ProQ3D: improved model quality assessments using deep learning

Karolis Uziela; David Menéndez Hurtado; Nanjiang Shu; Björn Wallner; Arne Elofsson

doi:10.1093/bioinformatics/btw819

ProQ3D: improved model quality assessments using deep learning

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

Authors

Karolis Uziela¹, David Menéndez Hurtado¹, Nanjiang Shu^{1

2}, Björn Wallner³, Arne Elofsson¹

Affiliations

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.

PMID: 28052925
DOI: 10.1093/bioinformatics/btw819

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.

MeSH terms

Computational Biology / methods*
Models, Molecular
Neural Networks, Computer*
Protein Conformation*
Software*
Support Vector Machine*