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Bioinformatics Methods for Topology Prediction of Membrane Proteins
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
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: urn:nbn:se:su:diva-138479ISBN: 978-91-7649-648-0 (print)ISBN: 978-91-7649-649-7 (electronic)OAI: oai:DiVA.org:su-138479DiVA: diva2:1067468
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
List of papers
1. A guideline to proteome-wide alpha-helical membrane protein topology predictions
Open this publication in new window or tab >>A guideline to proteome-wide alpha-helical membrane protein topology predictions
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.

Keyword
Bioinformatics, Genome analysis, a-Helical, Membrane, Membrane proteins, Topology predictors
National Category
Biological Sciences Bioinformatics (Computational Biology)
Research subject
Biochemistry towards Bioinformatics
Identifiers
urn:nbn:se:su:diva-81726 (URN)10.1002/pmic.201100495 (DOI)000307222600007 ()
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: 2018-01-12Bibliographically approved
2. The TOPCONS web server for consensus prediction of membrane protein topology and signal peptides
Open this publication in new window or tab >>The TOPCONS web server for consensus prediction of membrane protein topology and signal peptides
Show others...
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.

National Category
Biological Sciences
Research subject
Biochemistry; Biochemistry towards Bioinformatics
Identifiers
urn:nbn:se:su:diva-120710 (URN)10.1093/nar/gkv485 (DOI)000359772700063 ()
Funder
Swedish Research Council
Available from: 2015-09-16 Created: 2015-09-15 Last updated: 2017-12-04Bibliographically approved
3. PRODRES: Fast protein searches using a protein domain-reduced database
Open this publication in new window or tab >>PRODRES: Fast protein searches using a protein domain-reduced database
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

Motivation: Detection of homologous sequences is a the basis formany bioinformatics applications. Position-Specific Scoring Matrices(PSSMs) or Hidden Markov Models (HMMs) are often created fromthe detected homologous sequences. These are then widely usedin many bioinformatics software in order to incorporate evolutionaryinformation in the prediction process. However, due to the increasein the size of reference databases, there is a continuous decrease inspeed of homology detection even with faster computers.Results: By using PRODRES, we save on average X percent ofthe search time. This pipeline has been exploited in our widely usedtopology prediction software, TOPCONS. In total, more than 5 millionPSSMs have been generated, with an average running time of about1 minute. This corresponds to an approximate 10 times speed-up ofthe whole process.Availability and implementation: A standalone version ofPRODRES can be found in the Github repository https://github.com/-ElofssonLab/PRODRES, while a web-server implementing themethod is available for academic users at http://PRODRES.bioinfo.se/

National Category
Bioinformatics and Systems Biology
Research subject
Biochemistry towards Bioinformatics
Identifiers
urn:nbn:se:su:diva-138472 (URN)
Funder
Swedish Research Council, VR-NT 2012-5046
Available from: 2017-01-20 Created: 2017-01-20 Last updated: 2017-01-27Bibliographically approved
4. PRED-TMBB2: improved topology prediction and detection of beta-barrel outer membrane proteins
Open this publication in new window or tab >>PRED-TMBB2: improved topology prediction and detection of beta-barrel outer membrane proteins
2016 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 32, no 17, 665-671 p.Article in journal (Refereed) Published
Abstract [en]

Motivation: The PRED-TMBB method is based on Hidden Markov Models and is capable of predicting the topology of beta-barrel outer membrane proteins and discriminate them from water-soluble ones. Here, we present an updated version of the method, PRED-TMBB2, with several newly developed features that improve its performance. The inclusion of a properly defined end state allows for better modeling of the beta-barrel domain, while different emission probabilities for the adjacent residues in strands are used to incorporate knowledge concerning the asymmetric amino acid distribution occurring there. Furthermore, the training was performed using newly developed algorithms in order to optimize the labels of the training sequences. Moreover, the method is retrained on a larger, non-redundant dataset which includes recently solved structures, and a newly developed decoding method was added to the already available options. Finally, the method now allows the incorporation of evolutionary information in the form of multiple sequence alignments. Results: The results of a strict cross-validation procedure show that PRED-TMBB2 with homology information performs significantly better compared to other available prediction methods. It yields 76% in correct topology predictions and outperforms the best available predictor by 7%, with an overall SOV of 0.9. Regarding detection of beta-barrel proteins, PRED-TMBB2, using just the query sequence as input, achieves an MCC value of 0.92, outperforming even predictors designed for this task and are much slower.

National Category
Biological Sciences Bioinformatics (Computational Biology)
Research subject
Biochemistry towards Bioinformatics
Identifiers
urn:nbn:se:su:diva-135026 (URN)10.1093/bioinformatics/btw444 (DOI)000384666800034 ()27587687 (PubMedID)
Conference
15th European Conference on Computational Biology (ECCB), The Hague, Netherlands, September 3-7, 2016
Available from: 2016-11-10 Created: 2016-10-31 Last updated: 2018-01-13Bibliographically approved

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