title: ProQ3D: improved model quality assessments using deep learning.
abstract: CONCLUSIONS
  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).
  
  
  METHODS
  ProQ3D is freely available both as a webserver and a stand-alone program at http://proq3.bioinfo.se/.
  
  
  BACKGROUND
  arne@bioinfo.se.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Biochemistry and Biophysics and Science for Life Laboratory, Stockholm University, Solna, Sweden.
authors.forename: Karolis
authors.initials: K
authors.lastname: Uziela
authors.affiliation: Department of Biochemistry and Biophysics and Science for Life Laboratory, Stockholm University, Solna, Sweden.
authors.forename: David
authors.initials: D
authors.lastname: Menéndez Hurtado
authors.affiliation: Department of Biochemistry and Biophysics and Science for Life Laboratory, Stockholm University, Solna, Sweden.|Bioinformatics Short-term Support and Infrastructure (BILS), Science for Life Laboratory, Solna, Sweden.
authors.forename: Nanjiang
authors.initials: N
authors.lastname: Shu
authors.affiliation: Department of Physics, Chemistry and Biology (IFM)/Bioinformatics. Linköping University, ?Linköping, Sweden.
authors.forename: Björn
authors.initials: B
authors.lastname: Wallner
authors.affiliation: Department of Biochemistry and Biophysics and Science for Life Laboratory, Stockholm University, Solna, Sweden.
authors.forename: Arne
authors.initials: A
authors.lastname: Elofsson
mesh_terms: D019295:Computational Biology / Q000379:methods*; D008958:Models, Molecular; D016571:Neural Networks, Computer; D011487:Protein Conformation; D012984:Software; D060388:Support Vector Machine