The Unreasonable Redundancy of Nature's Protein Folds
2 hours ago
- #protein-folds
- #bioinformatics
- #deep-learning
- Deep learning models like AlphaFold3 are revolutionizing biomolecular interaction prediction and drug design, relying on scaling model size, compute, and data.
- Nature's protein folds are highly redundant, with vast sequence diversity not translating to comparable fold diversity, limiting the structural novelty from scaling sequence databases like MGnify.
- Predicted protein structures require advanced fragmentation (e.g., graph-theoretic spectral bisection) to separate compact domains from noise, ensuring training data quality.
- Clustering reveals that natural proteins concentrate in a small number of structural neighborhoods, with top clusters dominating mass, necessitating balanced sampling strategies for generative models.
- Enzyme design faces a choice between engineering known scaffolds or exploring novel backbone space, with models potentially inheriting natural redundancy despite increased data.