title: Large-scale model quality assessment for improving protein tertiary structure prediction.
abstract: BACKGROUND
  Sampling structural models and ranking them are the two major challenges of protein structure prediction. Traditional protein structure prediction methods generally use one or a few quality assessment (QA) methods to select the best-predicted models, which cannot consistently select relatively better models and rank a large number of models well.
  
  
  RESULTS
  Here, we develop a novel large-scale model QA method in conjunction with model clustering to rank and select protein structural models. It unprecedentedly applied 14 model QA methods to generate consensus model rankings, followed by model refinement based on model combination (i.e. averaging). Our experiment demonstrates that the large-scale model QA approach is more consistent and robust in selecting models of better quality than any individual QA method. Our method was blindly tested during the 11th Critical Assessment of Techniques for Protein Structure Prediction (CASP11) as MULTICOM group. It was officially ranked third out of all 143 human and server predictors according to the total scores of the first models predicted for 78 CASP11 protein domains and second according to the total scores of the best of the five models predicted for these domains. MULTICOM's outstanding performance in the extremely competitive 2014 CASP11 experiment proves that our large-scale QA approach together with model clustering is a promising solution to one of the two major problems in protein structure modeling.
  
  
  METHODS
  The web server is available at: http://sysbio.rnet.missouri.edu/multicom_cluster/human/.
authors.affiliation: Computer Science Department, University of Missouri, Columbia, Missouri, 65211, USA, Informatics Institute, University of Missouri, Columbia, Missouri, 65211, USA and C. Bond Life Science Center, University of Missouri, Columbia, Missouri, 65211, USA.
authors.forename: Renzhi
authors.initials: R
authors.lastname: Cao
authors.affiliation: Computer Science Department, University of Missouri, Columbia, Missouri, 65211, USA, Informatics Institute, University of Missouri, Columbia, Missouri, 65211, USA and C. Bond Life Science Center, University of Missouri, Columbia, Missouri, 65211, USA.
authors.forename: Debswapna
authors.initials: D
authors.lastname: Bhattacharya
authors.affiliation: Computer Science Department, University of Missouri, Columbia, Missouri, 65211, USA, Informatics Institute, University of Missouri, Columbia, Missouri, 65211, USA and C. Bond Life Science Center, University of Missouri, Columbia, Missouri, 65211, USA.
authors.forename: Badri
authors.initials: B
authors.lastname: Adhikari
authors.affiliation: Computer Science Department, University of Missouri, Columbia, Missouri, 65211, USA, Informatics Institute, University of Missouri, Columbia, Missouri, 65211, USA and C. Bond Life Science Center, University of Missouri, Columbia, Missouri, 65211, USA.
authors.forename: Jilong
authors.initials: J
authors.lastname: Li
authors.affiliation: Computer Science Department, University of Missouri, Columbia, Missouri, 65211, USA, Informatics Institute, University of Missouri, Columbia, Missouri, 65211, USA and C. Bond Life Science Center, University of Missouri, Columbia, Missouri, 65211, USA Computer Science Department, University of Missouri, Columbia, Missouri, 65211, USA, Informatics Institute, University of Missouri, Columbia, Missouri, 65211, USA and C. Bond Life Science Center, University of Missouri, Columbia, Missouri, 65211, USA Computer Science Department, University of Missouri, Columbia, Missouri, 65211, USA, Informatics Institute, University of Missouri, Columbia, Missouri, 65211, USA and C. Bond Life Science Center, University of Missouri, Columbia, Missouri, 65211, USA.
authors.forename: Jianlin
authors.initials: J
authors.lastname: Cheng
mesh_terms: D016000:Cluster Analysis; D006801:Humans; D008958:Models, Molecular; D017434:Protein Structure, Tertiary; D012984:Software