Structure prediction of alternative protein conformations

Proteins are dynamic molecules whose movements result in different conformations with different functions. Neural networks such as AlphaFold2 can predict the structure of single-chain proteins in the conformations most likely to exist in the PDB. However, almost all conformations in the PDB are seen during training. Therefore, it is not possible to assess whether alternative protein conformations can be predicted or if these are reproduced from memory. Here, we train a new structure prediction network on a conformational split of the PDB to generate alternative conformations. Our network, Cfold, enables efficient exploration of the conformational landscape of monomeric protein structures. 52% (81) of the nonredundant alternative protein conformations evaluated here are predicted with high accuracy (TM-score>0.8). Cfold is freely available at: https://github.com/patrickbryant1/Cfold