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PRODRES: Fast protein searches using a protein domain-reduced database
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
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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: urn:nbn:se:su:diva-138472OAI: oai:DiVA.org:su-138472DiVA, id: diva2:1067450
Funder
Swedish Research Council, VR-NT 2012-5046Available from: 2017-01-20 Created: 2017-01-20 Last updated: 2017-01-27Bibliographically 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. p. 60
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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