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.
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Selected molecules from Lippow and co-workers had better affinity for the target, demonstrating that in some scenarios computational approaches alone can be used for affinity maturation.
“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]
Lippow and co-workers computationally improved the binding of an antibody against its target, starting from an existing structural complex.
“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]
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- 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
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › 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
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