title: A method for alpha-helical integral membrane protein fold prediction.
abstract: Integral membrane proteins (of the alpha-helical class) are of central importance in a wide variety of vital cellular functions. Despite considerable effort on methods to predict the location of the helices, little attention has been directed toward developing an automatic method to pack the helices together. In principle, the prediction of membrane proteins should be easier than the prediction of globular proteins: there is only one type of secondary structure and all helices pack with a common alignment across the membrane. This allows all possible structures to be represented on a simple lattice and exhaustively enumerated. Prediction success lies not in generating many possible folds but in recognizing which corresponds to the native. Our evaluation of each fold is based on how well the exposed surface predicted from a multiple sequence alignment fits its allocated position. Just as exposure to solvent in globular proteins can be predicted from sequence variation, so exposure to lipid can be recognized by variable-hydrophobic (variphobic) positions. Application to both bacteriorhodopsin and the eukaryotic rhodopsin/opsin families revealed that the angular size of the lipid-exposed faces must be predicted accurately to allow selection of the correct fold. With the inherent uncertainties in helix prediction and parameter choice, this accuracy could not be guaranteed but the correct fold was typically found in the top six candidates. Our method provides the first completely automatic method that can proceed from a scan of the protein sequence databanks to a predicted three-dimensional structure with no intervention required from the investigator. Within the limited domain of the seven helix bundle proteins, a good chance can be given of selecting the correct structure. However, the limited number of sequences available with a corresponding known structure makes further characterization of the method difficult.
authors.affiliation: Laboratory of Mathematical Biology, National Institute for Medical Research, London, U.K.
authors.forename: W R
authors.initials: WR
authors.lastname: Taylor
authors.forename: D T
authors.initials: DT
authors.lastname: Jones
authors.forename: N M
authors.initials: NM
authors.lastname: Green
mesh_terms: D000595:Amino Acid Sequence; D001436:Bacteriorhodopsins / Q000737:chemistry*; D017124:Conserved Sequence; D008565:Membrane Proteins / Q000737:chemistry*; D008956:Models, Chemical; D008958:Models, Molecular; D008969:Molecular Sequence Data; D017510:Protein Folding; D017433:Protein Structure, Secondary; D012243:Rhodopsin / Q000737:chemistry*; D017386:Sequence Homology, Amino Acid
chemical_list: D008565:Membrane Proteins; D001436:Bacteriorhodopsins; D012243:Rhodopsin