ArticleBMC bioinformatics2004
A Hidden Markov Model method, capable of predicting and discriminating beta-barrel outer membrane proteins.
Article in BMC bioinformatics, 2004. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers.
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70 citing papers in PubMed.
- The hidden Markov model and its applications in bioinformatics analysis.Genes & diseases · 2026Review
- Design of a Multi-Epitope Vaccine using β-barrel Outer Membrane Proteins Identified in Chlamydia trachomatis.The Journal of membrane biology · 2025Article
- General features of transmembrane beta barrels from a large database.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- A bacterial autotransporter impairs innate immune responses by targeting the transcription factor TFE3.Nature communications · 2023Article
- A genome sequence for Biomphalaria pfeifferi, the major vector snail for the human-infecting parasite Schistosoma mansoni.PLoS neglected tropical diseases · 2023Article
- A novel structurally identified epitope delivered by macrophage membrane-coated PLGA nanoparticles elicits protection against Pseudomonas aeruginosa.Journal of nanobiotechnology · 2022Article
- Identification of amino acid domains ofFrontiers in cellular and infection microbiology · 2022Article
- Membrane Profiling by Free Flow Electrophoresis and SWATH-MS to Characterize Subcellular Compartment Proteomes inInternational journal of molecular sciences · 2021Article
- Computational prediction of secreted proteins in gram-negative bacteria.Computational and structural biotechnology journal · 2021Review
- Hidden neural networks for transmembrane protein topology prediction.Computational and structural biotechnology journal · 2021Article
- Prediction of Transmembrane Regions, Cholesterol, and Ganglioside Binding Sites in Amyloid-Forming Proteins Indicate Potential for Amyloid Pore Formation.Frontiers in molecular neuroscience · 2021Article
- Proteomic analysis of Rickettsia akari proposes a 44 kDa-OMP as a potential biomarker for Rickettsialpox diagnosis.BMC microbiology · 2020Article
- A Brief History of Protein Sorting Prediction.The protein journal · 2019Review
- Exploring the Potential Role of Moonlighting Function of the Surface-Associated Proteins FromFrontiers in immunology · 2019Article
- Acinetobacter spp. porin Omp33-36: Classification and transcriptional response to carbapenems and host cells.PloS one · 2018Article
- Iron and Virulence inFrontiers in cellular and infection microbiology · 2017Review
- Two outer membrane proteins are bovine lactoferrin-binding proteins in Mannheimia haemolytica A1.Veterinary research · 2016Article
- Consensus computational network analysis for identifying candidate outer membrane proteins from Borrelia spirochetes.BMC microbiology · 2016Article
- Two parallel pathways for ferric and ferrous iron acquisition support growth and virulence of the intracellular pathogen Francisella tularensis Schu S4.MicrobiologyOpen · 2016Article
- The Borrelia afzelii outer membrane protein BAPKO_0422 binds human factor-H and is predicted to form a membrane-spanning β-barrel.Bioscience reports · 2015Article
10 more citing papers are in PubMed but not listed here.
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4 authors.
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Abstract
backgroundIntegral membrane proteins constitute about 20-30% of all proteins in the fully sequenced genomes. They come in two structural classes, the alpha-helical and the beta-barrel membrane proteins, demonstrating different physicochemical characteristics, structure and localization. While transmembrane segment prediction for the alpha-helical integral membrane proteins appears to be an easy task nowadays, the same is much more difficult for the beta-barrel membrane proteins. We developed a method, based on a Hidden Markov Model, capable of predicting the transmembrane beta-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.
resultsThe 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.
conclusionBased 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: http://bioinformatics.biol.uoa.gr/PRED-TMBB, and it is the only freely available HMM-based predictor for beta-barrel outer membrane protein topology.
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