title: Protein structure prediction by all-atom free-energy refinement.
abstract: BACKGROUND
  The reliable prediction of protein tertiary structure from the amino acid sequence remains challenging even for small proteins. We have developed an all-atom free-energy protein forcefield (PFF01) that we could use to fold several small proteins from completely extended conformations. Because the computational cost of de-novo folding studies rises steeply with system size, this approach is unsuitable for structure prediction purposes. We therefore investigate here a low-cost free-energy relaxation protocol for protein structure prediction that combines heuristic methods for model generation with all-atom free-energy relaxation in PFF01.
  
  
  RESULTS
  We use PFF01 to rank and cluster the conformations for 32 proteins generated by ROSETTA. For 22/10 high-quality/low quality decoy sets we select near-native conformations with an average Calpha root mean square deviation of 3.03 A/6.04 A. The protocol incorporates an inherent reliability indicator that succeeds for 78% of the decoy sets. In over 90% of these cases near-native conformations are selected from the decoy set. This success rate is rationalized by the quality of the decoys and the selectivity of the PFF01 forcefield, which ranks near-native conformations an average 3.06 standard deviations below that of the relaxed decoys (Z-score).
  
  
  CONCLUSIONS
  All-atom free-energy relaxation with PFF01 emerges as a powerful low-cost approach toward generic de-novo protein structure prediction. The approach can be applied to large all-atom decoy sets of any origin and requires no preexisting structural information to identify the native conformation. The study provides evidence that a large class of proteins may be foldable by PFF01.
authors: Institute for Scientific Computing, Forschungszentrum Karlsruhe, Karlsruhe, Germany. verma@int.fzk.de <verma@int.fzk.de>
authors: Abhinav
authors: A
authors: Verma
authors: Wolfgang
authors: W
authors: Wenzel
mesh_terms: D019295:Computational Biology / Q000379:methods*; D019992:Databases as Topic; D004734:Energy Metabolism; D029968:Escherichia coli Proteins / Q000737:chemistry; D011237:Predictive Value of Tests; D011487:Protein Conformation; D017510:Protein Folding; D012269:Ribosomal Proteins / Q000737:chemistry*
chemical_list: D029968:Escherichia coli Proteins; D012269:Ribosomal Proteins