title: Online tools for predicting integral membrane proteins.
abstract: We identify and describe a set of tools readily available for integral membrane protein prediction. These tools address two problems: finding potential transmembrane proteins in a pool of new sequences, and identifying their transmembrane regions. All methods involve comparing the query protein against one or more target models. In the simplest of these, the target "model" is another protein sequence, while the more elaborate methods group together the entire set of t ansmembrane helical or transmembrane beta-barrel proteins. In general, prediction accuracy either in identifying new integral membrane proteins or transmembrane regions of known integral membrane proteins depends strongly on how closely the query fits the model. Because of this, the best approach is an opportunistic one: submit the protein of interest to all methods and choose the results with the highest confidence scores.
authors.affiliation: Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, USA.
authors.forename: Henry
authors.initials: H
authors.lastname: Bigelow
authors.forename: Burkhard
authors.initials: B
authors.lastname: Rost
mesh_terms: D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D030562:Databases, Protein; D008565:Membrane Proteins / Q000737:chemistry*; D008958:Models, Molecular; D017433:Protein Structure, Secondary; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software; D040681:Structural Homology, Protein
chemical_list: D008565:Membrane Proteins