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What is Computational Antibody Design?
What is Computational Antibody Design?
Biointron2024-10-04Read time: 8 mins
DOI:10.1016/j.ijbiomac.2023.125733
The technological advancements of the past few decades have revolutionized antibody discovery, production, and optimization. A particularly exciting approach is computational protein modeling and design, which have benefitted from the massive volume of next generation sequencing data and a combination of bioinformatics, machine learning, and artificial intelligence (AI).1
An example of this is AlphaFold, an AI system used to predict the 3D structure of a protein from amino acid sequences. AlphaFold2 has been used to model antibody-antigen complexes, as well as helping to predict the structure of protein complexes at much higher accuracy than classical docking approaches.2,3
From Sequence to Structure: The Role of AI in Antibody Design
One of the most transformative developments in computational biology has been the use of AI systems like AlphaFold. Developed by DeepMind, AlphaFold is a tool that predicts the 3D structure of proteins based on their amino acid sequences. Antibodies, which are complex protein structures, need accurate 3D structural prediction for scientists to study their interactions with antigens and other targets.
AlphaFold has become a key player in predicting antibody-antigen complexes. Accurate modeling of these complexes is crucial for understanding how an antibody binds to a target antigen. Traditional methods like X-ray crystallography and cryo-electron microscopy (cryo-EM) are highly accurate but often slow and expensive. AlphaFold enables researchers to bypass some of these challenges, providing rapid, high-quality structural models that can inform experimental design. In the context of antibody engineering, AI-driven approaches offer several benefits:
Structural Optimization: AI tools predict key regions like the complementarity-determining regions (CDRs), the hypervariable loops of an antibody responsible for antigen binding. This allows researchers to optimize these regions for better specificity and binding affinity.
Antigen-Antibody Docking: Predicting the interaction between an antibody and its target antigen is crucial for designing antibodies that bind with high precision. AlphaFold has improved the accuracy of antigen-antibody docking models, which traditionally relied on lower-precision docking algorithms.
Reducing Experimental Workload: By providing reliable structural predictions, AI models like AlphaFold minimize the need for multiple rounds of experimental validation, saving both time and resources in antibody discovery and development.
Machine Learning in Antibody Design: Predictive Power
Machine learning (ML) algorithms can help in analyzing large datasets from NGS and other high-throughput techniques. These algorithms predict how changes in the antibody's sequence will impact its binding affinity, stability, and immunogenicity. This predictive power makes ML invaluable in guiding the iterative process of antibody optimization.
ML-based models can be trained on existing antibody databases to identify patterns that correspond to high-functioning antibodies. For instance, given a large enough dataset, ML algorithms can identify sequence features that are correlated with a stronger immune response or reduced off-target effects. The trained models can then predict novel sequences that are more likely to exhibit these desirable characteristics. Applications in Antibody Design Include:
Predicting Developability: Developability refers to an antibody's suitability for therapeutic use, encompassing its manufacturability, stability, and solubility. ML models help flag sequence variants with poor developability early in the design process, preventing costly downstream failures.
Binding Affinity Prediction: ML algorithms are used to predict binding affinity based on antibody sequence data. This helps refine antibody candidates before conducting labor-intensive binding assays, expediting the identification of leads with optimal affinity.
Antibody Libraries: ML can also be used to generate virtual antibody libraries, which simulate diverse antibody populations. This enables researchers to select promising candidates in silico before moving to experimental validation.
Computational Design of Bispecific and Multispecific Antibodies
The increasing complexity of therapeutic needs has driven demand for bispecific and multispecific antibodies—antibodies that can bind to two or more distinct antigens simultaneously. Computational methods are particularly useful in the design of these complex antibodies, as they allow scientists to model the binding behavior and interactions of different antigen-binding regions (paratopes).
Traditional antibody design methods face significant challenges when engineering bispecifics due to the potential for steric clashes or unfavorable folding. Computational models mitigate these issues by predicting how different binding domains will orient themselves relative to each other. This helps ensure that both antigen-binding regions function independently without interfering with each other.4
Additionally, computational design tools enable rapid iteration when modifying the sequence and structure of bispecific antibodies. By virtually modeling the effects of these changes, researchers can optimize both binding and stability, leading to more effective therapeutic candidates.
Reducing Immunogenicity: A Key Challenge in Antibody Engineering
Another aspect of antibody design is reducing immunogenicity—the tendency of an antibody to provoke an immune response when introduced into the body. Immunogenicity can compromise the efficacy of therapeutic antibodies and lead to adverse reactions in patients. Computational tools aid in the design of antibodies that minimize immunogenicity by identifying potentially immunogenic regions and modifying them while maintaining the antibody's functional integrity.
AI and ML models, trained on data from clinical trials and immune response assays, have become increasingly adept at predicting immunogenic regions. By using these predictive tools, scientists can design antibodies that are more likely to evade immune detection, increasing their safety profile for use in humans.
T-cell Epitope Prediction: Computational tools can predict T-cell epitopes—regions of the antibody that might trigger an immune response. By identifying and mutating these regions, designers can create antibodies that are less likely to be recognized as foreign by the immune system.5
Humanization of Antibodies: Humanization involves modifying non-human antibodies to resemble human antibody structures. Computational models assist in identifying which residues to replace in order to reduce immunogenicity while retaining binding activity.
The Future of Computational Antibody Design
As the fields of computational biology, AI, and machine learning continue to evolve, their integration into antibody design is expected to grow. Future advances will likely focus on further improving the speed and accuracy of antibody structure prediction, as well as refining the ability to predict antibody behavior in vivo.
Richard A Norman, Francesco Ambrosetti, Alexandre M J J Bonvin, Lucy J Colwell, Sebastian Kelm, Sandeep Kumar, Konrad Krawczyk, Computational approaches to therapeutic antibody design: established methods and emerging trends, Briefings in Bioinformatics, Volume 21, Issue 5, September 2020, Pages 1549–1567, https://doi.org/10.1093/bib/bbz095
Ruffolo, J.A., Chu, LS., Mahajan, S.P. et al. Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies. Nat Commun 14, 2389 (2023). https://doi.org/10.1038/s41467-023-38063-x
Gao, M., Nakajima An, D., Parks, J. M., & Skolnick, J. (2022). AF2Complex predicts direct physical interactions in multimeric proteins with deep learning. Nature Communications, 13(1), 1-13. https://doi.org/10.1038/541467-022-29394-2
Madsen, A. V., Pedersen, L. E., Kristensen, P., & Goletz, S. (2024). Design and engineering of bispecific antibodies: Insights and practical considerations. Frontiers in Bioengineering and Biotechnology, 12. https://doi.org/10.3389/fbioe.2024.1352014
