Summary
Protein folding neural networks cannot predict stability. (1) found that ddG values did not correlate with pLDDT. (2) found by running Hamiltonian Replica-exchange molecular dynamics on high-pLDDT designs that subsequently fell apart. Papers cited by (3) showed that RMSD also does not correlate with stability, although (4) showed that a custom strain score was able to predict this with some success.
Figures
Ref (1)
See also
- High-pLDDT designs can be insoluble
- Protein structure prediction and design confidence metrics do not correlate with binding affinity
1.
Pak MA, Markhieva KA, Novikova MS, Petrov DS, Vorobyev IS, Maksimova ES, et al. Using AlphaFold to predict the impact of single mutations on protein stability and function. PLOS ONE. 2023;18(3):e0282689. Available from: https://doi.org/10.1371/journal.pone.0282689
2.
Aina A, Hsueh SCC, Gibbs E, Peng X, Cashman NR, Plotkin SS. De Novo Design of a β-Helix Tau Protein Scaffold: An Oligomer-Selective Vaccine Immunogen Candidate for Alzheimer’s Disease. ACS Chemical Neuroscience. 2023;14(15):2603–17. Available from: https://doi.org/10.1021/acschemneuro.3c00007
3.
Diaz DJ, Gong C, Ouyang-Zhang J, Loy JM, Wells J, Yang D, et al. Stability Oracle: A Structure-Based Graph-Transformer for Identifying Stabilizing Mutations. openRxiv; 2023. Available from: https://doi.org/10.1101/2023.05.15.540857
4.
McBride JM, Polev K, Abdirasulov A, Reinharz V, Grzybowski BA, Tlusty T. AlphaFold2 Can Predict Single-Mutation Effects. Physical Review Letters. 2023;131(21). Available from: https://doi.org/10.1103/physrevlett.131.218401