Atomically accurate de novo design of antibodies with RFdiffusion

Nathaniel R. Bennett(University of Washington), Joseph L. Watson(University of Washington), Robert J. Ragotte(University of Washington), Andrew J. Borst(University of Washington), DéJenaé L. See(University of Washington), Connor Weidle(University of Washington), Riti Biswas(University of Washington), Yutong Yu(University of Washington), Ellen Shrock(University of Washington), Russell Ault(Children's Hospital of Philadelphia), Philip J. Y. Leung(University of Washington), Buwei Huang(University of Washington), Inna Goreshnik(Howard Hughes Medical Institute), John Tam(University of Washington), Kenneth D. Carr(University of Washington), Benedikt Singer(University of Washington), Cameron Criswell(University of Washington), Basile I. M. Wicky(Korea Advanced Institute of Science and Technology), Dionne Vafeados(University of Washington), Mariana Garcia Sanchez(University of Washington), Ho Min Kim(Korea Advanced Institute of Science and Technology), Susana Vázquez Torres(University of Washington), Sidney Chan(University of Washington), Shirley M. Sun(Children's Hospital of Philadelphia), Timothy Spear(Children's Hospital of Philadelphia), Yi Sun(Children's Hospital of Philadelphia), Keelan O’Reilly, John M. Maris(Children's Hospital of Philadelphia), Nikolaos G. Sgourakis(Children's Hospital of Philadelphia), Roman A. Melnyk(University of Toronto), Chang C. Liu(University of California, Irvine), David Baker(Howard Hughes Medical Institute)
bioRxiv (Cold Spring Harbor Laboratory)
March 18, 2024
Cited by 186Open Access
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Abstract

Abstract Despite the central role that antibodies play in modern medicine, there is currently no method to design novel antibodies that bind a specific epitope entirely in silico . Instead, antibody discovery currently relies on animal immunization or random library screening approaches. Here, we demonstrate that combining computational protein design using a fine-tuned RFdiffusion network alongside yeast display screening enables the generation of antibody variable heavy chains (VHHs) and single chain variable fragments (scFvs) that bind user-specified epitopes with atomic-level precision. To verify this, we experimentally characterized VHH binders to four disease-relevant epitopes using multiple orthogonal biophysical methods, including cryo-EM, which confirmed the proper Ig fold and binding pose of designed VHHs targeting influenza hemagglutinin and Clostridium difficile toxin B (TcdB). For the influenza-targeting VHH, high-resolution structural data further confirmed the accuracy of CDR loop conformations. While initial computational designs exhibit modest affinity, affinity maturation using OrthoRep enables production of single-digit nanomolar binders that maintain the intended epitope selectivity. We further demonstrate the de novo design of single-chain variable fragments (scFvs), creating binders to TcdB and a Phox2b peptide-MHC complex by combining designed heavy and light chain CDRs. Cryo-EM structural data confirmed the proper Ig fold and binding pose for two distinct TcdB scFvs, with high-resolution data for one design additionally verifying the atomically accurate conformations of all six CDR loops. Our approach establishes a framework for the rational computational design, screening, isolation, and characterization of fully de novo antibodies with atomic-level precision in both structure and epitope targeting.


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