Repurposing Protein Language Models for Latent Flow-Based Fitness Optimization

• URL: http://arxiv.org/abs/2602.02425v1
• Date: Mon, 02 Feb 2026 18:25:33 GMT
• Title: Repurposing Protein Language Models for Latent Flow-Based Fitness Optimization
• Authors: Amaru Caceres Arroyo, Lea Bogensperger, Ahmed Allam, Michael Krauthammer, Konrad Schindler, Dominik Narnhofer,
• Abstract summary: CHASE is a framework that repurposes the evolutionary knowledge of pretrained protein language models.<n>It achieves state-of-the-art performance on AAV and GFP protein design benchmarks.
• Score: 24.267946140577806
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Protein fitness optimization is challenged by a vast combinatorial landscape where high-fitness variants are extremely sparse. Many current methods either underperform or require computationally expensive gradient-based sampling. We present CHASE, a framework that repurposes the evolutionary knowledge of pretrained protein language models by compressing their embeddings into a compact latent space. By training a conditional flow-matching model with classifier-free guidance, we enable the direct generation of high-fitness variants without predictor-based guidance during the ODE sampling steps. CHASE achieves state-of-the-art performance on AAV and GFP protein design benchmarks. Finally, we show that bootstrapping with synthetic data can further enhance performance in data-constrained settings.

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