Obtaining protein foldability information from computational models of AlphaFold2 and RoseTTAFold

Abstract

The recent breakthrough from AlphaFold2 and RoseTTAFold set a profound milestone for solving the protein folding problem, but they were not explicitly trained to predict protein foldability, i.e., if a protein can really fold into the predicted 3D structure. We wondered if the computational models from AlphaFold2 and RoseTTAFold might carry protein foldability information. Therefore, we predicted the structural models of 159 circular permutants and 158 alanine insertion mutants of the 159-residue dihydrofolate reductase. Our data showed that although AlphaFold2 and RoseTTAFold cannot directly identify unfoldable proteins, the RMSD values of computational models are correlated with protein foldability, with higher RMSD values indicating lower protein foldability. Furthermore, this correlation is independent of secondary structures, and the RMSD values of computational models are quantitatively correlated with protein foldability but not protein functions. Additionally, using a dataset of 129 de novo designed proteins, we showed that inter-model RMSD values between AlphaFold2 models and RoseTTAFold models are a good indicator of protein foldability. At last, we showed that inter-model RMSD values are also useful for evaluating protein solubility by modeling 1664 natural proteins. Our work could be of great value to the design of novel proteins and the prediction of protein foldability.