title: QAcon: single model quality assessment using protein structural and contact information with machine learning techniques.
abstract: Protein model quality assessment (QA) plays a very important role in protein structure prediction. It can be divided into two groups of methods: single model and consensus QA method. The consensus QA methods may fail when there is a large portion of low quality models in the model pool.
  
  
  
  In this paper, we develop a novel single-model quality assessment method QAcon utilizing structural features, physicochemical properties, and residue contact predictions. We apply residue-residue contact information predicted by two protein contact prediction methods PSICOV and DNcon to generate a new score as feature for quality assessment. This novel feature and other 11 features are used as input to train a two-layer neural network on CASP9 datasets to predict the quality of a single protein model. We blindly benchmarked our method QAcon on CASP11 dataset as the MULTICOM-CLUSTER server. Based on the evaluation, our method is ranked as one of the top single model QA methods. The good performance of the features based on contact prediction illustrates the value of using contact information in protein quality assessment.
  
  
  
  The web server and the source code of QAcon are freely available at: http://cactus.rnet.missouri.edu/QAcon.
  
  
  
  chengji@missouri.edu.
  
  
  
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Computer Science, Pacific Lutheran University, WA 98447, USA.
authors.forename: Renzhi
authors.initials: R
authors.lastname: Cao
authors.affiliation: Department of Computer Science, University of Missouri, Columbia, MO 65211, USA.
authors.forename: Badri
authors.initials: B
authors.lastname: Adhikari
authors.affiliation: Department of Electrical Engineering and Computer Science, Wichita State University, Wichita, KS 67260-0083, USA.
authors.forename: Debswapna
authors.initials: D
authors.lastname: Bhattacharya
authors.affiliation: Department of Electrical and Computer Engineering.
authors.forename: Miao
authors.initials: M
authors.lastname: Sun
authors.affiliation: Department of Computer Science, University of Missouri, Columbia, MO 65211, USA.
authors.forename: Jie
authors.initials: J
authors.lastname: Hou
authors.affiliation: Department of Computer Science, University of Missouri, Columbia, MO 65211, USA.|Informatics Institute, University of Missouri, Columbia, MO 65211, USA.
authors.forename: Jianlin
authors.initials: J
authors.lastname: Cheng
mesh_terms: D000818:Animals; D006801:Humans; D000069550:Machine Learning; D008958:Models, Molecular; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism; D011786:Quality Control
chemical_list: D011506:Proteins