  Determining the 3D structures of proteins is essential in understanding their
behavior in the cellular environment. Computational methods of predicting
protein structures have advanced, but assessing prediction accuracy remains a
challenge. The traditional method, RMSD, relies on experimentally determined
structures and lacks insight into improvement areas of predictions. We propose
an alternative: analyzing dihedral angles, bypassing the need for the reference
structure of an evaluated protein. Our method segments proteins into amino acid
subsequences and searches for matches, comparing dihedral angles across
numerous proteins to compute a metric using Mahalanobis distance. Evaluated on
many predictions, our approach correlates with RMSD and identifies areas for
prediction enhancement. This method offers a promising route for accurate
protein structure prediction assessment and improvement.
