Rapid generation of potent antibodies by autonomous hypermutation in yeast

By Alon Wellner, Conor McMahon, M. Gilman, J. Clements, S. Clark, Kianna M Nguyen, M. Ho, Jung-Eun Shin, J. Feldman, B. Hauser and 7 more. 2020

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The predominant approach for antibody generation remains animal immunization, which can yield exceptionally selective and potent antibody clones owing to the powerful evolutionary process of somatic hypermutation. However, animal immunization is inherently slow, has poor compatibility with certain antigens (e.g., integral membrane proteins), and suffers from self-tolerance and immunodominance, which limit the functional spectrum of antibodies that can be obtained. Here, we describe Autonomous Hypermutation yEast surfAce Display (AHEAD), a synthetic recombinant antibody generation technology that imitates somatic hypermutation inside engineered yeast. In AHEAD, antibody fragments are encoded on an error-prone orthogonal DNA replication system, resulting in Saccharomyces cerevisiae populations that continuously mutate surface-displayed antibody repertoires. Simple cycles of yeast culturing and enrichment for antigen binding drive the evolution of high-affinity antibody clones in a readily parallelizable process that takes as little as 2 weeks. We applied AHEAD to generate nanobodies against the SARS-CoV-2 S glycoprotein, a GPCR, and other targets. The SARS-CoV-2 nanobodies, concurrently evolved from an open-source naïve nanobody library in 8 independent experiments, reached subnanomolar affinities through the sequential fixation of multiple mutations over 3-8 AHEAD cycles that saw ∼580-fold and ∼925-fold improvements in binding affinities and pseudovirus neutralization potencies, respectively. These experiments highlight the defining speed, parallelizability, and effectiveness of AHEAD and provide a template for streamlined antibody generation at large with salient utility in rapid response to current and future viral outbreaks.

Subfields: Biotechnology, Molecular Biology, Radiology, Nuclear Medicine and Imaging

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Protein design and variant prediction using autoregressive generative models (2021, background): “In combination with a continuous evolution system, candidate binders from the designed library have been identi ﬁ ed and af ﬁ - nity matured after only a few rounds of selection with a single experiment 90 .”