title: Improving fragment quality for de novo structure prediction.
abstract: De novo structure prediction can be defined as a search in conformational space under the guidance of an energy function. The most successful de novo structure prediction methods, such as Rosetta, assemble the fragments from known structures to reduce the search space. Therefore, the fragment quality is an important factor in structure prediction. In our study, a method is proposed to generate a new set of fragments from the lowest energy de novo models. These fragments were subsequently used to predict the next-round of models. In a benchmark of 30 proteins, the new set of fragments showed better performance when used to predict de novo structures. The lowest energy model predicted using our method was closer to native structure than Rosetta for 22 proteins. Following a similar trend, the best model among top five lowest energy models predicted using our method was closer to native structure than Rosetta for 20 proteins. In addition, our experiment showed that the C-alpha root mean square deviation was improved from 5.99 to 5.03 Å on average compared to Rosetta when the lowest energy models were picked as the best predicted models.
authors.affiliation: Zhang Initiative Research Unit, Institute Laboratories, RIKEN, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan; Department of Computational Biology, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-0882, Japan.
authors.forename: Rojan
authors.initials: R
authors.lastname: Shrestha
authors.forename: Kam Y J
authors.initials: KY
authors.lastname: Zhang
mesh_terms: D000595:Amino Acid Sequence; D003198:Computer Simulation; D008958:Models, Molecular; D010446:Peptide Fragments / Q000378:metabolism*; D017510:Protein Folding; D017433:Protein Structure, Secondary; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000378:metabolism*
chemical_list: D010446:Peptide Fragments; D011506:Proteins
keywords: fragment assembly; fragment clustering; improved fragments; protein structure prediction; structure-based fragments