Learning Correlations between Internal Coordinates to Improve 3D Cartesian Coordinates for Proteins.

ABSTRACT: We consider a generic representation problem of internal coordinates (bond lengths, valence angles, and dihedral angles) and their transformation to 3-dimensional Cartesian coordinates of a biomolecule. We show that the internal-to-Cartesian process relies on correctly predicting chemically subtle correlations among the internal coordinates themselves, and learning these correlations increases the fidelity of the Cartesian representation. We developed a machine learning algorithm, Int2Cart, to predict bond lengths and bond angles from backbone torsion angles and residue types of a protein, which allows reconstruction of protein structures better than using fixed bond lengths and bond angles or a static library method that relies on backbone torsion angles and residue types in a local envir

SUBMITTER: Li J 

PROVIDER: S-EPMC10404647 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature