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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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Optimization of multi-site nicking mutagenesis for generation of large, user-defined combinatorial libraries
Abstract Generating combinatorial libraries of specific sets of mutations are essential for addressing protein engineering questions involving contingency in molecular evolution, epistatic relationships between mutations, as well as functional antibody and enzyme engineering. Here we present optimization of a combinatorial mutagenesis method involving template-based nicking mutagenesis, which allows for the generation of libraries with >99% coverage for tens of thousands of user-defined variants. The non-optimized method resulted in low library coverage, which could be rationalized by a model of oligonucleotide annealing bias resulting from the nucleotide mismatch free-energy difference between mutagenic oligo and template. The optimized method mitigated this thermodynamic bias using longer primer sets and faster annealing conditions. Our updated method, applied to two antibody fragments, delivered between 99.0% (32451/32768 library members) to >99.9% coverage (32757/32768) for our desired libraries in 2 days and at an approximate 140-fold sequencing depth of coverage.
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- Award ID(s):
- 2030221
- PAR ID:
- 10345156
- Date Published:
- Journal Name:
- Protein Engineering, Design and Selection
- Volume:
- 34
- ISSN:
- 1741-0126
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Heterologous tRNAs used for noncanonical amino acid (ncAA) mutagenesis in mammalian cells typically show poor activity. We recently introduced a virus‐assisted directed evolution strategy (VADER) that can enrich improved tRNA mutants from naïve libraries in mammalian cells. However, VADER was limited to processing only a few thousand mutants; the inability to screen a larger sequence space precluded the identification of highly active variants with distal synergistic mutations. Here, we report VADER2.0, which can process significantly larger mutant libraries. It also employs a novel library design, which maintains base‐pairing between distant residues in the stem regions, allowing us to pack a higher density of functional mutants within a fixed sequence space. VADER2.0 enabled simultaneous engineering of the entire acceptor stem ofM. mazeipyrrolysyl tRNA (tRNAPyl), leading to a remarkably improved variant, which facilitates more efficient incorporation of a wider range of ncAAs, and enables facile development of viral vectors and stable cell‐lines for ncAA mutagenesis.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1093/protein/gzab017
