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A guideline to proteome-wide alpha-helical membrane protein topology predictions
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).ORCID iD: 0000-0002-7115-9751
2013 (English)In: Proteomics, ISSN 1615-9853, E-ISSN 1615-9861, Vol. 12, no 14, 2282-2294 p.Article in journal (Refereed) Published
Abstract [en]

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 data sets obtained from protein structures or biochemical assays. Here, we test a number of topology predictors on an unseen set of proteins of known structure and also on four genome-scale data sets, including one recent large set of experimentally validated human membrane proteins with glycosylated sites. The set of glycosylated proteins is also used to examine the ability of prediction methods to separate membrane from nonmembrane proteins. The results show that methods utilizing multiple sequence alignments are overall superior to methods that do not. The best performance is obtained by TOPCONS, a consensus method that combines several of the other prediction methods. The best methods to distinguish membrane from nonmembrane proteins belong to the Phobius group of predictors. We further observe that the reported high accuracies in the smaller benchmark sets are not quite maintained in larger scale benchmarks. Instead, we estimate the performance of the best prediction methods for eukaryotic membrane proteins to be between 60% and 70%. The low agreement between predictions from different methods questions earlier estimates about the global properties of the membrane proteome. Finally, we suggest a pipeline to estimate these properties using a combination of the best predictors that could be applied in large-scale proteomics studies of membrane proteins.

Place, publisher, year, edition, pages
2013. Vol. 12, no 14, 2282-2294 p.
Keyword [en]
Bioinformatics, Genome analysis, a-Helical, Membrane, Membrane proteins, Topology predictors
National Category
Biological Sciences Bioinformatics (Computational Biology)
Research subject
Biochemistry towards Bioinformatics
Identifiers
URN: urn:nbn:se:su:diva-81726DOI: 10.1002/pmic.201100495ISI: 000307222600007OAI: oai:DiVA.org:su-81726DiVA: diva2:563731
Funder
Swedish Research Council, VR-NT 2009-5072; VR-M 2010-3555Swedish Foundation for Strategic Research EU, FP7, Seventh Framework Programme, 201924
Note

AuthorCount:4;

Available from: 2012-10-31 Created: 2012-10-30 Last updated: 2017-12-07Bibliographically approved
In thesis
1. Bioinformatics Methods for Topology Prediction of Membrane Proteins
Open this publication in new window or tab >>Bioinformatics Methods for Topology Prediction of Membrane Proteins
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Membrane proteins are key elements of the cell since they are associated with a variety of very important biological functions crucial to its survival. They are implicated in cellular recognition and adhesion, act as molecular receptors, transport substrates through membranes and exhibit specific enzymatic activity.This thesis is focused on integral membrane proteins, most of which contain transmembrane segments that form an alpha helix and are composed of mainly hydrophobic residues, spanning the lipid bilayer. A more specialized and less well-studied case, is the case of integral membrane proteins found in the outer membrane of Gram-negative bacteria and (presumably) in the outer envelope of mitochondria and chloroplasts, proteins whose transmembrane segments are formed by amphipathic beta strands that create a closed barrel (beta-barrels). The importance of transmembrane proteins, as well as the inherent difficulties in crystallizing and obtaining three-dimensional structures of these, dictates the need for developing computational algorithms and tools that will allow for a reliable and fast prediction of their structural and functional features. In order to elucidate their function, we must acquire knowledge about their structure and topology with relation to the membrane. Therefore, a large number of computational methods have been developed in order to predict the transmembrane segments and the overall topology of transmembrane proteins. In this thesis, I initially describe a large-scale benchmark of many topology prediction tools in order to devise a strategy that will allow for better detection of alpha-helical membrane proteins in a proteome. Then, I give a description of construction of improved machine-learning algorithms and computer software for accurate topology prediction of transmembrane proteins and discrimination of such proteins from non-transmembrane proteins. Finally, I introduce a fast way to obtain a position-specific scoring matrix, which is essential for modern topology prediction methods.

Place, publisher, year, edition, pages
Stockholm: Department of Biochemistry and Biophysics, Stockholm University, 2017. 60 p.
National Category
Bioinformatics and Systems Biology
Research subject
Biochemistry towards Bioinformatics
Identifiers
urn:nbn:se:su:diva-138479 (URN)978-91-7649-648-0 (ISBN)978-91-7649-649-7 (ISBN)
Public defence
2017-02-23, Magnélisalen, Kemiska övningslaboratoriet, Svante Arrhenius väg 16 B, 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 3: Manuscript.

Available from: 2017-01-31 Created: 2017-01-23 Last updated: 2017-02-21Bibliographically approved

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