title: Combinatorial analysis for sequence and spatial motif discovery in short sequence fragments.
abstract: Motifs are overrepresented sequence or spatial patterns appearing in proteins. They often play important roles in maintaining protein stability and in facilitating protein function. When motifs are located in short sequence fragments, as in transmembrane domains that are only 6-20 residues in length, and when there is only very limited data, it is difficult to identify motifs. In this study, we introduce combinatorial models based on permutation for assessing statistically significant sequence and spatial patterns in short sequences. We show that our method can uncover previously unknown sequence and spatial motifs in beta-barrel membrane proteins and that our method outperforms existing methods in detecting statistically significant motifs in this data set. Last, we discuss implications of motif analysis for problems involving short sequences in other families of proteins.
authors.affiliation: Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110, USA. rjackups@path.wustl.edu
authors.forename: Ronald
authors.initials: R
authors.lastname: Jackups
authors.forename: Jie
authors.initials: J
authors.lastname: Liang
mesh_terms: D000465:Algorithms; D020816:Amino Acid Motifs; D000595:Amino Acid Sequence; D008958:Models, Molecular; D017510:Protein Folding; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D016415:Sequence Alignment / Q000379:methods; D020539:Sequence Analysis, Protein / Q000379:methods* / Q000706:statistics & numerical data
chemical_list: D011506:Proteins