title: PconsD: ultra rapid, accurate model quality assessment for protein structure prediction.
abstract: CONCLUSIONS
  Clustering methods are often needed for accurately assessing the quality of modeled protein structures. Recent blind evaluation of quality assessment methods in CASP10 showed that there is little difference between many different methods as far as ranking models and selecting best model are concerned. When comparing many models, the computational cost of the model comparison can become significant. Here, we present PconsD, a fast, stream-computing method for distance-driven model quality assessment that runs on consumer hardware. PconsD is at least one order of magnitude faster than other methods of comparable accuracy.
  
  
  BACKGROUND
  The source code for PconsD is freely available at http://d.pcons.net/. Supplementary benchmarking data are also available there.
  
  
  BACKGROUND
  arne@bioinfo.se
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Biochemistry and Biophysics, Science for Life Laboratory, Swedish E-Science Research Center, Stockholm University, Box 1031, 17121 Solna, Sweden.
authors.forename: Marcin J
authors.initials: MJ
authors.lastname: Skwark
authors.forename: Arne
authors.initials: A
authors.lastname: Elofsson
mesh_terms: D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D008958:Models, Molecular; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry; D012984:Software
chemical_list: D011506:Proteins