Efficient generation of epitope-targeted de novo antibodies with Germinal

Abstract Obtaining novel antibodies against specific protein targets is a widely important yet experimentally laborious process. Meanwhile, computational methods for antibody design have been limited by low success rates that currently require resource-intensive screening. Here, we introduce Germinal, a broadly enabling generative pipeline that designs antibodies against specific epitopes with nanomolar binding affinities while requiring only low-n experimental testing. Our method co-optimizes antibody structure and sequence by integrating a structure predictor with an antibody-specific protein language model to perform de novo design of functional complementarity-determining regions (CDRs) onto a user-specified structural framework. When tested against four diverse protein targets, Germinal successfully designed functional antibodies across all targets and binder formats, testing only 43-101 designs for each antigen. Validated designs also exhibited robust expression in mammalian cells and high sequence and structural novelty. We provide open-source code and full computational and experimental protocols to facilitate wide adoption. Germinal represents a milestone in efficient, epitope-targeted de novo antibody design, with notable implications for the development of molecular tools and therapeutics. Competing Interest Statement B.L.H. acknowledges outside interest in Arpelos Biosciences and Genyro as a scientific co-founder. X.J.G. is a cofounder and serves on the scientific advisory board of Radar Tx. L.S.M, J.N.W., C.L.D., H.D., T.W., X.Z., B.L.H, and X.J.G are named on a provisional patent application applied for by Stanford University and Arc Institute related to this manuscript Footnotes ↵Δ These authors agreed to list themselves first in bibliographic entries. This revision adds new results including scFv data for IL3 and PDL1, polyreactivity analysis, alanine mutagenesis across all targets, and cryo EM characterization.