Advancing Knotted Protein Design with ESM3: Guided Generation and Topological Insights

Multimodal protein language models have transformed protein design, yet their capacity to capture complex topological features remains poorly understood. We use knotted proteins, rare structures in which the backbone forms a nontrivial topological knot, as a test case to probe this capacity using ESM3, a generative protein language model. ESM3’s guided generation produces knotted proteins with an 89% success rate (95% CI: 81–94%), compared to ∼ 0.5% for unguided diffusion-based approaches. Knot topology is remarkably robust to sequence perturbation: on average 84% of the protein sequence must be altered before the knot breaks, and the loss follows a sharp threshold rather than gradual degradation. Strikingly, structural drift accumulates well before topological disruption, suggesting that topology is more robust than specific three-dimensional arrangement. Generated proteins show no close sequence similarity to known knotted proteins, arguing against simple memorization. These findings have implications for protein engineering and, more speculatively, for discussions of biosecurity in the era of generative biological AI.