ProteinGuide: On-the-fly property guidance for protein sequence generative models
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866915733376598016 |
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| author | Xiong, Junhao Gaur, Ishan Lukarska, Maria Nisonoff, Hunter Oltrogge, Luke M. Savage, David F. Listgarten, Jennifer |
| author_facet | Xiong, Junhao Gaur, Ishan Lukarska, Maria Nisonoff, Hunter Oltrogge, Luke M. Savage, David F. Listgarten, Jennifer |
| contents | Sequence generative models are transforming protein engineering. However, no principled framework exists for conditioning these models on auxiliary information, such as experimental data, without additional training of a generative model. Herein, we present ProteinGuide, a method for such "on-the-fly" conditioning, amenable to a broad class of protein generative models including Masked Language Models (e.g. ESM3), any-order auto-regressive models (e.g. ProteinMPNN) as well as diffusion and flow matching models (e.g. MultiFlow). ProteinGuide stems from our unifying view of these model classes under a single statistical framework. As proof of principle, we perform several in silico experiments. We first guide pre-trained generative models to design proteins with user-specified properties, such as higher stability or activity. Next, we design for optimizing two desired properties that are in tension with each other. Finally, we apply our method in the wet lab, using ProteinGuide to increase the editing activity of an adenine base editor in vivo with data from only a single pooled library of 2,000 variants. We find that a single round of ProteinGuide achieves a higher editing efficiency than was previously achieved using seven rounds of directed evolution. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_04823 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ProteinGuide: On-the-fly property guidance for protein sequence generative models Xiong, Junhao Gaur, Ishan Lukarska, Maria Nisonoff, Hunter Oltrogge, Luke M. Savage, David F. Listgarten, Jennifer Machine Learning Biomolecules Sequence generative models are transforming protein engineering. However, no principled framework exists for conditioning these models on auxiliary information, such as experimental data, without additional training of a generative model. Herein, we present ProteinGuide, a method for such "on-the-fly" conditioning, amenable to a broad class of protein generative models including Masked Language Models (e.g. ESM3), any-order auto-regressive models (e.g. ProteinMPNN) as well as diffusion and flow matching models (e.g. MultiFlow). ProteinGuide stems from our unifying view of these model classes under a single statistical framework. As proof of principle, we perform several in silico experiments. We first guide pre-trained generative models to design proteins with user-specified properties, such as higher stability or activity. Next, we design for optimizing two desired properties that are in tension with each other. Finally, we apply our method in the wet lab, using ProteinGuide to increase the editing activity of an adenine base editor in vivo with data from only a single pooled library of 2,000 variants. We find that a single round of ProteinGuide achieves a higher editing efficiency than was previously achieved using seven rounds of directed evolution. |
| title | ProteinGuide: On-the-fly property guidance for protein sequence generative models |
| topic | Machine Learning Biomolecules |
| url | https://arxiv.org/abs/2505.04823 |