Fold Recognition using the OPLS All-Atom Potential and the Surface Generalized Born Solvent Model

Protein decoy data sets provide a benchmark for testing scoring functions designed for fold recognition and protein homology modeling problems. It is commonly believed that statistical potentials based on reduced atomic models are better able to discriminate native-like from misfolded decoys than scoring functions based on more detailed molecular mechanics models. Recent benchmark tests, however, suggest otherwise. Further analysis of the effectiveness of all atom molecular mechanics scoring functions for detecting misfolded decoys and direct comparison with results obtained using a statistical potential derived for a reduced atomic model are presented in this report. The OPLS all-atom force field is used as a scoring function to detect native protein folds among the Park & Levitt large decoy sets. Solvent electrostatic effects are included through the Surface Generalized Born (SGB) model. The OPLS potential with SGB solvation (OPLS-AA/SGB) provides good discrimination between native-like structures and non-native decoys. From an analysis of the individual energy components of the OPLS-AA/SGB potential for the native and the best-ranked decoy, it is determined that a roughly even balance of the terms of the potential is responsible for distinguishing the native from the misfolded conformations. Different combinations of individual energy terms provide less discrimination than the total energy. The effects of scoring decoys using several dielectric models are compared also. With the SGB solvation model, close to 100% of the structures with energies within 100 kcal/mol of the native state minimum are native-like. In contrast, only 20% of the low energy structures are found to be native-like when a distance dependent dielectric is used instead of SGB to model solvent electrostatic effects. The results are consistent with observations that all-atom molecular potentials coupled with intermediate level solvent dielectric models are competitive with knowledge-based potentials for decoy detection and protein modeling problems such as fold recognition.