RT Journal Article
SR Electronic
T1 Current protein structure predictors do not produce meaningful folding pathways
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.09.20.461137
DO 10.1101/2021.09.20.461137
A1 Carlos Outeiral
A1 Daniel A. Nissley
A1 Charlotte M. Deane
YR 2021
UL http://biorxiv.org/content/early/2021/09/20/2021.09.20.461137.abstract
AB Protein structure prediction has long been considered a gateway problem for understanding protein folding. Recent advances in deep learning have achieved unprecedented success at predicting a protein’s crystal structure, but whether this achievement relates to a better modelling of the folding process remains an open question. In this work, we compare the pathways generated by state-of-the-art protein structure prediction methods to experimental folding data. The methods considered were AlphaFold 2, RoseTTAFold, trRosetta, RaptorX, DMPfold, EVfold, SAINT2 and Rosetta. We find evidence that their simulated dynamics capture some information about the folding pathwhay, but their predictive ability is worse than a trivial classifier using sequence-agnostic features like chain length. The folding trajectories produced are also uncorrelated with parameters such as intermediate structures and the folding rate constant. These results suggest that recent advances in protein structure prediction do not yet provide an enhanced understanding of the principles underpinning protein folding.Competing Interest StatementThe authors have declared no competing interest.