Authors
Junhao Xiong, Ishan Gaur, Maria Lukarska, Hunter Nisonoff, Luke M Oltrogge, David F Savage, Jennifer Listgarten
Published in
Nature biotechnology. Jul 29, 2026. Epub Jul 29, 2026.
Abstract
No principled framework exists for conditioning sequence generative models for protein engineering on auxiliary information, such as experimental data, without additional training of a generative model. Here we present ProteinGuide, a method for such 'on-the-fly' conditioning. ProteinGuide is amenable to a broad class of protein generative models including masked language models such as ESM3, any-order autoregressive models such as ProteinMPNN and diffusion and flow-matching models on discrete state-spaces such as MultiFlow. ProteinGuide stems from a unifying statistical framework for these model classes. As proof of principle, pretrained generative models are used to design proteins with user-specified properties, such as higher stability or activity. Proteins are additionally designed to optimize for two desired properties that are in tension with each other. Lastly, we apply ProteinGuide jointly with wet-lab data generation to increase the editing activity of an adenine base editor in vivo, resulting in a base editor with higher editing efficiency than was previously achieved using seven rounds of directed evolution.
PMID:
42527525
Bibliographic data and abstract were imported from PubMed on 30 Jul 2026.
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