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ProQ3D: improved model quality assessments using deep learning
Stockholm University, Sweden.
Stockholm University, Sweden.
Stockholm University, Sweden; Science Life Lab, Sweden.
Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-3772-8279
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2017 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 33, no 10, 1578-1580 p.Article in journal (Refereed) Published
Abstract [en]

A Summary: Protein quality assessment is a long-standing problem in bioinformatics. For more than a decade we have developed state-of-art predictors by carefully selecting and optimising inputs to a machine learning method. The correlation has increased from 0.60 in ProQ to 0.81 in ProQ2 and 0.85 in ProQ3 mainly by adding a large set of carefully tuned descriptions of a protein. Here, we show that a substantial improvement can be obtained using exactly the same inputs as in ProQ2 or ProQ3 but replacing the support vector machine by a deep neural network. This improves the Pearson correlation to 0.90 (0.85 using ProQ2 input features). Supplementary information: Supplementary data are available at Bioinformatics online.

Place, publisher, year, edition, pages
OXFORD UNIV PRESS , 2017. Vol. 33, no 10, 1578-1580 p.
National Category
Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:liu:diva-138917DOI: 10.1093/bioinformatics/btw819ISI: 000402130700023PubMedID: 28052925OAI: oai:DiVA.org:liu-138917DiVA: diva2:1115838
Note

Funding Agencies|Swedish Research Council [VR-NT 2012-5046, 2012-5270]; Swedish e-Science Research Center

Available from: 2017-06-27 Created: 2017-06-27 Last updated: 2017-06-27

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