1985) · Science · doi:10.1126/science.4001944 · Cited by 4,143
Phage antibodies: filamentous phage displaying antibody variable domains
McCafferty, J., Griffiths, A. D., Winter, G. P., & Chiswell, D. J. (1990) · Nature · doi:10.1038/348552a0 · Cited by 2,427
Engineered antibody fragments and the rise of single domains
Holliger, P., & Hudson, P. J. (2005) · Nature Biotechnology · doi:10.1038/nbt1142 · Cited by 1,928
Design of a Novel Globular Protein Fold with Atomic-Level Accuracy
Kuhlman, B. A., Dantas, G. A., Ireton, G. C., Varani, G., Stoddard, B. L., & Baker, D. A. (2003) · Science · doi:10.1126/science.1089427 · Cited by 1,666
Making antibodies by phage display technology
Winter, G., Griffiths, A. D., Hawkins, R. E., & Hoogenboom, H. R. (1994) · Annual Review of Immunology · doi:10.1146/annurev.iy.12.040194.002245 · Cited by 1,573
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
Self-citationCanonical 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
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Macromolecular modeling and design in Rosetta: recent methods and frameworks
Leman, J. K., Weitzner, B. D., Lewis, S. M., Adolf-Bryfogle, J., Alam, N., Alford, R. F., Aprahamian, M., Baker, D., Barlow, K. A., Barth, P., et al. (2020) · Nature Methods · doi:10.1038/s41592-020-0848-2 · Cited by 888
Self-citationAdvances in protein structure prediction and design
Kuhlman, B., & Bradley, P. (2019) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-019-0163-x · Cited by 806
Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies
Ruffolo, J. A., Chu, L., Mahajan, S. P., & Gray, J. J. (2023) · Nature Communications · doi:10.1038/s41467-023-38063-x · Cited by 364
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
“Since then, four methods have been made available: OptCDR [139], OptMAVEn[140], AbDesign [143] and RosettaAntibodyDesign [142].”
MethodComputational and artificial intelligence-based methods for antibody development
Kim, J., McFee, M., Fang, Q., Abdin, O., & Kim, P. M. (2023) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2022.12.005 · Cited by 178
Metadata
- Authors
- Adolf-Bryfogle, J., Kalyuzhniy, O., Kubitz, M., Weitzner, B. D., Hu, X., Adachi, Y., Schief, W. R., & Dunbrack, R. L.
- Affiliations
- Scripps Research Institute (US)
- University of Washington (US)
- Fox Chase Cancer Center (US)
- Corresponding author
- Dunbrack, Roland L.
- ORCID
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2018-04-27
- Volume
- 14
- Issue
- 4
- Pages
- e1006112
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC5942852
- OpenAlex
- W2953176058
- Cited by
- 227
- References
- 120
- Citations by year
- 2026: 29 · 2025: 46 · 2024: 42 · 2023: 30 · 2022: 25 · 2021: 21 · 2020: 16 · 2019: 13 · 2018: 5
- FWCI
- 4.89
- Type
- journal article
- Language
- en
- Update notices
- new_version (2018-05-09)
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Funders
- National Institute of General Medical Sciences (R35 GM122517, R01 GM111819)
- National Institute of Allergy and Infectious Diseases (P01 AI094419)
- 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 › Molecular Biology › vaccines and immunoinformatics approaches
- Keywords
- computational antibody design, CDR grafting, sequence design, antibody-antigen interface, immune complexes, sequence profiles, epitope mapping
- MeSH terms
- Amino Acid Sequence, Animals, Antibodies, Antigen-Antibody Complex, Computer Simulation, Humans, Models, Molecular, Monte Carlo Method, Protein Conformation, Software, Drug Design, Protein Engineering, Directed Molecular Evolution, Computational Biology, Complementarity Determining Regions
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