Summary
Zero-shot fitness prediction performance with protein language models, but not structure-based models (e.g., inverse folding models), correlates with number of homologs available for training (1).
Figures
Ref (1)
See also
- ESM-IF, but not other inverse folding models, has learned some evolutionary constraints from sequence databases
- Inverse folding models favor archaeal proteins while sequence-only PLMs favor eukaryotic proteins
- Inverse folding selects more evolutionarily conserved design positions than sequence-only protein language models
1.
Li F-Z, Yang J, Johnston KE, Gürsoy E, Yue Y, Arnold FH. Evaluation of machine learning-assisted directed evolution across diverse combinatorial landscapes. Cell Systems. 2025;16(9):101387. Available from: https://doi.org/10.1016/j.cels.2025.101387