A cell-free nanobody engineering platform rapidly generates SARS-CoV-2 neutralizing nanobodies

Antibody engineering technologies face increasing demands for speed, reliability and scale. We develop CeVICA, a cell-free nanobody engineering platform that uses ribosome display for in vitro selection of nanobodies from a library of 10(11) randomized sequences. We apply CeVICA to engineer nanobodies against the Receptor Binding Domain (RBD) of SARS-CoV-2 spike protein and identify >800 binder families using a computational pipeline based on CDR-directed clustering. Among 38 experimentally-tested families, 30 are true RBD binders and 11 inhibit SARS-CoV-2 pseudotyped virus infection. Affinity maturation and multivalency engineering increase nanobody binding affinity and yield a virus neutralizer with picomolar IC50. Furthermore, the capability of CeVICA for comprehensive binder prediction allows us to validate the fitness of our nanobody library. CeVICA offers an integrated solution for rapid generation of divergent synthetic nanobodies with tunable affinities in vitro and may serve as the basis for automated and highly parallel nanobody engineering. Faster, higher throughput antibody engineering methods are needed. Here the authors present CeVICA, a cell-free nanobody engineering platform using ribosome display and computational clustering analysis for in vitro selection; they use this to develop nanobodies against the RBD of SARS-CoV-2 spike protein.

Automated summary

CeVICA is a cell-free nanobody engineering platform using ribosome display and computational clustering analysis for in vitro selection. It has successfully developed nanobodies against the RBD of SARS-CoV-2 spike protein, with 30 identified as true RBD binders and 11 able to inhibit SARS-CoV-2 pseudotyped virus infection.