title: APOLLO: a quality assessment service for single and multiple protein models.
abstract: CONCLUSIONS
  We built a web server named APOLLO, which can evaluate the absolute global and local qualities of a single protein model using machine learning methods or the global and local qualities of a pool of models using a pair-wise comparison approach. Based on our evaluations on 107 CASP9 (Critical Assessment of Techniques for Protein Structure Prediction) targets, the predicted quality scores generated from our machine learning and pair-wise methods have an average per-target correlation of 0.671 and 0.917, respectively, with the true model quality scores. Based on our test on 92 CASP9 targets, our predicted absolute local qualities have an average difference of 2.60 Å with the actual distances to native structure.
  
  
  BACKGROUND
  http://sysbio.rnet.missouri.edu/apollo/. Single and pair-wise global quality assessment software is also available at the site.
authors.affiliation: Department of Computer Science, University of Missouri, Columbia, MO 65211, USA.
authors.forename: Zheng
authors.initials: Z
authors.lastname: Wang
authors.forename: Jesse
authors.initials: J
authors.lastname: Eickholt
authors.forename: Jianlin
authors.initials: J
authors.lastname: Cheng
mesh_terms: D001185:Artificial Intelligence; D008958:Models, Molecular; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry; D011786:Quality Control; D012984:Software
chemical_list: D011506:Proteins