ProQ3D: improved model quality assessments using deep learning

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


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


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*