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Antibodies are essential biochemical reagents for detecting protein post-translational modifications (PTMs) in complex samples. However, recent efforts in developing PTM-targeting antibodies have reported frequent non-specific binding and limited affinity of such antibodies. To address these challenges, we investigated whether directed evolution could be applied to improve the affinity of a high-specificity antibody targeting phospho-threonine 231 (pT231) of the human microtubule-associated protein tau. On the basis of existing structural information, we hypothesized that improving antibody affinity may come at the cost of loss in specificity. To test this hypothesis, we developed a novel approach using yeast surface display to quantify the specificity of PTM-targeting antibodies. When we affinity-matured the single-chain variable antibody fragment through directed evolution, we found that its affinity can be improved > 20-fold over that of the wild-type antibody, reaching a picomolar range. We also discovered that most of the high-affinity variants exhibit cross-reactivity towards the non-phosphorylated target site, but not to the phosphorylation site with a scrambled sequence. However, systematic quantification of the specificity revealed that such a tradeoff between the affinity and specificity did not apply to all variants and led to the identification of a picomolar-affinity variant that has a matching high specificity of the original phospho-tau antibody. In cell- and tissue-imaging experiments, the high-affinity variant gave significantly improved signal intensity while having no detectable nonspecific binding. These results demonstrate that directed evolution is a viable approach for obtaining high-affinity PTM-specific antibodies, and highlight the importance of assessing the specificity in the antibody engineering process.
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Co-optimization of therapeutic antibody affinity and specificity using machine learning models that generalize to novel mutational space
Abstract Therapeutic antibody development requires selection and engineering of molecules with high affinity and other drug-like biophysical properties. Co-optimization of multiple antibody properties remains a difficult and time-consuming process that impedes drug development. Here we evaluate the use of machine learning to simplify antibody co-optimization for a clinical-stage antibody (emibetuzumab) that displays high levels of both on-target (antigen) and off-target (non-specific) binding. We mutate sites in the antibody complementarity-determining regions, sort the antibody libraries for high and low levels of affinity and non-specific binding, and deep sequence the enriched libraries. Interestingly, machine learning models trained on datasets with binary labels enable predictions of continuous metrics that are strongly correlated with antibody affinity and non-specific binding. These models illustrate strong tradeoffs between these two properties, as increases in affinity along the co-optimal (Pareto) frontier require progressive reductions in specificity. Notably, models trained with deep learning features enable prediction of novel antibody mutations that co-optimize affinity and specificity beyond what is possible for the original antibody library. These findings demonstrate the power of machine learning models to greatly expand the exploration of novel antibody sequence space and accelerate the development of highly potent, drug-like antibodies.
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- Award ID(s):
- 1804313
- PAR ID:
- 10368235
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Nature Communications
- Volume:
- 13
- Issue:
- 1
- ISSN:
- 2041-1723
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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