Literature DB >> 16858668

Predicting transmembrane beta-barrels and interstrand residue interactions from sequence.

J Waldispühl1, Bonnie Berger, Peter Clote, Jean-Marc Steyaert.   

Abstract

Transmembrane beta-barrel (TMB) proteins are embedded in the outer membrane of Gram-negative bacteria, mitochondria, and chloroplasts. The cellular location and functional diversity of beta-barrel outer membrane proteins (omps) makes them an important protein class. At the present time, very few nonhomologous TMB structures have been determined by X-ray diffraction because of the experimental difficulty encountered in crystallizing transmembrane proteins. A novel method using pairwise interstrand residue statistical potentials derived from globular (nonouter membrane) proteins is introduced to predict the supersecondary structure of transmembrane beta-barrel proteins. The algorithm transFold employs a generalized hidden Markov model (i.e., multitape S-attribute grammar) to describe potential beta-barrel supersecondary structures and then computes by dynamic programming the minimum free energy beta-barrel structure. Hence, the approach can be viewed as a "wrapping" component that may capture folding processes with an initiation stage followed by progressive interaction of the sequence with the already-formed motifs. This approach differs significantly from others, which use traditional machine learning to solve this problem, because it does not require a training phase on known TMB structures and is the first to explicitly capture and predict long-range interactions. TransFold outperforms previous programs for predicting TMBs on smaller (<or=200 residues) proteins and matches their performance for straightforward recognition of longer proteins. An exception is for multimeric porins where the algorithm does perform well when an important functional motif in loops is initially identified. We verify our simulations of the folding process by comparing them with experimental data on the functional folding of TMBs. A Web server running transFold is available and outputs contact predictions and locations for sequences predicted to form TMBs. Proteins 2006. (c) 2006 Wiley-Liss, Inc.

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Year:  2006        PMID: 16858668     DOI: 10.1002/prot.21046

Source DB:  PubMed          Journal:  Proteins        ISSN: 0887-3585


  9 in total

1.  A graph-theoretic approach for classification and structure prediction of transmembrane β-barrel proteins.

Authors:  Van Du T Tran; Philippe Chassignet; Saad Sheikh; Jean-Marc Steyaert
Journal:  BMC Genomics       Date:  2012-04-12       Impact factor: 3.969

2.  Simultaneous alignment and folding of protein sequences.

Authors:  Jérôme Waldispühl; Charles W O'Donnell; Sebastian Will; Srinivas Devadas; Rolf Backofen; Bonnie Berger
Journal:  J Comput Biol       Date:  2014-04-25       Impact factor: 1.479

3.  Probabilistic grammatical model for helix-helix contact site classification.

Authors:  Witold Dyrka; Jean-Christophe Nebel; Malgorzata Kotulska
Journal:  Algorithms Mol Biol       Date:  2013-12-18       Impact factor: 1.405

4.  TMBB-DB: a transmembrane β-barrel proteome database.

Authors:  Thomas C Freeman; William C Wimley
Journal:  Bioinformatics       Date:  2012-07-27       Impact factor: 6.937

5.  Predicting the outer membrane proteome of Pasteurella multocida based on consensus prediction enhanced by results integration and manual confirmation.

Authors:  Teerasak E-komon; Richard Burchmore; Pawel Herzyk; Robert Davies
Journal:  BMC Bioinformatics       Date:  2012-04-27       Impact factor: 3.169

6.  A method for probing the mutational landscape of amyloid structure.

Authors:  Charles W O'Donnell; Jérôme Waldispühl; Mieszko Lis; Randal Halfmann; Srinivas Devadas; Susan Lindquist; Bonnie Berger
Journal:  Bioinformatics       Date:  2011-07-01       Impact factor: 6.937

7.  transFold: a web server for predicting the structure and residue contacts of transmembrane beta-barrels.

Authors:  J Waldispühl; Bonnie Berger; Peter Clote; Jean-Marc Steyaert
Journal:  Nucleic Acids Res       Date:  2006-07-01       Impact factor: 16.971

8.  Searching for universal model of amyloid signaling motifs using probabilistic context-free grammars.

Authors:  Marlena Gąsior-Głogowska; Monika Szefczyk; Witold Dyrka; Natalia Szulc
Journal:  BMC Bioinformatics       Date:  2021-04-29       Impact factor: 3.169

9.  How many 3D structures do we need to train a predictor?

Authors:  Pantelis G Bagos; Georgios N Tsaousis; Stavros J Hamodrakas
Journal:  Genomics Proteomics Bioinformatics       Date:  2009-09       Impact factor: 7.691

  9 in total

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