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      PROFtmb: a web server for predicting bacterial transmembrane beta barrel proteins

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      Author(s): 1 , 2 , * , 1 , 2 , 3
      Publication date (Electronic):
      Journal: Nucleic Acids Research
      Publisher: Oxford University Press

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          Abstract

          PROFtmb predicts transmembrane beta-barrel (TMB) proteins in Gram-negative bacteria. For each query protein, PROFtmb provides both a Z-value indicating that the protein actually contains a membrane barrel, and a four-state per-residue labeling of upward- and downward-facing strands, periplasmic hairpins and extracellular loops. While most users submit individual proteins known to contain TMBs, some groups submit entire proteomes to screen for potential TMBs. Response time is about 4 min for a 500-residue protein. PROFtmb is a profile-based Hidden Markov Model (HMM) with an architecture mirroring the structure of TMBs. The per-residue accuracy on the 8-fold cross-validated testing set is 86% while whole-protein discrimination accuracy was 70 at 60% coverage. The PROFtmb web server includes all source code, training data and whole-proteome predictions from 78 Gram-negative bacterial genomes and is available freely and without registration at http://rostlab.org/services/proftmb.

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          SCOP database in 2002: refinements accommodate structural genomics.

          Tim J P Hubbard, Richard E. Brenner, Cyrus Chothia … (2002)
          The SCOP (Structural Classification of Proteins) database is a comprehensive ordering of all proteins of known structure, according to their evolutionary and structural relationships. Protein domains in SCOP are grouped into species and hierarchically classified into families, superfamilies, folds and classes. Recently, we introduced a new set of features with the aim of standardizing access to the database, and providing a solid basis to manage the increasing number of experimental structures expected from structural genomics projects. These features include: a new set of identifiers, which uniquely identify each entry in the hierarchy; a compact representation of protein domain classification; a new set of parseable files, which fully describe all domains in SCOP and the hierarchy itself. These new features are reflected in the ASTRAL compendium. The SCOP search engine has also been updated, and a set of links to external resources added at the level of domain entries. SCOP can be accessed at http://scop.mrc-lmb.cam.ac.uk/scop.
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            Predicting transmembrane beta-barrels in proteomes.

            Dariusz Przybylski, D Petrey, Burkhard Rost … (2003)
            Very few methods address the problem of predicting beta-barrel membrane proteins directly from sequence. One reason is that only very few high-resolution structures for transmembrane beta-barrel (TMB) proteins have been determined thus far. Here we introduced the design, statistics and results of a novel profile-based hidden Markov model for the prediction and discrimination of TMBs. The method carefully attempts to avoid over-fitting the sparse experimental data. While our model training and scoring procedures were very similar to a recently published work, the architecture and structure-based labelling were significantly different. In particular, we introduced a new definition of beta- hairpin motifs, explicit state modelling of transmembrane strands, and a log-odds whole-protein discrimination score. The resulting method reached an overall four-state (up-, down-strand, periplasmic-, outer-loop) accuracy as high as 86%. Furthermore, accurately discriminated TMB from non-TMB proteins (45% coverage at 100% accuracy). This high precision enabled the application to 72 entirely sequenced Gram-negative bacteria. We found over 164 previously uncharacterized TMB proteins at high confidence. Database searches did not implicate any of these proteins with membranes. We challenge that the vast majority of our 164 predictions will eventually be verified experimentally. All proteome predictions and the PROFtmb prediction method are available at http://www.rostlab.org/ services/PROFtmb/.
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              A Hidden Markov Model method, capable of predicting and discriminating β-barrel outer membrane proteins

              Pantelis G. Bagos, Theodore Liakopoulos, Ioannis Spyropoulos … (2004)
              Background Integral membrane proteins constitute about 20–30% of all proteins in the fully sequenced genomes. They come in two structural classes, the α-helical and the β-barrel membrane proteins, demonstrating different physicochemical characteristics, structure and localization. While transmembrane segment prediction for the α-helical integral membrane proteins appears to be an easy task nowadays, the same is much more difficult for the β-barrel membrane proteins. We developed a method, based on a Hidden Markov Model, capable of predicting the transmembrane β-strands of the outer membrane proteins of gram-negative bacteria, and discriminating those from water-soluble proteins in large datasets. The model is trained in a discriminative manner, aiming at maximizing the probability of correct predictions rather than the likelihood of the sequences. Results The training has been performed on a non-redundant database of 14 outer membrane proteins with structures known at atomic resolution; it has been tested with a jacknife procedure, yielding a per residue accuracy of 84.2% and a correlation coefficient of 0.72, whereas for the self-consistency test the per residue accuracy was 88.1% and the correlation coefficient 0.824. The total number of correctly predicted topologies is 10 out of 14 in the self-consistency test, and 9 out of 14 in the jacknife. Furthermore, the model is capable of discriminating outer membrane from water-soluble proteins in large-scale applications, with a success rate of 88.8% and 89.2% for the correct classification of outer membrane and water-soluble proteins respectively, the highest rates obtained in the literature. That test has been performed independently on a set of known outer membrane proteins with low sequence identity with each other and also with the proteins of the training set. Conclusion Based on the above, we developed a strategy, that enabled us to screen the entire proteome of E. coli for outer membrane proteins. The results were satisfactory, thus the method presented here appears to be suitable for screening entire proteomes for the discovery of novel outer membrane proteins. A web interface available for non-commercial users is located at: , and it is the only freely available HMM-based predictor for β-barrel outer membrane protein topology.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Nucleic Acids Res
                Journal ID (publisher-id): Nucleic Acids Research
                Title: Nucleic Acids Research
                Publisher: Oxford University Press
                ISSN (Print): 0305-1048
                ISSN (Electronic): 1362-4962
                Publication date Collection: 01 July 2006
                Publication date (Print): 01 July 2006
                Publication date (Electronic): 14 July 2006
                Volume: 34
                Issue: Web Server issue
                Pages: W186-W188
                Affiliations
                1CUBIC, Department of Biochemistry and Molecular Biophysics, Columbia University 630 West 168th Street, New York, NY 10032, USA
                2Columbia University Center for Computational Biology and Bioinformatics (C2B2) 1130 St. Nicholas Avenue Rm. 802, New York, NY 10032, USA
                3North East Structural Genomics Center (NESG), Irvine Cancer Center 1130 St. Nicholas Avenue Rm. 802, New York, NY 10032, USA
                Author notes
                *To whom correspondence should be addressed. Tel: +1 212 851 4669; Fax: +1 212 851 5176; Email: hrbigelow@ 123456gmail.com
                Article
                DOI: 10.1093/nar/gkl262
                PMC ID: 1538807
                PubMed ID: 16844988
                SO-VID: d30f2a12-4bc4-46b8-b355-e9ec86dcd96d
                Copyright © © The Author 2006. Published by Oxford University Press. All rights reserved
                License:

                The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oxfordjournals.org

                History
                Date received : 14 February 2006
                Date revision received : 01 March 2006
                Date accepted : 31 March 2006
                Categories
                Subject: Article

                ScienceOpen disciplines: Genetics
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                ScienceOpen disciplines: Genetics

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