title: Efficient identification of near-native conformations in ab initio protein structure prediction using structural profiles.
abstract: One of the major bottlenecks in many ab initio protein structure prediction methods is currently the selection of a small number of candidate structures for high-resolution refinement from large sets of low-resolution decoys. This step often includes a scoring by low-resolution energy functions and a clustering of conformations by their pairwise root mean square deviations (RMSDs). As an efficient selection is crucial to reduce the overall computational cost of the predictions, any improvement in this direction can increase the overall performance of the predictions and the range of protein structures that can be predicted. We show here that the use of structural profiles, which can be predicted with good accuracy from the amino acid sequences of proteins, provides an efficient means to identify good candidate structures.
authors.affiliation: Institut für Festkörperphysik, Technische Universität Darmstadt, 64289 Darmstadt, Germany.
authors.forename: Katrin
authors.initials: K
authors.lastname: Wolff
authors.forename: Michele
authors.initials: M
authors.lastname: Vendruscolo
authors.forename: Markus
authors.initials: M
authors.lastname: Porto
mesh_terms: D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D016571:Neural Networks, Computer; D011487:Protein Conformation; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry*
chemical_list: D011506:Proteins