Directionality in protein fold prediction

Abstract

Background: Ever since the ground-breaking work of Anfinsen et al. in which a denatured protein was found to refold to its native state, it has been frequently stated by the protein fold prediction community that all the information required for protein folding lies in the amino acid sequence. Recent in vitro experiments and in silico computational studies, however, have shown that cotranslation may affect the folding pathway of some proteins, especially those of ancient folds. In this paper aspects of cotranslational folding have been incorporated into a protein structure prediction algorithm by adapting the Rosetta program to fold proteins as the nascent chain elongates. This makes it possible to conduct a pairwise comparison of folding accuracy, by comparing folds created sequentially from each end of the protein.

Results: A single main result emerged: in 94% of proteins analyzed, following the sense of translation, from N-terminus to C-terminus, produced better predictions than following the reverse sense of translation, from the C-terminus to N-terminus. Two secondary results emerged. First, this superiority of N-terminus to C-terminus folding was more marked for proteins showing stronger evidence of cotranslation and second, an algorithm following the sense of translation produced predictions comparable to, and occasionally better than, Rosetta.

Conclusions: There is a directionality effect in protein fold prediction. At present, prediction methods appear to be too noisy to take advantage of this effect; as techniques refine, it may be possible to draw benefit from a sequential approach to protein fold prediction.

Figure 1

Cotranslational structure prediction of the FLiG C-terminal domain (1qc7A; 101 residues). Segments of nine residues are extruded at a time except for the last segment which consists of two residues. One thousand decoys were produced; the particular simulation above produced the structure with the highest GDT_TS of 63.12%. In each sub-figure the N-terminal is coloured dark blue and appears at the center adopting approximately the same orientation; it cannot always be the same orientation due to changes in conformation as the protein folds.

Figure 2

Plots of mean and maximum GDT_TS for the positive set. Graphic A shows the mean GDT_TS scores for the 34 proteins in the positive set, for SAINT (red squares), reverse SAINT (blue circles) and Rosetta (green triangles), with the proteins ordered according to ascending mean SAINT GDT_TS. SAINT and Rosetta perform similarly and consistently better than reverse SAINT. Graphic B plots maximum GDT_TS in the same way, ordered this time by ascending maximum SAINT GDT_TS, revealing greater variation but still a consistent and generally larger improvement of SAINT on reverse SAINT.

Figure 3

Plots of mean and maximum GDT_TS for the negative set. Graphic A shows the mean GDT_TS scores for the 34 proteins in the negative set, for SAINT (red squares), reverse SAINT (blue circles) and Rosetta (green triangles), with the proteins ordered according to ascending mean SAINT GDT_TS. Graphic B plots maximum GDT_TS for proteins in the negative set, ordered by ascending maximum SAINT GDT_TS. Outcomes are the same as for the positive set, with all differences less marked.

Figure 4

Superpositions of the best predictions for 3vubA on the native structure. The best decoy produced overall was by SAINT for 3vubA, whose native conformation is shown in a). The remaining graphics show the superposition of this native conformation with the best decoy produced by b) SAINT (GDT_TS = 67.57), c) reverse SAINT (GDT_TS =37.62) and d) Rosetta (GDT_TS = 51.24). The SAINT decoy best captures the native loop and sheet conformation; a loop error causes the C-terminal helix to be incorrectly oriented.

Figure 5

Accuracy of helix and strand predictions. Accuracy of helix and strand predictions separately for (A) positive and (B) negative sets. Plots show the difference (reverse SAINT minus SAINT) in the secondary structure distance measure for helical (grey) and strand (black) residues. Positive values here indicate that SAINT is producing predictions that are more accurate than those of reverse SAINT. Evidently SAINT outperforms reverse SAINT for both types of secondary structure, but more strongly for strands and the negative set.