This post was written by Alyssa Shepard and Angelo Nicolaci, a PhD student at Moffitt Cancer Center.
Engineering usually calls to mind building things, lots of math, and maybe heavy machinery. But not all engineering is at a large scale — some is done on much smaller equipment on a lab bench. The best part? Less complicated math.
Bioengineering is a field that has been gaining a lot of traction in recent years. The goal is to engineer the most miniscule parts of life, like proteins, to study or alter their function for a variety of purposes. One focus of bioengineering is antibody engineering, to improve reagents and therapeutics.
What is antibody engineering (and why do we like it)?
Antibody engineering is the modification of antibody sequences and structures to affect their function. Usually, the goal is to enhance their efficacy, safety, and/or specificity. Antibodies are frequently used both benchside and in the clinic.
At the bench, improving antibodies means improving research. More specific antibodies support common research tools like western blots, ELISAs, immunoprecipitation, and flow cytometry. The better these tools are, the better the reproducibility of experiments and the validity of conclusions.
Antibody engineering also benefits antibody-based therapeutics. Whether antibodies are used directly as therapeutic agents or serve as vehicles for delivering other treatments, their efficacy can be significantly increased through structural and functional optimization to increase the specificity and stability. Optimizing antibodies for better pharmacokinetics and reduced immunogenicity allows for more efficient and targeted delivery of treatments, ultimately increasing treatment effectiveness.
How do you engineer an antibody?
There are many, many ways to engineer an antibody, thanks to the versatility of the basic antibody structure (Figure 1). Antibodies can be broken up into smaller parts, the variable regions can be altered for specific functions, the constant regions can be altered for different isotypes, and so much more.
How you engineer an antibody depends on your ultimate goal, and some engineering projects will be easier and quicker than others. We’ve covered some methods in previous blog posts, like developing recombinant antibodies, antibody conjugation, chimeric antibodies, and smaller engineered antibody fragments. One technique we haven’t discussed in depth is antibody directed evolution. This is a larger process of gradually changing and selecting the best antibodies for a specific purpose.
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Figure 1: Overview of antibody engineering methods. (A) Basic structure of an antibody. (B) Examples of engineered antibodies, including chimerization, conjugation, and antibody fragments. Fab: fragment antigen binding; Fc: fragment crystallizable; IgG: immunoglobin G; ScFv: single-chain fragment variable; sdAb: single-domain antibody (often referred to as nanobodies). Created with BioRender.com. |
Antibody directed evolution
Directed evolution is a method employed to rapidly change a protein towards a specific goal, like improved specificity. There are different ways to achieve this goal, including error-prone PCR, degenerate primers, gene shuffling, and structure-guided directed evolution.
The nice thing about these methods is that they all follow the same general cyclical process (Figure 2). The differences typically come in when generating the library of mutants and how they are screened. When conducting antibody evolution experiments, you’ll typically go through this cycle many times, until you achieve whatever goal you’re aiming for. When you complete each cycle, your best-performing mutant is usually the starting point for the next cycle.
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Figure 2: General process of antibody directed evolution. Created with BioRender.com. |
Generating mutant libraries
Error-prone PCR
So, you want to improve an antibody, but don’t have a specific mutation in mind. Generating a random library of mutants using error-prone PCR might be a good solution. The process of error-prone PCR is pretty much exactly what it sounds like — a PCR, but with errors! On any normal day, you wouldn’t want errors in a PCR and would use a high-fidelity polymerase to ensure accuracy. In this case, you would want a low-fidelity polymerase, like Taq. Other PCR conditions can be changed to further lower fidelity, like changing the metal cofactor and the dNTP ratio. The resulting random errors can give you new and unexpected sequences.
Degenerate primers
Using degenerate primers is similar to error-prone PCR. Degenerate primers are a group of primers that contains a variety of degenerate codons (different codon sequences that code for the same amino acid) for various amino acids. Essentially, instead of forcing sequence diversity using error-prone polymerases, you force sequence diversity using a pool of different primers. While error-prone PCR is usually employed to mutate whole antibody sequences, degenerate primers can be designed to selectively mutate specific regions of your antibody.
Gene shuffling
Perhaps you like bits and pieces of an antibody but want to know if different combinations or even different chimeras might function better. In that case, gene shuffling might be a good option. In gene shuffling, you chop up your target sequences into fragments, typically using a DNase. Then you run a PCR without primers to randomly anneal and extend the DNA fragments, followed by an additional PCR with primers to get full sequences. In the end, you’ll have a full-length antibody sequence, but with random combinations of the different sections.
Structure-guided directed evolution
If you desire a bit more control, structure guided directed evolution might be more your style. Instead of leaving sequence generation to chance, you start with a known or predicted structure of your antibody-antigen complex as a template. This structural framework then guides the specific mutations you choose, whether it's tweaking the antibody-antigen interface or fine-tuning the regions you want to evolve. With this added layer of insight, you can shrink your mutant library and increase the chances of finding beneficial mutations. In other words, structure-guided evolution allows you to combine the strengths of rational design with the power of directed evolution, all while maintaining a higher degree of control and efficiency!
Screening mutant libraries
After generating your mutant libraries, you have to screen thousands or millions of different mutants for the specific functionality you’re looking for. For example, if you were aiming to increase an antibody’s binding specificity, you can perform binding assays against the target antigen to identify mutants with enhanced specificity. Assay conditions, such as pH or temperature, can be adjusted to find the best-performing mutant antibody in those conditions. If you think functionality can be ramped up even more, the best mutant can then be used as the starting point in the next round of mutant library generation.
To evaluate improved properties across large mutant libraries, high-throughput screening techniques such as ELISA and cell surface display are common. ELISA enables quantitative measurement of antigen-antibody interactions in a plate-based format, making it suitable for comparing relative affinities across mutants. Cell surface display systems, such as bacterial, yeast, or mammalian display, allow for the presentation of your mutant libraries on the surface of cells. This enables direct screening via flow cytometry for improved binding under defined environmental conditions. These methods are especially powerful when coupled with iterative rounds of selection and mutagenesis to refine antibody performance.
If you are interested in learning more about antibody engineering technologies, the Institute of Protein Innovation (IPI) provides information on their large-scale recombinant antibody production platform. IPI is a leading expert on antibody discovery and engineering, and have recently launched a course on yeast display for antibody discovery, to share their expertise on the topic.
Evolving the future of antibodies
Antibodies are an extremely common lab reagent, so maintaining high-quality, validated antibodies with verifiable data is critical, especially when sharing with other scientists. Addgene partners with IPI to provide recombinant antibodies that target families of cell surface and secreted proteins (glypicans, integrins, and more coming soon) as well as epitope tags like His, Flag, and Biotin. The antibodies developed by IPI are extensively characterized and validated, with the application data from IPI and other labs easily viewed in Addgene’s Data Hub.
The diversity of antibody reagents is continuing to expand, thanks to the efforts of antibody engineers. Many advancements have allowed us to develop more specific antibodies for therapies and improve their delivery methods to various cells. The wealth of smaller engineered antibody fragments has further increased possibilities for synthetic antibody products and development methods. Despite recent advances, there is still always room to improve, and scientists will continue to engineer the future of antibodies!
Angelo Nicolaci is a PhD candidate in the lab of Jennifer Binning at Moffitt Cancer Center in Tampa, Florida. His projects focus on using yeast display, structural biology, and protein engineering to develop antibody-based cancer therapeutics.
References and Resources
References
Chan, A. C., & Carter, P. J. (2010). Therapeutic antibodies for autoimmunity and inflammation. Nature Reviews Immunology, 10(5), 301–316. https://doi.org/10.1038/nri2761
Chiu, M. L., Goulet, D. R., Teplyakov, A., & Gilliland, G. L. (2019). Antibody Structure and Function: the basis for Engineering therapeutics. Antibodies, 8(4), 55. https://doi.org/10.3390/antib8040055
Holliger, P., & Hudson, P. J. (2005). Engineered antibody fragments and the rise of single domains. Nature Biotechnology, 23(9), 1126–1136. https://doi.org/10.1038/nbt1142
Kang, B. H., Lax, B. M., & Wittrup, K. D. (2022). Yeast Surface Display for Protein Engineering: library generation, screening, and affinity maturation. Methods in Molecular Biology, 29–62. https://doi.org/10.1007/978-1-0716-2285-8_2
McMahon, C., Baier, A. S., Pascolutti, R., Wegrecki, M., Zheng, S., Ong, J. X., Erlandson, S. C., Hilger, D., Rasmussen, S. G. F., Ring, A. M., Manglik, A., & Kruse, A. C. (2018). Yeast surface display platform for rapid discovery of conformationally selective nanobodies. Nature Structural & Molecular Biology, 25(3), 289–296. https://doi.org/10.1038/s41594-018-0028-6
Packer, M. S., & Liu, D. R. (2015). Methods for the directed evolution of proteins. Nature Reviews Genetics, 16(7), 379–394. https://doi.org/10.1038/nrg3927
Wang, Y., Xue, P., Cao, M., Yu, T., Lane, S. T., & Zhao, H. (2021). Directed Evolution: Methodologies and applications. Chemical Reviews, 121(20), 12384–12444. https://doi.org/10.1021/acs.chemrev.1c00260
Wilson, D. S., & Keefe, A. D. (2000). Random mutagenesis by PCR. Current Protocols in Molecular Biology, 51(1). https://doi.org/10.1002/0471142727.mb0803s51
Additional resources on the Addgene blog
- All Antibody Topics
- Antibody Data Repositories and Search Engines
- Antibodies 101: Producing Recombinant Antibodies
- An Integrin Antibody Toolkit from IPI
Additional resources on Addgene.org
Topics: Antibodies
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