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PRED-TMBB2: improved topology prediction and detection of beta-barrel outer membrane proteins
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab). University of Thessaly, Greece .
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
Number of Authors: 3
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.

Place, publisher, year, edition, pages
2016. Vol. 32, no 17, 665-671 p.
National Category
Biological Sciences Environmental Biotechnology Computer and Information Science Mathematics
Identifiers
URN: urn:nbn:se:su:diva-135026DOI: 10.1093/bioinformatics/btw444ISI: 000384666800034PubMedID: 27587687OAI: oai:DiVA.org:su-135026DiVA: diva2:1045649
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: 2016-11-10Bibliographically approved

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Tsirigos, Konstantinos D.Elofsson, Arne
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