arXiv:2602.06020v3 Announce Type: replace Abstract: How do protein structure prediction models fold proteins? We investigate this question through causal interventions on the folding trunks of ESMFold, OpenFold, and Boltz-1. Across all three models, we find a shared two-stage computational structure. In the first stage, early blocks initialize pairwise biochemical signals: features like charge propagate from sequence into pairwise representations through architecture-specific pathways. In the second stage, late blocks develop pairwise spatial features: distance and contact information accumula
The accelerating pace of AI development in protein folding and the public release of powerful models like AlphaFold and ESMFold are driving increased research into their underlying mechanisms.
Understanding the computational mechanisms of leading AI protein folding models can guide future advancements in drug discovery, materials science, and synthetic biology, potentially unlocking new therapeutic and industrial applications.
This research provides deeper insight into how current AI models achieve protein folding, which can lead to more robust, efficient, and explainable AI systems for biological design.
- · Synthetic Biology sector
- · Pharmaceutical R&D
- · AI/ML researchers
- · Biotechnology companies
- · Traditional protein folding methods
Improved interpretability and reliability of AI models in protein design.
Faster discovery and development of novel proteins for medicine, materials, and energy.
New classes of self-assembling, programmable biological systems with unprecedented functionality.
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Read at arXiv cs.LG