title: NeBcon: protein contact map prediction using neural network training coupled with naïve Bayes classifiers.
abstract: BACKGROUND
  Recent CASP experiments have witnessed exciting progress on folding large-size non-humongous proteins with the assistance of co-evolution based contact predictions. The success is however anecdotal due to the requirement of the contact prediction methods for the high volume of sequence homologs that are not available to most of the non-humongous protein targets. Development of efficient methods that can generate balanced and reliable contact maps for different type of protein targets is essential to enhance the success rate of the ab initio protein structure prediction.
  
  
  RESULTS
  We developed a new pipeline, NeBcon, which uses the naïve Bayes classifier (NBC) theorem to combine eight state of the art contact methods that are built from co-evolution and machine learning approaches. The posterior probabilities of the NBC model are then trained with intrinsic structural features through neural network learning for the final contact map prediction. NeBcon was tested on 98 non-redundant proteins, which improves the accuracy of the best co-evolution based meta-server predictor by 22%; the magnitude of the improvement increases to 45% for the hard targets that lack sequence and structural homologs in the databases. Detailed data analysis showed that the major contribution to the improvement is due to the optimized NBC combination of the complementary information from both co-evolution and machine learning predictions. The neural network training also helps to improve the coupling of the NBC posterior probability and the intrinsic structural features, which were found particularly important for the proteins that do not have sufficient number of homologous sequences to derive reliable co-evolution profiles.
  
  
  METHODS
  On-line server and standalone package of the program are available at http://zhanglab.ccmb.med.umich.edu/NeBcon/ .
  
  
  BACKGROUND
  zhng@umich.edu.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing 100190, China.|School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.|Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.
authors.forename: Baoji
authors.initials: B
authors.lastname: He
authors.affiliation: Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.
authors.forename: S M
authors.initials: SM
authors.lastname: Mortuza
authors.affiliation: Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing 100190, China.|School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.
authors.forename: Yanting
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.|Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200240, China.
authors.forename: Hong-Bin
authors.initials: HB
authors.lastname: Shen
authors.affiliation: Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.|Department of Biological Chemistry, University of Michigan, Ann Arbor, MI 48109, USA.
authors.forename: Yang
authors.initials: Y
authors.lastname: Zhang
mesh_terms: D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D000069550:Machine Learning; D008958:Models, Molecular; D016571:Neural Networks, Computer; D011487:Protein Conformation; D017510:Protein Folding