Protein structure prediction by emphasizing local side-chain/backbone and side-chain/side-chain interactions.

摘要

Based on the hypothesis that local interactions play an important role in specifying long-range structures, a consistent set of statistical potentials containing 4 energy terms for quantifying local side-chain and backbone interactions are calculated from high-resolution crystal structures. The combined energy function, containing no interactions between atoms more than four residues apart, identifies the correct structural fragment for randomly selected 15 mers over 40% of the time, after searching through 232,000 alternative conformations. For 14 out of 20 sets of all-atom Rosetta decoys analyzed, the native structure has a combined score lower than any of the 17001900 decoy conformations. The ability of this local energy function to detect energetically important details of local structure is demonstrated by its power to distinguish high-resolution crystal structures from NMR solution structures. In addition, protein fragments are generated de novo by recombining very short segments of protein structures (2, 4 or 6 residues), either selected at random or optimized with respect to this local energy function. When local energy is optimized in selected fragments, more efficient sampling of conformational space near the native conformation is consistently observed on average for 450 randomly selected single turn-fragments, with turn lengths varying from 3 to 12 residues and all four combinations of flanking secondary structure. These results further demonstrate the energetic significance of local interactions in protein conformations.; To predict protein structure at high resolution, a statistical potential representing all pairs of atomic interactions in the protein is calculated and optimized for its ability to discriminate native structures from Rosetta decoys. This all-atom potential is then incorporated into the fitness function of a Genetic Algorithm program. Tests on the program show that four small proteins with ensemble of structures in which every native phi/psi angle has been randomly changed by either plus or minus 3, 5 or 10 degrees are able to "refold". These results suggest that this all-atom potential can be used in high-resolution structure prediction and that Genetic Algorithms provide an effective method for conformational search in refining protein models.