ABSTRACT
Despite the central role that antibodies play in modern medicine, there is currently no way to rationally design novel antibodies to bind a specific epitope on a target. Instead, antibody discovery currently involves time-consuming immunization of an animal or library screening approaches. Here we demonstrate that a fine-tuned RFdiffusion network is capable of designing de novo antibody variable heavy chains (VHH's) that bind user-specified epitopes. We experimentally confirm binders to four disease-relevant epitopes, and the cryo-EM structure of a designed VHH bound to influenza hemagglutinin is nearly identical to the design model both in the configuration of the CDR loops and the overall binding pose.
ABSTRACT
We are entering an era in which therapeutic proteins are assembled using building block-like strategies, with no standardized schema to discuss these formats. Existing nomenclatures, like AbML, sacrifice human readability for precision. Therefore, considering even a dozen such formats, in combination with hundreds of possible targets, can create confusion and increase the complexity of drug discovery. To address this challenge, we introduce Verified Taxonomy for Antibodies (VERITAS). This classification and nomenclature scheme is extensible to multispecific therapeutic formats and beyond. VERITAS names are easy to understand while drawing direct connections to the structure of a given format, with or without specific target information, making these names useful to adopt in scientific discourse and as inputs to machine learning algorithms for drug development.
