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.

Composite motifs integrating multiple protein structures increase sensitivity for function prediction.

The study of disease often hinges on the biological function of proteins, but determining protein function is a difficult experimental process. To minimize duplicated effort, algorithms for function prediction seek characteristics indicative of possible protein function. One approach is to identify substructural matches of geometric and chemical similarity between motifs representing known active sites and target protein structures with unknown function. In earlier work, statistically significant matches of certain effective motifs have identified functionally related active sites. Effective motifs must be carefully designed to maintain similarity to functionally related sites (sensitivity) and avoid incidental similarities to functionally unrelated protein geometry (specificity). Existing motif design techniques use the geometry of a single protein structure. Poor selection of this structure can limit motif effectiveness if the selected functional site lacks similarity to functionally related sites. To address this problem, this paper presents composite motifs, which combine structures of functionally related active sites to potentially increase sensitivity. Our experimentation compares the effectiveness of composite motifs with simple motifs designed from single protein structures. On six distinct families of functionally related proteins, leave-one-out testing showed that composite motifs had sensitivity comparable to the most sensitive of all simple motifs and specificity comparable to the average simple motif. On our data set, we observed that composite motifs simultaneously capture variations in active site conformation, diminish the problem of selecting motif structures, and enable the fusion of protein structures from diverse data sources.