High-throughput antibody engineering in mammalian cells by CRISPR/Cas9-mediated homology-directed mutagenesis.

Antibody engineering is often performed to improve therapeutic properties by directed evolution, usually by high-throughput screening of phage or yeast display libraries. Engineering antibodies in mammalian cells offer advantages associated with expression in their final therapeutic format (full-length glycosylated IgG); however, the inability to express large and diverse libraries severely limits their potential throughput. To address this limitation, we have developed homology-directed mutagenesis (HDM), a novel method which extends the concept of CRISPR/Cas9-mediated homology-directed repair (HDR). HDM leverages oligonucleotides with degenerate codons to generate site-directed mutagenesis libraries in mammalian cells. By improving HDR to a robust efficiency of 15-35% and combining mammalian display screening with next-generation sequencing, we validated this approach can be used for key applications in antibody engineering at high-throughput: rational library construction, novel variant discovery, affinity maturation and deep mutational scanning (DMS). We anticipate that HDM will be a valuable tool for engineering and optimizing antibodies in mammalian cells, and eventually enable directed evolution of other complex proteins and cellular therapeutics.

Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning.

The optimization of therapeutic antibodies is time-intensive and resource-demanding, largely because of the low-throughput screening of full-length antibodies (approximately 1 × 103 variants) expressed in mammalian cells, which typically results in few optimized leads. Here we show that optimized antibody variants can be identified by predicting antigen specificity via deep learning from a massively diverse space of antibody sequences. To produce data for training deep neural networks, we deep-sequenced libraries of the therapeutic antibody trastuzumab (about 1 × 104 variants), expressed in a mammalian cell line through site-directed mutagenesis via CRISPR-Cas9-mediated homology-directed repair, and screened the libraries for specificity to human epidermal growth factor receptor 2 (HER2). We then used the trained neural networks to screen a computational library of approximately 1 × 108 trastuzumab variants and predict the HER2-specific subset (approximately 1 × 106 variants), which can then be filtered for viscosity, clearance, solubility and immunogenicity to generate thousands of highly optimized lead candidates. Recombinant expression and experimental testing of 30 randomly selected variants from the unfiltered library showed that all 30 retained specificity for HER2. Deep learning may facilitate antibody engineering and optimization.

Synthetic Standards Combined With Error and Bias Correction Improve the Accuracy and Quantitative Resolution of Antibody Repertoire Sequencing in Human Naïve and Memory B Cells.

High-throughput sequencing of immunoglobulin (Ig) repertoires (Ig-seq) is a powerful method for quantitatively interrogating B cell receptor sequence diversity. When applied to human repertoires, Ig-seq provides insight into fundamental immunological questions, and can be implemented in diagnostic and drug discovery projects. However, a major challenge in Ig-seq is ensuring accuracy, as library preparation protocols and sequencing platforms can introduce substantial errors and bias that compromise immunological interpretation. Here, we have established an approach for performing highly accurate human Ig-seq by combining synthetic standards with a comprehensive error and bias correction pipeline. First, we designed a set of 85 synthetic antibody heavy-chain standards (in vitro transcribed RNA) to assess correction workflow fidelity. Next, we adapted a library preparation protocol that incorporates unique molecular identifiers (UIDs) for error and bias correction which, when applied to the synthetic standards, resulted in highly accurate data. Finally, we performed Ig-seq on purified human circulating B cell subsets (naïve and memory), combined with a cellular replicate sampling strategy. This strategy enabled robust and reliable estimation of key repertoire features such as clonotype diversity, germline segment, and isotype subclass usage, and somatic hypermutation. We anticipate that our standards and error and bias correction pipeline will become a valuable tool for researchers to validate and improve accuracy in human Ig-seq studies, thus leading to potentially new insights and applications in human antibody repertoire profiling.