title: BPROMPT: A consensus server for membrane protein prediction.
abstract: Protein structure prediction is a cornerstone of bioinformatics research. Membrane proteins require their own prediction methods due to their intrinsically different composition. A variety of tools exist for topology prediction of membrane proteins, many of them available on the Internet. The server described in this paper, BPROMPT (Bayesian PRediction Of Membrane Protein Topology), uses a Bayesian Belief Network to combine the results of other prediction methods, providing a more accurate consensus prediction. Topology predictions with accuracies of 70% for prokaryotes and 53% for eukaryotes were achieved. BPROMPT can be accessed at http://www.jenner.ac.uk/BPROMPT.
authors.affiliation: Edward Jenner Institute for Vaccine Research, Bioinformatics, Compton, Berkshire, UK. paul.taylor@jenner.ac.uk
authors.forename: Paul D
authors.initials: PD
authors.lastname: Taylor
authors.forename: Teresa K
authors.initials: TK
authors.lastname: Attwood
authors.forename: Darren R
authors.initials: DR
authors.lastname: Flower
mesh_terms: D001499:Bayes Theorem; D005057:Eukaryotic Cells; D020407:Internet; D008565:Membrane Proteins / Q000737:chemistry*; D011387:Prokaryotic Cells; D017433:Protein Structure, Secondary; D015203:Reproducibility of Results; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software
chemical_list: D008565:Membrane Proteins