Guide your favorite protein sequence generative model

• URL: http://arxiv.org/abs/2505.04823v3
• Date: Mon, 28 Jul 2025 04:57:58 GMT
• Title: Guide your favorite protein sequence generative model
• Authors: Junhao Xiong, Hunter Nisonoff, Maria Lukarska, Ishan Gaur, Luke M. Oltrogge, David F. Savage, Jennifer Listgarten,
• Abstract summary: We present ProteinGuide, a principled and general method for conditioning protein generative models.<n>We demonstrate the applicability of ProteinGuide by guiding two protein generative models, ProteinMPNN and ESM3, to generate amino acid and structure token sequences.<n>We also used ProteinGuide with inverse folding models and our own experimental assay to design adenine base editor sequences for high activity.
• Score: 1.5914835340090132
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative machine learning models on sequences are transforming protein engineering. However, no principled framework exists for conditioning these models on auxiliary information, such as experimental data, in a plug-and-play manner. Herein, we present ProteinGuide -- a principled and general method for conditioning -- by unifying a broad class of protein generative models under a single framework. We demonstrate the applicability of ProteinGuide by guiding two protein generative models, ProteinMPNN and ESM3, to generate amino acid and structure token sequences, conditioned on several user-specified properties such as enhanced stability, enzyme classes, and CATH-labeled folds. We also used ProteinGuide with inverse folding models and our own experimental assay to design adenine base editor sequences for high activity.

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