Computational methods for high resolution prediction and refinement of protein structures

We review advances in implicit solvation and sampling algorithms which have resulted in enhanced capabilities in predicting and refining localized protein structures (e.g. loop regions) to high resolution. Improvements in the generalized Born model and hydrophobicity term yield significantly more accurate energetics; specialized sampling algorithms allow complex local structures, such as a loop–helix–loop region, to be reliably predicted. A novel penalty term is added for loops containing patterns of dihedrals seldom found in experimental structures. We show prediction of diverse sets of large loops, in the native backbone environment, to subångström accuracy. The methodology offers the promise of addressing the refinement problem in homology modeling if an approach can be devised to handle delocalized errors in the structure.

► Effective polarization can be added to GB solvation by a variable dielectric model. ► An improved hydrophobicity term can be derived from ligand docking calculations. ► Secondary structure prediction permits buildup with helical dihedral libraries. ► A penalty term from empirically observed dihedral patterns detects non-native loops. ► These advances led to subångström predictions of loops in non-native environments.