The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Computational design of antibody-affinity improvement beyond in vivo maturation
Open accessAbstract
Antibodies are used extensively in diagnostics and as therapeutic agents. Achieving high-affinity binding is important for expanding detection limits, extending dissociation half-times, decreasing drug dosages and increasing drug efficacy. However, antibody-affinity maturation in vivo often fails to produce antibody drugs of the targeted potency, making further affinity maturation in vitro by directed evolution or computational design necessary. Here we present an iterative computational design procedure that focuses on electrostatic binding contributions and single mutants. By combining multiple designed mutations, a tenfold affinity improvement to 52 pM was engineered into the anti-epidermal growth factor receptor drug cetuximab (Erbitux), and a 140-fold improvement in affinity to 30 pM was obtained for the anti-lysozyme model antibody D44.1. The generality of the methods was further demonstrated through identification of known affinity-enhancing mutations in the therapeutic antibody bevacizumab (Avastin) and the model anti-fluorescein antibody 4-4-20. These results demonstrate computational capabilities for enhancing and accelerating the development of protein reagents and therapeutics.
Overview
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
Verification
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?
In the citing paper
“Selected molecules had better affinity for the target, demonstrating that in some scenarios computational approaches alone can be used for affinity maturation.”
From Computational approaches to therapeutic antibody design: established methods and emerging trends (2020) · cites this paper as [181]
In the citing paper
“The seminal work on the subject was published by Lippow and co-workers, who computationally improved the binding of an antibody against its target, starting from an existing structural complex [181].”
From Computational approaches to therapeutic antibody design: established methods and emerging trends (2020) · cites this paper as [181]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
1 retracted
Computational Design of a Biologically Active Enzyme
Dwyer, M. A., Looger, L. L., & Hellinga, H. W. (2004) · Science · doi:10.1126/science.1098432 · Cited by 295
RetractedDesign 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
Presta, L. G., Chen, H., O'Connor, S. J., Chisholm, V., Meng, Y. G., KRUMMEN, L. A., Winkler, M., & Ferrara, N. (1997) · PubMed · Cited by 1,596
De Novo Protein Design: Fully Automated Sequence Selection
Dahiyat, B. I., & Mayo, S. L. (1997) · Science · doi:10.1126/science.278.5335.82 · Cited by 1,156
Directed evolution of antibody fragments with monovalent femtomolar antigen-binding affinity
Boder, E. T., Midelfort, K. S., & Wittrup, K. D. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.170297297 · Cited by 673
Self-citationComputational design of receptor and sensor proteins with novel functions
Looger, L. L., Dwyer, M. A., Smith, J. J., & Hellinga, H. W. (2003) · Nature · doi:10.1038/nature01556 · Cited by 668
Biological effects in vitro of monoclonal antibodies to human epidermal growth factor receptors.
Sato, J. D., Kawamoto, T., Le, A. D., Mendelsohn, J. M., Polikoff, J., & SATO, G. H. (1983) · PubMed · Cited by 458
Chen, Y., Wiesmann, C., Fuh, G., Li, B., Christinger, H. W., McKay, P., de Vos, A. M., & Lowman, H. B. (1999) · Journal of Molecular Biology · doi:10.1006/jmbi.1999.3192 · Cited by 449
Kinetic and affinity limits on antibodies produced during immune responses.
Foote, J., & Eisen, H. N. (1995) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.92.5.1254 · Cited by 375
Computational Thermostabilization of an Enzyme
Korkegian, A., Black, M. E., Baker, D. A., & Stoddard, B. L. (2005) · Science · doi:10.1126/science.1107387 · Cited by 360
Cited by
Sorted by citations
Items 301–340 of 340
Sachdev, S., & Cheloha, R. W. (2026) · British Journal of Pharmacology · doi:10.1111/bph.70519 · Cited by 1
Screening for Epigenetic Target Genes that Enhance Reprogramming Using Lentiviral-Delivered shRNA
Rim, J. S., Strickler, K. L., Barnes, C. W., Harkins, L. L., Staszkiewicz, J., Power, R., & Eilertsen, K. J. (2010) · Methods in molecular biology · doi:10.1007/978-1-61737-960-4_22 · Cited by 1
Protein Engineering: Methods and Applications
Dobson, C., & Dall’Acqua, W. F. (2017) · Methods and principles in medicinal chemistry · doi:10.1002/9783527699124.ch7 · Cited by 1
A sound strategy for homology modeling-based affinity maturation of a HIF-1α single-domain intrabodyPreprint
Hu, M., Kang, G., Cheng, X. M., Wang, J., Li, R., Bai, Z., Yang, D., & Huang, H. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.14.096149 · Cited by 1
(2012) · Elsevier eBooks · doi:10.1016/b978-1-907568-37-4.50029-4 · Cited by 1
H3-OPT: Accurate prediction of CDR-H3 loop structures of antibodies with deep learning
Chen, H., Fan, X., Zhu, S., Pei, Y., Zhang, X., Zhang, X., Liu, L., Qian, F., & Tian, B. (2023) · doi:10.1101/2023.08.19.553933 · Cited by 1
H3-OPT: Accurate prediction of CDR-H3 loop structures of antibodies with deep learningPreprint
Chen, H., Fan, X., Zhu, S., Pei, Y., Zhang, X., Zhang, X., Liu, L., Qian, F., & Tian, B. (2024) · eLife · doi:10.7554/elife.91512.3 · Cited by 1
Georgiev, I. S. (2009) · DukeSpace (Duke University) · Cited by 1
Retrobiosynthesis of D-glucaric acid in a metabolically engineered strain of Escherichia coli
Moon, T. S. (2009) · DSpace@MIT (Massachusetts Institute of Technology)
Analysis and redesign of protein-protein interactions: A hotspot-centric view
Layton, C. J. (2010) · DukeSpace (Duke University)
Chen, T. S. (2012) · DSpace@MIT (Massachusetts Institute of Technology)
Computer-aided design of functional protein
Kortemme, T. (2009)
Computational Antibody Engineering
Nevanen, T. K., Munck, N., & Lamminmäki, U. (2008) · WORLD SCIENTIFIC eBooks · doi:10.1142/9789812778789_0016
Modulation Of Intrinsic Properties By Computational Design
Xu, F., Nanda, V., & Hsieh, D. H. (2009) · CRC Press eBooks · doi:10.1201/9781420076592.ch16
Modulating Protein Interactions By Rational And Computational Design
Looger, L. L., & Marvin, J. S. (2009) · CRC Press eBooks · doi:10.1201/9781420076592.ch17
Sassolas, A., & Marty, J. (2013) · Series in sensors · doi:10.1201/b15589-11
Rational and Combinatorial Methods to Create Designer Protein Interfaces
Lui, B. H., & Cochran, J. R. (2011) · Comprehensive Biomaterials · doi:10.1016/b978-0-08-055294-1.00258-0
Strategies for Protein Engineering
Sun, M. (2016) · TSpace (University of Toronto)
Altering the solubility of recombinant proteins through modification of surface features
Carballo‐Amador, M. A. (2015) · Research Explorer (The University of Manchester)
Antibody Modeling, Engineering, and Design
Fischman, S., & Ofran, Y. (2018) · Springer eBooks · doi:10.1007/978-3-642-35943-9_10083-1
Cheng, S. (2014) · Deep Blue (University of Michigan)
Karthik, M. V. K., & Shukla, P. (2012) · SpringerBriefs in systems biology · doi:10.1007/978-1-4614-4723-8_1
(2007) · Nature Reviews Drug Discovery · doi:10.1038/nrd2454
H3-OPT: Accurate prediction of CDR-H3 loop structures of antibodies with deep learningPreprint
Chen, H., Fan, X., Zhu, S., Pei, Y., Zhang, X., Zhang, X., Liu, L., Qian, F., & Tian, B. (2023) · eLife · doi:10.7554/elife.91512.1
H3-OPT: Accurate prediction of CDR-H3 loop structures of antibodies with deep learningPreprint
Chen, H., Fan, X., Zhu, S., Pei, Y., Zhang, X., Zhang, X., Liu, L., Qian, F., & Tian, B. (2024) · eLife · doi:10.7554/elife.91512.2
Design and development of vaccines through computational approaches
Rath, S. L., & Tripathy, D. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-15378-5.00023-1
Wen, W., Pan, F., Wang, Z., Li, X., Wang, J., Talib, H. S., Hu, J., Zhao, Y., Liu, Z., Liang, Y., et al. (2024) · Microchemical Journal · doi:10.1016/j.microc.2024.112290
An AI-driven approach for nanobody affinity maturationPreprint
Yang, X., Guo, Z., & Zhao, Q. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.11.687768
Gabrielli, J., Lewis, N. E., Kontoravdi, C., & Ceroni, F. (2025) · Cell Systems · doi:10.1016/j.cels.2025.101424 · Cited by 1
Leach, A. R., & Mobarec, J. C. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-29808-0.00086-8
Computational approaches toward protein design
Traore, S. (2014) · theses.fr (ABES) · doi:10.70675/c27d4ed9z5b88z4a76z87e4z888652ddc315
Introduction to computer-aided drug design
Zhang, C., & Wu, Y. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-45105-8.00012-3
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
Multi-objective antibody design and optimization using machine learning
Kuo, Y., Brown, C. N., Akin, E., Xie, Y., & Tessier, P. M. (2026) · Nature Reviews Bioengineering · doi:10.1038/s44222-026-00444-4
A tethered yeast surface display system facilitates enriching epitope-specific binding interactions
Panton, R. A., Lind, J., Richardson, A., Panozzo, I., García, D. G., Muzaffar, S., & Stern, L. A. (2026) · Protein Engineering Design and Selection · doi:10.1093/protein/gzag019
Niazi, S. K., Magoola, M., & Mariam, Z. (2026) · npj Drug Discovery. · doi:10.1038/s44386-026-00084-z
Wang, Q., Li, C., Nie, J., Yao, X., Guan, S., Hui, J., Li, X., Zhou, J., Yang, S., Ding, F., et al. (2026) · International Immunopharmacology · doi:10.1016/j.intimp.2026.117574
RNA 3D Structure Analysis and Validation, and Design Algorithms for Proteins and RNA
Jain, S. (2015) · DukeSpace (Duke University)
Metadata
- Authors
- Lippow, S. M., Wittrup, K. D., & Tidor, B.
- Affiliations
- Massachusetts Institute of Technology (US)
- Corresponding author
- Wittrup, K Dane
- Tidor, Bruce
- Venue
- Nature Biotechnology
- Published
- 2007-09-23
- Volume
- 25
- Issue
- 10
- Pages
- 1171-1176
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC2803018
- OpenAlex
- W2083028331
- Cited by
- 342
- References
- 30
- Citations by year
- 2026: 9 · 2025: 11 · 2024: 14 · 2023: 17 · 2022: 24 · 2021: 14 · 2020: 22 · 2019: 15 · 2018: 23 · 2017: 18 · 2016: 15 · 2015: 24 · 2014: 23 · 2013: 21 · 2012: 21
- FWCI
- 14.05
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Science Foundation
- National Institutes of Health
- National Cancer Institute (CA96504, R01CA096504)
- National Institute of General Medical Sciences (GM65418, R56GM065418, R01GM065418)
- Topics
- Life Sciences › Immunology and Microbiology › Immunology › Monoclonal and Polyclonal Antibodies Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Keywords
- affinity maturation, computational antibody design, point mutations, cetuximab, anti-EGFR monoclonal antibody, bevacizumab, anti-fluorescein antibody, anti-lysozyme antibody, therapeutic antibodies, directed evolution, binding affinity enhancement
- MeSH terms
- Antibodies, Antibody Affinity, Antigens, Genetic Engineering, Models, Biological, Models, Molecular, Muramidase, Mutation, Protein Binding, Protein Conformation, Yeasts, Computer-Aided Design
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
