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PconsFold: improved contact predictions improve protein models
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
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2014 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 30, no 17, 1482-1488 p.Article in journal (Refereed) Published
Abstract [en]

Motivation: Recently it has been shown that the quality of protein contact prediction from evolutionary information can be improved significantly if direct and indirect information is separated. Given sufficiently large protein families, the contact predictions contain sufficient information to predict the structure of many protein families. However, since the first studies contact prediction methods have improved. Here, we ask how much the final models are improved if improved contact predictions are used.

Results: In a small benchmark of 15 proteins, we show that the TM-scores of top-ranked models are improved by on average 33% using PconsFold compared with the original version of EVfold. In a larger benchmark, we find that the quality is improved with 15-30% when using PconsC in comparison with earlier contact prediction methods. Further, using Rosetta instead of CNS does not significantly improve global model accuracy, but the chemistry of models generated with Rosetta is improved.

Place, publisher, year, edition, pages
2014. Vol. 30, no 17, 1482-1488 p.
National Category
Bioinformatics (Computational Biology)
URN: urn:nbn:se:su:diva-108840DOI: 10.1093/bioinformatics/btu458ISI: 000342912400020PubMedID: 25161237OAI: diva2:760838
Available from: 2014-11-04 Created: 2014-11-04 Last updated: 2014-11-10Bibliographically approved

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Michel, MircoElofsson, Arne
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