Back

Fast isolation of sub-nanomolar affinity alpaca nanobody against the Spike RBD of SARS-CoV-2 by combining bacterial display and a simple single-step density gradient selection

Valenzuela Nieto, G. E.; Jara, R.; Himelreichs, J.; Salinas, C.; Pinto, T.; Cheuquemilla, Y.; Margolles, Y.; Lopez Gonzalez del Rey, N.; Miranda Chacon, Z.; Cuevas, A.; Berking, A.; Deride, C.; Gonzalez-Moraga, S.; Mancilla, H.; Maturana, D.; Langer, A.; Toledo, J. P.; Müller, A.; Uberti, B.; Krall, P.; Ehrenfeld, P.; Blesa, J.; Chana-Cuevas, P.; Rehren, G.; Fernandez, L. A.; Rojas-Fernandez, A.

2020-06-10 molecular biology
10.1101/2020.06.09.137935 bioRxiv
Show abstract

Despite unprecedented global efforts to rapidly develop SARS-CoV-2 treatments, in order to reduce the burden placed on health systems, the situation remains critical. Effective diagnosis, treatment, and prophylactic measures are urgently required to meet global demand: recombinant antibodies fulfill these requirements and have marked clinical potential. Here, we describe the fast-tracked development of an alpaca Nanobody specific for the receptor-binding-domain (RBD) of the SARS-CoV-2 Spike protein with therapeutic potential applicability. We present a rapid method for nanobody isolation that includes an optimized immunization regimen coupled with VHH library E. coli surface display, which allows single-step selection of high-affinity nanobodies using a simple density gradient centrifugation of the bacterial library. The selected single and monomeric Nanobody, W25, binds to the SARS-CoV-2 S RBD with sub-nanomolar affinity and efficiently competes with ACE-2 receptor binding. Furthermore, W25 potently neutralizes SARS-CoV-2 wild type and the D614G variant with IC50 values in the nanomolar range, demonstrating its potential as antiviral agent.

Matching journals

The top 10 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.