Contact

  • Academic
    dhy@stanford.edu
    University - Student Department: Chemical Engineering Position: Graduate
    University - Student Department: Chemical Engineering Position: Graduate

Additional Info

  • Mail Code: 4300

All Publications

  • Efficient generation of epitope-targetedde novoantibodies with Germinal. bioRxiv : the preprint server for biology Mille-Fragoso, L. S., Wang, J. N., Driscoll, C. L., Dai, H., Widatalla, T., Zhang, X., Hie, B. L., Gao, X. J. 2025

    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 framework 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 achieved an experimental success rate of 4-22% across all targets, testing only 43-101 designs for each antigen. Validated nanobodies also exhibited robust expression in mammalian cells and nanomolar binding affinities. 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.

    View details for DOI 10.1101/2025.09.19.677421

    View details for PubMedID 41040335