title: TMB-Hunt: an amino acid composition based method to screen proteomes for beta-barrel transmembrane proteins.
abstract: BACKGROUND
  Beta-barrel transmembrane (bbtm) proteins are a functionally important and diverse group of proteins expressed in the outer membranes of bacteria (both gram negative and acid fast gram positive), mitochondria and chloroplasts. Despite recent publications describing reasonable levels of accuracy for discriminating between bbtm proteins and other proteins, screening of entire genomes remains troublesome as these molecules only constitute a small fraction of the sequences screened. Therefore, novel methods are still required capable of detecting new families of bbtm protein in diverse genomes.
  
  
  RESULTS
  We present TMB-Hunt, a program that uses a k-Nearest Neighbour (k-NN) algorithm to discriminate between bbtm and non-bbtm proteins on the basis of their amino acid composition. By including differentially weighted amino acids, evolutionary information and by calibrating the scoring, an accuracy of 92.5% was achieved, with 91% sensitivity and 93.8% positive predictive value (PPV), using a rigorous cross-validation procedure. A major advantage of this approach is that because it does not rely on beta-strand detection, it does not require resolved structures and thus larger, more representative, training sets could be used. It is therefore believed that this approach will be invaluable in complementing other, physicochemical and homology based methods. This was demonstrated by the correct reassignment of a number of proteins which other predictors failed to classify. We have used the algorithm to screen several genomes and have discussed our findings.
  
  
  CONCLUSIONS
  TMB-Hunt achieves a prediction accuracy level better than other approaches published to date. Results were significantly enhanced by use of evolutionary information and a system for calibrating k-NN scoring. Because the program uses a distinct approach to that of other discriminators and thus suffers different liabilities, we believe it will make a significant contribution to the development of a consensus approach for bbtm protein detection.
authors.affiliation: School of Biochemistry and Microbiology, University of Leeds, Leeds, LS2 9JT, UK. bmbagg@bmb.leeds.ac.uk
authors.forename: Andrew G
authors.initials: AG
authors.lastname: Garrow
authors.forename: Alison
authors.initials: A
authors.lastname: Agnew
authors.forename: David R
authors.initials: DR
authors.lastname: Westhead
mesh_terms: D000465:Algorithms; D001425:Bacterial Outer Membrane Proteins / Q000737:chemistry; D002138:Calibration; D002462:Cell Membrane / Q000378:metabolism*; D002736:Chloroplasts / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D004926:Escherichia coli / Q000378:metabolism; D019143:Evolution, Molecular; D016678:Genome; D008390:Markov Chains; D008565:Membrane Proteins / Q000737:chemistry*; D008958:Models, Molecular; D015233:Models, Statistical; D017433:Protein Structure, Secondary; D017434:Protein Structure, Tertiary; D011506:Proteins; D020543:Proteome; D040901:Proteomics / Q000379:methods*; D015203:Reproducibility of Results; D016415:Sequence Alignment; D020539:Sequence Analysis, Protein; D017386:Sequence Homology, Amino Acid; D012984:Software; D014584:User-Computer Interface
chemical_list: D001425:Bacterial Outer Membrane Proteins; D008565:Membrane Proteins; D011506:Proteins; D020543:Proteome