New publication by Dr. Lea Bogensperger, Prof. Michael Krauthammer, et al.
"Repurposing Protein Language Models for Latent Flow-Based Fitness Optimization". By Arroyo, A. C., Bogensperger, L., Allam, A., Krauthammer, M., Schindler, K. & Narnhofer, D. (2026)
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. Continue reading