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Model quality assessment for membrane proteins
KTH, School of Engineering Sciences (SCI), Theoretical Physics, Theoretical & Computational Biophysics.
KTH, School of Engineering Sciences (SCI), Theoretical Physics, Theoretical & Computational Biophysics.ORCID iD: 0000-0002-2734-2794
Center for Biomembrane Research, Stockholm, Sweden.
2010 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1460-2059, Vol. 26, no 24, 3067-3074 p.Article in journal (Refereed) Published
Abstract [en]

Motivation: Learning-based model quality assessment programs have been quite successful at discriminating between high-and low-quality protein structures. Here, we show that it is possible to improve this performance significantly by restricting the learning space to a specific context, in this case membrane proteins. Since these are among the most important structures from a pharmaceutical point-of-view, it is particularly interesting to resolve local model quality for regions corresponding, e. g. to binding sites. Results: Our new ProQM method uses a support vector machine with a combination of general and membrane protein-specific features. For the transmembrane region, ProQM clearly outperforms all methods developed for generic proteins, and it does so while maintaining performance for extra-membrane domains; in this region it is only matched by ProQres. The predictor is shown to accurately predict quality both on the global and local level when applied to GPCR models, and clearly outperforms consensus-based scoring. Finally, the combination of ProQM and the Rosetta low-resolution energy function achieve a 7-fold enrichment in selection of near-native structural models, at very limited computational cost.

Place, publisher, year, edition, pages
2010. Vol. 26, no 24, 3067-3074 p.
National Category
Biological Sciences Computer and Information Science
URN: urn:nbn:se:kth:diva-27984DOI: 10.1093/bioinformatics/btq581ISI: 000284947700009ScopusID: 2-s2.0-79951747486OAI: diva2:382681
Swedish Research CouncilSwedish e‐Science Research Center
QC 20110103Available from: 2011-01-03 Created: 2011-01-03 Last updated: 2012-05-22Bibliographically approved
In thesis
1. Quality assessment of protein models
Open this publication in new window or tab >>Quality assessment of protein models
2012 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Proteins are crucial for all living organisms and they are involved in many different processes. The function of a protein is tightly coupled to its structure, yet to determine the structure experimentally is both non-trivial and expensive. Computational methods that are able to predict the structure are often the only possibility to obtain structural information for a particular protein. Structure prediction has come a long way since its inception. More advanced algorithms, refined mathematics and statistical analysis and use of machine learning techniques have improved this field considerably. Making a large number of protein models is relatively fast. The process of identifying and separating correct from less correct models, from a large set of plausible models, is also known as model quality assessment. Critical Assessment of Techniques for Protein Structure Prediction (CASP) is an international experiment to assess the various methods for structure prediction of proteins. CASP has shown the improvements of these different methods in model quality assessment, structure prediction as well as better model building.

In the two studies done in this thesis, I have improved the model quality assessment part of this structure prediction problem for globular proteins, as well as trained the first such method dedicated towards membrane proteins. The work has resulted in a much-improved version of our previous model quality assessment program ProQ, and in addition I have also developed the first model quality assessment program specifically tailored for membrane proteins.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2012. vi, 37 p.
Trita-FYS, ISSN 0280-316X ; 2012:07
National Category
Bioinformatics and Systems Biology
Research subject
SRA - Molecular Bioscience
urn:nbn:se:kth:diva-90830 (URN)978-91-7501-256-8 (ISBN)
2012-03-09, FB53, Roslagstullsbacken 21, Albanova, Stockholm, 10:00 (English)
Swedish e‐Science Research CenterScience for Life Laboratory - a national resource center for high-throughput molecular bioscience

QC 20120313

Available from: 2012-03-13 Created: 2012-02-29 Last updated: 2013-04-15Bibliographically approved

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