title: Computational Classification of P-Type ATPases.
abstract: Analysis of sequence data is inevitable in modern molecular biology, and important information about for example proteins can be inferred efficiently using computational methods. Here, we explain how to use the information in freely available databases together with computational methods for classification and motif detection to assess whether a protein sequence corresponds to a P-type ATPase (and if so, which subtype) or not.
authors.affiliation: Bioinformatics Research Centre (BiRC), Aarhus University, C. F. Møllers Allé 8, DK-8000, Aarhus C, Denmark.|Centre for Membrane Pumps in Cells and Disease-PUMPKIN, Danish National Research Foundation, Aarhus C, Denmark.
authors.forename: Dan
authors.initials: D
authors.lastname: Søndergaard
authors.affiliation: Bioinformatics Research Centre (BiRC), Aarhus University, C. F. Møllers Allé 8, DK-8000, Aarhus C, Denmark.
authors.forename: Michael
authors.initials: M
authors.lastname: Knudsen
authors.affiliation: Bioinformatics Research Centre (BiRC), Aarhus University, C. F. Møllers Allé 8, DK-8000, Aarhus C, Denmark. cstorm@birc.au.dk.|Centre for Membrane Pumps in Cells and Disease-PUMPKIN, Danish National Research Foundation, Aarhus C, Denmark. cstorm@birc.au.dk.
authors.forename: Christian Nørgaard Storm
authors.initials: CN
authors.lastname: Pedersen
mesh_terms: D000251:Adenosine Triphosphatases / Q000737:chemistry / Q000145:classification*; D000595:Amino Acid Sequence; D000818:Animals; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D006801:Humans; D051379:Mice
chemical_list: D000251:Adenosine Triphosphatases
keywords: Classification; Databases; Motif detection; P-type ATPase s; Protein sequences