title: A model for statistical significance of local similarities in structure.
abstract: Structural biology can provide three-dimensional structures for proteins of unknown function. When sequence or structure comparisons fail to suggest a function, insights can come from discovery of functionally important local structural patterns. Existing methods to detect such patterns lack rigorous statistics needed for widespread application. Here, we derive a formula to calculate statistical significance of the root-mean-square deviation between atoms in such patterns. When combined with a database search method, our statistics permit true functional or structural patterns in different folds to be discerned from noise. The approach is highly complementary to fold comparison for providing functional clues for new structures, and is key for the detection of recurrences of any new pattern.
authors.affiliation: EMBL, Structural & Computational Biology Programme, Meyerhofstrasse 1, 69117, Heidelberg, Germany.
authors.forename: Alexander
authors.initials: A
authors.lastname: Stark
authors.forename: Shamil
authors.initials: S
authors.lastname: Sunyaev
authors.forename: Robert B
authors.initials: RB
authors.lastname: Russell
mesh_terms: D000465:Algorithms; D000818:Animals; D001665:Binding Sites; D016208:Databases, Factual; D006801:Humans; D008958:Models, Molecular; D011487:Protein Conformation; D017510:Protein Folding; D020836:Protein Structure, Quaternary; D011506:Proteins / Q000737:chemistry* / Q000235:genetics; D016415:Sequence Alignment; D017421:Sequence Analysis; D040681:Structural Homology, Protein
chemical_list: D011506:Proteins