Rapid membrane protein topology prediction

Department of Biochemistry and Biophysics, Stockholm Bioinformatics Center, Center for Biomembrane Research, Swedish e-science Research Center, Stockholm University, Stockholm, Sweden.
Bioinformatics (Impact Factor: 4.98). 05/2011; 27(9):1322-3. DOI: 10.1093/bioinformatics/btr119
Source: PubMed


State-of-the-art methods for topology of α-helical membrane proteins are based on the use of time-consuming multiple sequence alignments obtained from PSI-BLAST or other sources. Here, we examine if it is possible to use the consensus of topology prediction methods that are based on single sequences to obtain a similar accuracy as the more accurate multiple sequence-based methods. Here, we show that TOPCONS-single performs better than any of the other topology prediction methods tested here, but ∼6% worse than the best method that is utilizing multiple sequence alignments.
Availability and Implementation: TOPCONS-single is available as a web server from and is also included for local installation from the web site. In addition, consensus-based topology predictions for the entire international protein index (IPI) is available from the web server and will be updated at regular intervals.
Supplementary information: Supplementary data are avaliable at Bioinformatics online.

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Available from: Arne Elofsson, May 19, 2014
    • "The downside of TOPCONS is the running time, increased by the fact that four of the five predictors are based on profiles which first have to be constructed from a database search. An alternative consensus server, only based on methods that do not require profiles, is TOPCONS-single 33 (Hennerdal and Elofsson 2011), which does approximately six percentage units worse than TOPCONS, but 70 times faster. "
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    ABSTRACT: When predicting the subcellular localization of proteins from their amino acid sequences, there are basically three approaches: signal-based, global property-based, and homology-based. Each of these has its advantages and drawbacks, and it is important when comparing methods to know which approach was used. Various statistical and machine learning algorithms are used with all three approaches, and various measures and standards are employed when reporting the performances of the developed methods. This chapter presents a number of available methods for prediction of sorting signals and subcellular localization, but rather than providing a checklist of which predictors to use, it aims to function as a guide for critical assessment of prediction methods.
    No preview · Article · Jan 2016 · Current topics in microbiology and immunology
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    • "su . se / ) ( Hennerdal and Elofsson 2011 ) . The rela - tive conservation at each amino acid position was determined using CONSURF ( Ashkenazy et al . "

    Full-text · Dataset · Oct 2015
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    • "Consensus prediction of α-helical transmembrane regions was performed using TOPCONS ( (Hennerdal and Elofsson 2011). The relative conservation at each amino acid position was determined using CONSURF (Ashkenazy et al. 2010). "

    Full-text · Dataset · Oct 2015
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