title: Fold recognition via a tree.
abstract: Recently, we developed a pairwise structural alignment algorithm using realistic structural and environmental information (SAUCE). In this paper, we at first present an automatic fold hierarchical classification based on SAUCE alignments. This classification enables us to build a fold tree containing different levels of multiple structural profiles. Then a tree-based fold search algorithm is described. We applied this method to a group of structures with sequence identity less than 35% and did a series of leave one out tests. These tests are approximately comparable to fold recognition tests on superfamily level. Results show that fold recognition via a fold tree can be faster and better at detecting distant homologues than classic fold recognition methods.
authors.affiliation: Bioinformatics Program, University of Michigan, Ann Arbor, Michigan 48109-1065, USA.
authors.forename: Yu
authors.initials: Y
authors.lastname: Chen
authors.forename: Gordon M
authors.initials: GM
authors.lastname: Crippen
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001699:Biometry; D030562:Databases, Protein; D008958:Models, Molecular; D015233:Models, Statistical; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry* / Q000235:genetics; D040901:Proteomics / Q000706:statistics & numerical data; D016415:Sequence Alignment / Q000706:statistics & numerical data*
chemical_list: D011506:Proteins