Study Reveals Taxonomic Biases in Protein Design Models, Training Data Shapes Output
iskander · x · 2026-07-31
A computational biology study systematically characterizes hidden biases in state-of-the-art protein design models, revealing that training data fundamentally dictates systematic preferences.
- Structure-conditioned models like ProteinMPNN show minimal true taxonomic bias: after controlling for biophysical properties, species identity accounts for <3% of score variance, suggesting they learn general folding principles.
- Sequence-conditioned models may exhibit stronger biases.
- Overrepresentation of archaeal proteins in structural databases (due to thermostability) significantly shifts generated sequence properties.
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