Efficient generation of epitope-targeted <i>de novo</i> 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 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, Germina

SUBMITTER: Mille-Fragoso LS 

PROVIDER: S-EPMC12485712 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature