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A guideline to α-helical membrane protein topology prediction
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. (Arne Elofsson)
(English)Manuscript (preprint) (Other academic)
Abstract [en]

All living organisms have a “membrane proteome” that mainly consists of α-helical mem- brane proteins containing one or more TM-helices. Prediction methods have been extensively used to identify as well as to classify the topology of these proteins. For current state-of-the- art methods, the prediction of correct topology of membrane proteins has been reported to be above 80%. However, this performance has only been observed in small and possibly biased datasets. Here, we add four “genome-scale” datasets, including a recent large set of experimen- tally validated membrane proteins with glycosylation sites. This set is also used to examine whether the qualities of topology predictions hold and if any prediction methods perform con- sistently better than others. We find that methods utilizing multiple sequence alignments are overall superior to methods that do not. The best performance is obtained by TOPCONS, a consensus method which combines several of the other prediction methods. Further, we show that the accuracy is most likely lower in eukaryotes than for prokaryotic proteins as the agree- ment between the predictors is significantly lower there. Finally, we show that three related methods, Phobius, Phillius and PolyPhobius, that incorporate a specific signal peptide module are superior to all other methods at the task of distinguishing between membrane and non- membrane proteins.

National Category
Bioinformatics and Systems Biology
Research subject
Biochemistry with Emphasis on Theoretical Chemistry
Identifiers
URN: urn:nbn:se:su:diva-61923OAI: oai:DiVA.org:su-61923DiVA: diva2:438713
Available from: 2011-09-05 Created: 2011-09-05 Last updated: 2011-09-07Bibliographically approved
In thesis
1. Application of membrane protein topology prediction
Open this publication in new window or tab >>Application of membrane protein topology prediction
2011 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Membrane proteins often have essential functions in the cell and many are important drug targets, yet only a small fraction of available protein structures are of membrane proteins. Experimental techniques for elucidating membrane protein structures have proven laborious and expensive, opening the field for comparatively inexpensive computational modeling. Topology prediction addresses a sub-problem of structure prediction for α-helical membrane proteins by modeling which parts of the peptide chain are in, and which parts are on either side, of the membrane.

This work describes an algorithm for combining the results of several topology prediction methods to increase prediction accuracy and to quantify prediction reliability, and a faster implementation of the algorithm applicable to large-scale genome data.

Further, topology prediction is applied, together with other sequence-based methods, to detect duplications in membrane proteins in whole genomes. We find more duplications in the genomes of yeast and E. coli than in human, possibly due to the abundance of nonduplicated GPCRs in human. A gene duplication and subsequent fusion event constitute a likely origin for duplicated proteins, yet only for one superfamily, the AcrB Multidrug Efflux Pump, do we find the duplicated unit in its nonduplicated form. This apparent scarcity of nonduplicated forms is confirmed when extending the study to the whole human genome.

Finally, a benchmark study of topology prediction on several comparably large datasets is described. We confirm previous results showing that methods utilizing homology information top the ranking of topology prediction methods. We also see that the separation of membrane proteins from non-membrane proteins has a partially different set of requirements than topology prediction of membrane proteins, and we suggest a pipeline using different methods for these two tasks.

Place, publisher, year, edition, pages
Stockholm: Department of Biochemistry and Biophysics, Stockholm University, 2011. 65 p.
National Category
Biochemistry and Molecular Biology
Research subject
Biochemistry with Emphasis on Theoretical Chemistry
Identifiers
urn:nbn:se:su:diva-61950 (URN)978-91-7447-324-7 (ISBN)
Public defence
2011-10-14, Magnélisalen, Kemiska övningslaboratoriet, Svante Arrhenius väg 16 B, Stockholm, 10:00 (English)
Opponent
Supervisors
Note
At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 4: Manuscript.Available from: 2011-09-22 Created: 2011-09-06 Last updated: 2011-09-19Bibliographically approved

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CiteExportLink to record
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Citation style
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