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Bioinformatics. 2017 May 15;33(10):1578-1580. doi: 10.1093/bioinformatics/btw819.

ProQ3D: improved model quality assessments using deep learning.

Author information

1
Department of Biochemistry and Biophysics and Science for Life Laboratory, Stockholm University, Solna, Sweden.
2
Bioinformatics Short-term Support and Infrastructure (BILS), Science for Life Laboratory, Solna, Sweden.
3
Department of Physics, Chemistry and Biology (IFM)/Bioinformatics. Linköping University, ?Linköping, Sweden.

Abstract

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).

Availability and Implementation:

ProQ3D is freely available both as a webserver and a stand-alone program at http://proq3.bioinfo.se/.

Contact:

arne@bioinfo.se.

Supplementary information:

Supplementary data are available at Bioinformatics online.

PMID:
28052925
DOI:
10.1093/bioinformatics/btw819
[Indexed for MEDLINE]

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