GDFold2: A fast and parallelizable protein folding environment with freely defined objective functions.

ABSTRACT: An important step of mainstream protein structure prediction is to model the 3D protein structure based on the predicted 2D inter-residue geometric information. This folding step has been integrated into a unified neural network to allow end-to-end training in state-of-the-art methods like AlphaFold2, but is separately implemented using the Rosetta folding environment in some traditional methods like trRosetta. Despite the inferiority in prediction accuracy, the conventional approach allows for the sampling of various protein conformations compatible with the predicted geometric constraints, partially capturing the dynamic information. Here, we propose GDFold2, a novel protein folding environment, to address the limitations of Rosetta. On the one hand, GDFold2 is highly computationally eff

SUBMITTER: Mi T 

PROVIDER: S-EPMC11773392 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature