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The TOPCONS web server for consensus prediction of membrane protein topology and signal peptides
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).
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Number of Authors: 5
2015 (English)In: Nucleic Acids Research, ISSN 0305-1048, E-ISSN 1362-4962, Vol. 43, no W1, W401-W407 p.Article in journal (Refereed) Published
Abstract [en]

TOPCONS (http://topcons.net/) is a widely used web server for consensus prediction of membrane protein topology. We hereby present a major update to the server, with some substantial improvements, including the following: (i) TOPCONS can now efficiently separate signal peptides from transmembrane regions. (ii) The server can now differentiate more successfully between globular and membrane proteins. (iii) The server now is even slightly faster, although a much larger database is used to generate the multiple sequence alignments. For most proteins, the final prediction is produced in a matter of seconds. (iv) The user-friendly interface is retained, with the additional feature of submitting batch files and accessing the server programmatically using standard interfaces, making it thus ideal for proteome-wide analyses. Indicatively, the user can now scan the entire human proteome in a few days. (v) For proteins with homology to a known 3D structure, the homology-inferred topology is also displayed. (vi) Finally, the combination of methods currently implemented achieves an overall increase in performance by 4% as compared to the currently available best-scoring methods and TOPCONS is the only method that can identify signal peptides and still maintain a state-of-the-art performance in topology predictions.

Place, publisher, year, edition, pages
2015. Vol. 43, no W1, W401-W407 p.
National Category
Biological Sciences
Research subject
Biochemistry; Biochemistry towards Bioinformatics
Identifiers
URN: urn:nbn:se:su:diva-120710DOI: 10.1093/nar/gkv485ISI: 000359772700063OAI: oai:DiVA.org:su-120710DiVA: diva2:854434
Funder
Swedish Research Council
Available from: 2015-09-16 Created: 2015-09-15 Last updated: 2017-12-04Bibliographically approved
In thesis
1. Topology Prediction of α-Helical Transmembrane Proteins
Open this publication in new window or tab >>Topology Prediction of α-Helical Transmembrane Proteins
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Membrane proteins fulfil a number of tasks in cells, including signalling, cell-cell interaction, and the transportation of molecules. The prominence of these tasks makes membrane proteins an important target for clinical drugs. Because of the decreasing price of sequencing, the number of sequences known is increasing at such a rate that manual annotations cannot compete. Here, topology prediction is a way to provide additional information. It predicts the location and number of transmembrane helices in the protein and the orientation inside the membrane. An important factor to detect transmembrane helices is their hydrophobicity, which can be calculated using dedicated scales. In the first paper, we studied the difference between several hydrophobicity scales and evaluated their performance. We showed that while they appear to be similar, their performance for topology prediction differs significantly. The better performing scales appear to measure the probability of amino acids to be within a transmembrane helix, instead of just being located in a hydrophobic environment.

Around 20% of the transmembrane helices are too hydrophilic to explain their insertion with hydrophobicity alone. These are referred to as marginally hydrophobic helices. In the second paper, we studied three of these helices experimentally and performed an analysis on membrane proteins. The experiments show that for all three helices positive charges on the N-terminal side of the subsequent helix are important to insert, but only two need the subsequent helix. Additionally, the analysis shows that not only the N-terminal helices are more hydrophobic, but also the C-terminal transmembrane helices.

In Paper III, the finding from the second paper was used to improve the topology prediction. By extending our hidden Markov model with N- and C-terminal helix states, we were able to set stricter cut-offs. This improved the general topology prediction and in particular miss-prediction in large N- and C-terminal domains, as well the separation between transmembrane and non-transmembrane proteins.

Lastly, we contribute several new features to our consensus topology predictor, TOPCONS. We added states for the detection of signal peptides to its hidden Markov model and thus reduce the over-prediction of transmembrane helices. With a new method for the generation of profile files, it is possible to increase the size of the database used to find homologous proteins and decrease the running time by 75%.

Place, publisher, year, edition, pages
Stockholm: Department of Biochemistry and Biophysics, Stockholm University, 2016. 46 p.
National Category
Bioinformatics (Computational Biology)
Research subject
Biochemistry
Identifiers
urn:nbn:se:su:diva-129061 (URN)
Public defence
2016-06-03, Magnélisalen, Kemiska övningslaboratoriet, Svante Arrhenius väg 16 B, Stockholm, 10:00 (English)
Opponent
Supervisors
Available from: 2016-05-11 Created: 2016-04-13 Last updated: 2017-02-24Bibliographically approved
2. 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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