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Fragment-based computational design of antibodies targeting structured epitopes

DOI: 10.1126/sciadv.abp9540 DOI Help

Authors: Mauricio Aguilar Rangel (University of Cambridge; Stanford University) , Alice Bedwell (University of Cambridge) , Elisa Costanzi (Università degli Studi di Milano) , Ross J. Taylor (University of Cambridge) , Rosaria Russo (Università degli Studi di Milano) , Gonçalo J. L. Bernardes (University of Cambridge) , Stefano Ricagno (Università degli Studi di Milano; IRCCS Policlinico San Donato) , Judith Frydman (Stanford University) , Michele Vendruscolo (University of Cambridge) , Pietro Sormanni (University of Cambridge)
Co-authored by industrial partner: No

Type: Journal Paper
Journal: Science Advances , VOL 8

State: Published (Approved)
Published: November 2022
Diamond Proposal Number(s): 20221

Open Access Open Access

Abstract: De novo design methods hold the promise of reducing the time and cost of antibody discovery while enabling the facile and precise targeting of predetermined epitopes. Here, we describe a fragment-based method for the combinatorial design of antibody binding loops and their grafting onto antibody scaffolds. We designed and tested six single-domain antibodies targeting different epitopes on three antigens, including the receptor-binding domain of the SARS-CoV-2 spike protein. Biophysical characterization showed that all designs are stable and bind their intended targets with affinities in the nanomolar range without in vitro affinity maturation. We further discuss how a high-resolution input antigen structure is not required, as similar predictions are obtained when the input is a crystal structure or a computer-generated model. This computational procedure, which readily runs on a laptop, provides a starting point for the rapid generation of lead antibodies binding to preselected epitopes.

Subject Areas: Biology and Bio-materials, Medicine


Instruments: I04-Macromolecular Crystallography

Added On: 16/11/2022 09:10

Documents:
sciadv.abp9540.pdf

Discipline Tags:

Health & Wellbeing Technique Development - Life Sciences & Biotech Structural biology Drug Discovery Life Sciences & Biotech

Technical Tags:

Diffraction Macromolecular Crystallography (MX)