Prediction of Long Loops with Embedded Secondary Structure using the Protein Local Optimization Program.

Robust homology modeling to atomic-level accuracy requires in the general case successful prediction of protein loops containing small segments of secondary structure. Further, as loop prediction advances to success with larger loops, the exclusion of loops containing secondary structure becomes awkward. Here, we extend the applicability of the Protein Local Optimization Program (PLOP) to loops up to 17 residues in length that contain either helical or hairpin segments. In general, PLOP hierarchically samples conformational space and ranks candidate loops with a high-quality molecular mechanics force field. For loops identified to possess α-helical segments, we employ an alternative dihedral library composed of (ϕ,ψ) angles commonly found in helices. The alternative library is searched over a user-specified range of residues that define the helical bounds. The source of these helical bounds can be from popular secondary structure prediction software or from analysis of past loop predictions where a propensity to form a helix is observed. Due to the maturity of our energy model, the lowest energy loop across all experiments can be selected with an accuracy of sub-Ångström RMSD in 80% of cases, 1.0 to 1.5 Å RMSD in 14% of cases, and poorer than 1.5 Å RMSD in 6% of cases. The effectiveness of our current methods in predicting hairpin-containing loops is explored with hairpins up to 13 residues in length and again reaching an accuracy of sub-Ångström RMSD in 83% of cases, 1.0 to 1.5 Å RMSD in 10% of cases, and poorer than 1.5 Å RMSD in 7% of cases. Finally, we explore the effect of an imprecise surrounding environment, in which side chains, but not the backbone, are initially in perturbed geometries. In these cases, loops perturbed to 3Å RMSD from the native environment were restored to their native conformation with sub-Ångström RMSD.

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.