title: TMFoldRec: a statistical potential-based transmembrane protein fold recognition tool.
abstract: BACKGROUND
  Transmembrane proteins (TMPs) are the key components of signal transduction, cell-cell adhesion and energy and material transport into and out from the cells. For the deep understanding of these processes, structure determination of transmembrane proteins is indispensable. However, due to technical difficulties, only a few transmembrane protein structures have been determined experimentally. Large-scale genomic sequencing provides increasing amounts of sequence information on the proteins and whole proteomes of living organisms resulting in the challenge of bioinformatics; how the structural information should be gained from a sequence.
  
  
  RESULTS
  Here, we present a novel method, TMFoldRec, for fold prediction of membrane segments in transmembrane proteins. TMFoldRec based on statistical potentials was tested on a benchmark set containing 124 TMP chains from the PDBTM database. Using a 10-fold jackknife method, the native folds were correctly identified in 77% of the cases. This accuracy overcomes the state-of-the-art methods. In addition, a key feature of TMFoldRec algorithm is the ability to estimate the reliability of the prediction and to decide with an accuracy of 70%, whether the obtained, lowest energy structure is the native one.
  
  
  CONCLUSIONS
  These results imply that the membrane embedded parts of TMPs dictate the TM structures rather than the soluble parts. Moreover, predictions with reliability scores make in this way our algorithm applicable for proteome-wide analyses.
  
  
  BACKGROUND
  The program is available upon request for academic use.
authors.affiliation: "Momentum" Membrane Protein Bioinformatics Research Group, Institute of Enzymology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, PO Box 7, , H 1518, Budapest, Hungary. kozma.daniel@ttk.mta.hu.
authors.forename: Dániel
authors.initials: D
authors.lastname: Kozma
authors.affiliation: "Momentum" Membrane Protein Bioinformatics Research Group, Institute of Enzymology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, PO Box 7, , H 1518, Budapest, Hungary. tusnady.gabor@ttk.mta.hu.
authors.forename: Gábor E
authors.initials: GE
authors.lastname: Tusnády
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D006801:Humans; D008565:Membrane Proteins / Q000737:chemistry*; D015233:Models, Statistical; D011487:Protein Conformation; D017510:Protein Folding
chemical_list: D008565:Membrane Proteins