Identifying native-like protein structures with scoring functions based on all-atom ECEPP force fields, implicit solvent models and structure relaxation

Abstract

Availability of energy functions which can discriminate native-like from non-native protein conformations is crucial for theoretical protein structure prediction and refinement of low-resolution protein models. This article reports the results of benchmark tests for scoring functions based on two all-atom ECEPP force fields, that is, ECEPP/3 and ECEPP05, and two implicit solvent models for a large set of protein decoys. The following three scoring functions are considered: (i) ECEPP05 plus a solvent-accessible surface area model with the parameters optimized with a set of protein decoys (ECEPP05/SA); (ii) ECEPP/3 plus the solvent-accessible surface area model of Ooi et al. (Proc Natl Acad Sci USA 1987;84:3086-3090) (ECEPP3/OONS); and (iii) ECEPP05 plus an implicit solvent model based on a solution of the Poisson equation with an optimized Fast Adaptive Multigrid Boundary Element (FAMBEpH) method (ECEPP05/FAMBEpH). Short Monte Carlo-with-Minimization (MCM) simulations, following local energy minimization, are used as a scoring method with ECEPP05/SA and ECEPP3/OONS potentials, whereas energy calculation is used with ECEPP05/FAMBEpH. The performance of each scoring function is evaluated by examining its ability to distinguish between native-like and non-native protein structures. The results of the tests show that the new ECEPP05/SA scoring function represents a significant improvement over the earlier ECEPP3/OONS version of the force field. Thus, it is able to rank native-like structures with C(alpha) root-mean-square-deviations below 3.5 A as lowest-energy conformations for 76% and within the top 10 for 87% of the proteins tested, compared with 69 and 80%, respectively, for ECEPP3/OONS. The use of the FAMBEpH solvation model, which provides a more accurate description of the protein-solvent interactions, improves the discriminative ability of the scoring function to 89%. All failed tests in which the native-like structures cannot be discriminated as those with low energy, are due to omission of protein-protein interactions. The results of this study represent a benchmark in force-field development, and may be useful for evaluation of the performance of different force fields.

Figure 1

Energy versus C^α rmsd from the native structure for the decoys obtained as a result of local energy minimization (red dots) followed by MCM search (the nonred dots).

Figure 2

Bar diagram of the occurrence frequency of the changes in the C^α rmsd from the native structure of each decoy caused by the MCM search. Δ_rmsd was computed as the difference between the C^α rmsd of the lowest energy decoy found during the MCM search and the energy-minimized starting conformation.

Figure 3

Rmsd of the lowest-energy decoy (black bars), the lowest rmsd decoy from the top 5 decoys selected by the total energy (white bars), the lowest rmsd decoy from the top 10 decoys selected by the total energy (grey bars): (a) ECEPP05/SA; (b) ECEPP3/OONS; (c) ECEPP05/FAMBEpH.

Figure 4

Overlay of the ECEPP05/SA lowest energy decoy (green) and the native structure (red) for (a) 1ail (rmsd = 3.77 Å) and (b) 1enh (rmsd = 2.74 Å).

Figure 5

Percentage of proteins with native-like decoys as stated on p.13 which have lowest-energy native-like structures with rmsd within a given range.

Figure 6

Experimental structure of 1hz6 (a) and the lowest energy decoy selected by the ECEPP05/SA scoring function (b).

Figure 7

The x-axis is the number of proteins for which the lowest C^α rmsd of five-selected decoys is at or below the C^α rmsd on the y-axis. The line labeled rmsd is the best-case scenario for selecting the lowest C^α rmsd decoy for each protein. Enrichment of the best decoys by C^α rmsd (solid line), total ECEPP05/SA (filled squares), ECEPP3/OONS (open triangles), and ECEPP05/FAMBEpH (open circles) energies.